ABSTRACT. Abstract. Large language models (LLMs) are increasingly used in education to support writing, problem solving, feedback, and personalized learning. Yet much of the current discussion frames their value in terms of efficiency, responsiveness, and task performance. This paper argues that such a framing is insufficient for educational analysis because it overlooks how learner-LLM interaction restructures who is doing the thinking, judging, and monitoring during learning. To address this gap, the paper proposes a human-centered framework for analyzing and designing learner-LLM interaction through three dimensions: cognitive responsibility distribution, metacognitive engagement demand, and productive resistance. The framework takes subjectification as its normative orientation and treats learner agency as a central educational concern rather than a byproduct of system effectiveness. It is then applied to contrasting learner-facing systems to show that the key difference is not simply how much help AI provides, but how that help is organized in interaction. By shifting attention from assistance alone to the conditions under which learners remain active participants in meaning making, the paper offers a conceptual contribution for future research and a design-oriented lens for more human-centered uses of LLMs in education.

ABSTRACT. Design thinking is essential for students in modern society, and integrated STEM education serves as a vital approach to cultivating this capacity. However, implementing STEM education poses significant challenges for teachers due to time constraints and a lack of multidisciplinary knowledge. While GenAI-driven Multi-Agent Systems (MASs) offer personalized solutions, their direct impact on students’ design thinking remains unclear. Furthermore, educators worry about students' overreliance on GenAI. Engaging students in complex, authentic problem-solving might mitigate this since GenAI lacks a physical understanding of the real world. However, its effectiveness in fostering critical knowledge is underexplored. To address these gaps, this study developed a personalized MAS to scaffold 38 secondary school students’ design thinking process during a semester-long STEM project. Pre- and post-test questionnaires were collected and paired-sample t-tests revealed significant improvements in both design thinking and GenAI critical knowledge. The findings indicate that a pedagogically structured MAS can effectively scaffold students’ complex engineering design processes. Crucially, by confronting GenAI's physical limitations during authentic tasks alongside teacher guidance, students developed epistemological vigilance rather than passive dependency, offering a scalable model for critical human-AI collaboration.

ABSTRACT. Explanations of artificial intelligence features in AI in Education (AIED) systems potentially can deepen learning and engagement. Explanations may clarify an AI’s decision-making process or help users deepen their understanding. However, explanations may also increase cognitive load or confuse students. Given this, it is important to evaluate the effectiveness of explanations and their impact on students. Previous research on explanations in Active Video Watching (AVW) showed that they have a positive impact on students’ learning of empathy skills. We enhanced AVW-Space, an AVW platform, by adding explanations of how the quality of comments students write on videos is determined. We present a study examining the impact of explanations in AVW on presentation skills. We compared data collected from a study performed in two consecutive years in the same first-year introductory engineering course at the University of Canterbury. In 2024, students only received a quality score for their comments. Meanwhile, in 2025, in addition to the quality indicator, students could also obtain the explanation of how the quality was determined. The results show an increase in the number of comments and their overall quality, shifting to more high-quality comments. This also translated to constructive behavior among those who accessed explanations. The results of causal modeling reveal the impact of explanations on the conceptual knowledge scores of students. This study verifies the impact of explanations in AVW and its applicability in different contexts. This study contributes to the widening research on explainability in VBL and AIED.

ABSTRACT. In inquiry-based learning, it is crucial for learners to examine inquiry topics　divergently from multiple perspectives, formulate consistent and logically coherent exploratory hypotheses aligned with their objectives, and use these hypotheses to guide subsequent exploration. However, when learners’ focus on exploratory keywords becomes narrow, the range of hypotheses available for critical examination is correspondingly limited. To address this issue, we developed a support system equipped with two functions: one that presents candidate keywords to help learners expand and organize their keyword maps, and another that provides advisory prompts based on the SCAMPER method, a well-known idea-generation technique, to encourage the revision and structuring of exploratory hypotheses. An evaluation experiment involving 12 students showed that the numbers of both keywords and hypotheses increased after the introduction of these support functions, indicating the potential of the system to facilitate the formulation of multifaceted exploratory hypotheses. Furthermore, questionnaire results indicated that participants generally perceived the support for organizing keywords and formulating hypotheses as useful.

ABSTRACT. On-demand video learning has become increasingly popular due to its flexibility, accessibility, and support for self-paced learning. In these on-demand video- based learning environments, students often struggle to maintain their attention due to the lack of real-time learning support. To address this issue, this paper proposes a robot-mediated learning support system that integrates real-time state estimation and adaptive intervention. The system continuously monitors learners’ attention states using multimodal signals, including gaze tracking and head-pose estimation. Based on these signals, the system identifies learners’ cognitive states. When distraction is detected, a physical social robot dynamically provides prompts to help learners refocus their attention. A quasi-experiment was conducted by recruiting 20 participants. The result showed that the robot-assisted group approach effectively improved participants’ learning achievement. Temporal analysis based on the Eye Aspect Ratio (EAR) further demonstrated greater stability of attention, and attentional recovery also improved following the intervention. Questionnaire results indicated improved concentration (M = 3.69 vs. 3.25), although cognitive load slightly increased (M = 3.06 vs. 2.87). These findings suggest that real-time, context-aware robot intervention effectively supports sustained attention while highlighting a trade-off between attentional support and cognitive burden.

ABSTRACT. Teacher Professional Vision (TPV), the ability to notice instructionally relevant classroom events and reason about their implications for teaching and learning, is an important component of teacher expertise. Existing approaches for assessing TPV often rely on laboratory-based eye-tracking equipment, controlled environments, or resource-intensive assessment procedures, limiting their scalability in teacher education contexts. This paper presents the design and evaluation of a web-based TPV assessment system that combines webcam-based gaze tracking with stimulated recall. Using Webgazer.js, the system captures participants’ gaze behavior while viewing a classroom image on their own devices and generates personalized gaze heatmaps that are later used as recall cues to elicit reflective reasoning. 21 pre-service and in-service teachers completed a three-phase assessment protocol consisting of timed viewing, self-paced viewing, and heatmap-mediated stimulated recall. Gaze data were analyzed across predefined classroom Areas of Interest, while verbal responses were coded using a TPV reasoning rubric encompassing Description, Explanation, and Prediction dimensions. Results indicate that the protocol can be deployed outside laboratory settings and produces verbal data that can be reliably coded. Gaze patterns showed directional trends consistent with prior TPV research, while stimulated recall elicited richer action-oriented reasoning than unprimed observation alone. The findings demonstrate the feasibility of integrating low-cost webcam eye-tracking and personalized visual feedback into TPV assessment, offering a scalable approach for studying teacher noticing and reasoning in authentic settings.

ABSTRACT. Peer assessment has been widely recognized as an effective pedagogical approach in higher education, fostering critical thinking and evaluative competencies. Central to its effectiveness is the quality of feedback that learners provide and receive. However, feedback engagement is shaped by learners' individual psychological and cognitive characteristics. Among these, self-efficacy and cognitive load have been theorized to influence how learners interact with, process, and act upon feedback. Despite growing interest in feedback literacy, the mechanisms through which these factors operate within interactive peer assessment contexts remain insufficiently understood. To address this gap, the present study examined the effects of self-efficacy and cognitive load on feedback quality and feedback literacy in a dual-cycle peer assessment context. Twenty-four undergraduate students participated in three rounds of peer assessment, generating 198 assessment records alongside pre- and post-course questionnaires. Epistemic Network Analysis (ENA) was employed to examine cognitive network structures underlying feedback quality, while one-way ANOVA and LSD post hoc comparisons were used to assess group differences in feedback literacy dimensions. The findings revealed that learners with higher self-efficacy significantly outperformed their peers in Eliciting, Processing, and Enacting, and demonstrated more complex cognitive networks shifting from personal opinion toward capability assessment and supportive construction. Regarding cognitive load, higher levels were significantly associated with lower scores in Processing and Readiness to Engage, while a moderate level of cognitive load fostered the most balanced and fully developed connections among cognitive elements in feedback interactions. These findings offer practical reference value for the design of peer assessment activities and the cultivation of feedback literacy in higher education settings.

ABSTRACT. This study examined the psychological and structural factors associated with the implementation of inquiry-based learning (IBL) among 650 in-service teachers in Japan. An exploratory factor analysis of items related to the school environment identified five dimensions: “School Infrastructure,” “Implementation Barriers,” “Educational Value,” “Support from External Stakeholders,” and “Leadership Support.” Hierarchical regression analysis revealed that school infrastructure, teacher self-efficacy for inquiry, and professional learning orientation were significant predictors of IBL practice, even when psychological and structural variables were simultaneously considered. In contrast, positive attitudes toward IBL were not significant when structural and capability-related variables were controlled. These findings suggest that sustainable IBL implementation depends not only on teachers’ personal beliefs, but also on the availability of adequate organizational infrastructure and opportunities for continuous professional development. This is particularly relevant in the context of technology-enhanced learning environments, where ICT infrastructure forms a central component of school readiness for inquiry-based practice.

ABSTRACT. We introduce a web-based simulation for teacher professional development that makes LLM training processes transparent through explicit controls and implicit preference‑based shaping. Designed with teachers’ formative feedback practice as the context, our simulation positions teachers as AI co‑trainers. A qualitative pilot with three experienced in‑service teachers showed building up of clearer, more process‑focused intuitions about how LLMs learn. Interaction traces also revealed teachers' increased noticing of how tuning and critiques shape outputs.

ABSTRACT. Despite the availability of sophisticated inquiry-based labs, literature reports persistent issues such as low learner engagement. This often stems from the daunting nature of complex problem scenarios, intangible scientific concepts, and cognitively demanding tasks inherent in inquiry-based learning environments. As a result, learners may feel overwhelmed and hesitate to explore underlying phenomena, struggle to complete the required cognitive activities, underutilize the affordances of associated simulations, or even drop out prematurely. These issues underscore the need for design interventions that can support and sustain cognitive engagement during inquiry-based learning. This paper presents the design of GaMINLab (Gamification (Meaningful) in INquiry Lab), that employs content gamification approach to cognitively engage learners in science inquiry practices in simulations-based labs. Unlike structural gamification, Content gamification integrates game-like elements directly into the learning content to enhance cognitive involvement. In GaMINLab, learners interact with problem scenarios built around OLabs simulations by engaging in core inquiry practices such as planning and carrying out investigations, analyzing and interpreting data, engaging in argument from evidence, etc. Relevant content gamification elements such as problems based on storyline, role based avatars, progression indicators, task-specific badges, etc and supporting pedagogical elements are incorporated in and around these inquiry tasks. We report student perceptions of these elements across 4 iterative studies where they engaged with inquiry tasks in GaMINLab. Qualitative data from student feedback and audio transcripts were analyzed using a deductive thematic analysis. We investigated impact perceived by student on affective domain overall, science process skills related, Scaffolding/support. Findings reveal that avatars and progression indicators’ novelty effect diminishes across multiple problem-solving cycles, while for elements like story-based problems, task specific badges the effect persists. The findings also highlight the role played by each element in isolation and together in the overall gamification ecosystem during the problem-solving process.

ABSTRACT. Sustaining attention is a critical challenge in multimedia-rich digital learning environments, where learners are frequently exposed to multiple sources of information. While gamification and immersive technologies have been widely used to enhance engagement, limited research has examined how gaze interaction can be integrated as an active mechanism for attention training. This study proposes a gamified attention training system implemented in an immersive virtual reality (VR) environment with gaze-based interaction. The system incorporates goal-oriented visual search tasks, immediate feedback, and progressive challenges, while transforming gaze behavior into a core interaction modality that directly drives task performance. A quasi-experimental design was employed to compare training effects between VR and computer-based (PC) environments, as well as across learners with different attention tendencies. The results indicate that learners demonstrated significant improvement in visual search performance, particularly in task completion time, after training. The VR condition showed stronger improvement compared to the PC condition, suggesting that immersive gamified environments can enhance attentional engagement. In addition, learners with attention-deficit tendencies exhibited more pronounced improvement in the VR condition. This study contributes to the field of educational gamification and game-based learning by demonstrating how gaze interaction can be integrated as a gameplay mechanic within immersive environments, supporting both attention training and attention-aware learning design.

ABSTRACT. Gamification in education is increasingly prevalent. However, its application to complex and abstract learning domains remains less explored. One such domain would be the learning of Data Structures and Algorithms (DSA). DSA presents a challenge as learners are required to strengthen the relationship between four different parts: abstract concepts, visual representations, algorithmic logic and code implementation for any DSA concept to be considered completely learnt. Many tools that already exist support learning only part of the learning process, while the learners need help in going through the full process, from concept to code. A key missing link is the connection between conceptual understanding, algorithm logic, and code implementation. This paper aims to solve this problem by connecting the dots, with a stronger linkage between concepts, visualisation and code. To best represent each concept and its intricacies with games, this project explores the different game genres: puzzle, board, card and action games, can represent different DSA topics. This will enable players to have an improved learning experience with better conceptual understanding of the DSA concepts as they engage and interact more with the games as compared to traditional learning methods of DSA.

ABSTRACT. Educational technology platforms that personalize instruction routinely test proposed changes to their decision rules by running those rules against simulated student data before applying any change to real students. As generative and agentic AI enter learning ecologies, large-language-model-based learner simulators are increasingly proposed for this offline testing. A learner simulator takes a student's recorded interaction history and generates a synthetic sequence of future actions that stands in for the student's actual future behavior. Current evaluations focus on whether simulated sequences look realistic in timing patterns, action types, and response rates. This focus leaves a consequential gap: a simulator can produce realistic-looking sequences while still misidentifying which students need support, which follow-up best serves a student who answered incorrectly, or which students are ready to advance. This paper introduces decision-centered validity, a reusable deployment-readiness framework that tests whether replacing actual student data with simulated data changes the decisions a platform would make about individual students. The audit evaluates four decisions platforms commonly make: selecting which students receive limited instructional support, assigning follow-up activities after incorrect responses, identifying students who have gone inactive, and judging which students are ready to advance. Results are organized through explicit pass-or-fail tests and three readiness tiers indicating whether a simulator is appropriate for a given decision type. We apply the audit to three publicly available datasets: the Open University Learning Analytics Dataset (OULAD), EdNet KT3, and ASSISTments. Together they cover 35,873 students and 13.57 million recorded interactions across nine benchmarked simulator families. No simulator passes all applicable decision tests, and every family fails the support-allocation ranking test; this verdict holds for frontier and open-weight generative large language models and under an independent multi-model auditor panel. Because support allocation governs which active learners receive scarce resources, a simulator that cannot preserve this ranking raises an equity-of-access concern: a platform may deploy rules that fail to reach the students who need help most.

ABSTRACT. This study defines an individually optimal problem as one in which a learner’s perceived confidence and challenge are balanced and preliminarily evaluates a method for recommending such problems. This approach is grounded in flow theory, which suggests that learners become most engaged when challenge and skill are well balanced. Thus, recommending individually optimal problems not only requires considering a problem’s objective difficulty, but also learners’ subjective perceptions of difficulty. To address this issue, we propose a recommendation method based on Large Language Models (LLMs) that constructs learner profiles from a small set of diagnostic problems and uses these profiles to predict learners’ confidence and perceived challenge for unseen problems. Based on flow theory, we define Gap as Confidence − Challenge and recommend problems with a small |Gap|. A preliminary evaluation was conducted using problems targeting a single knowledge component in linear algebra, specifically elementary row operations and the degrees of freedom of solutions. The results indicate that the LLM can predict learner confidence with moderate accuracy and that the proposed prompt incorporating learner profiles improves the prediction accuracy of both Confidence and Gap compared with a baseline prompt that does not use learner profiles. In contrast, challenge prediction accuracy remained limited because perceived challenge strongly depended on problem-specific characteristics and because a mismatch existed between the procedural load assumed in the prompt and the subjective ratings provided by participants, who did not actually solve the unseen problems.

ABSTRACT. This study developed and validated a Natural Language Processing (NLP) framework for the automated assessment of students' cognitive engagement in offline elementary science classes. A coding scheme was first constructed by integrating the ICAP framework with Bloom’s Taxonomy and refined through the Delphi expert method. Subsequently, speech data from 22 classroom videos (totaling 850 minutes) was transcribed and segmented. A fine-tuned Llama-2 large language model was then employed to automatically classify the 5,241 derived text segments according to the coding scheme.

Validation against human coding demonstrated satisfactory reliability, with an accuracy of 80.89% and a high intraclass correlation coefficient (ICC) of 0.85. The analysis of the automatically coded data yielded three main findings: (1) "Active" engagement was most frequent (41.02%), while higher-order "Constructive" engagement was relatively low (10.59%); (2) Lag sequential analysis revealed significant cognitive-state transitions, such as "Receiving → Discussing" and "Elaborating → Questioning"; (3) A clear developmental trajectory was observed, with engagement shifting from passive reception in lower grades towards more active application and social discussion in higher grades.

The results confirm the feasibility and reliability of using advanced NLP for automated, fine-grained analysis of classroom dialogue. This work provides a practical tool and empirical insights for evidence-based classroom evaluation and the enhancement of instructional practices in science education.

ABSTRACT. LLM tutors are usually optimized for correctness, but how correct knowledge is disclosed may also shape learning. We decompose Academically Productive Talk (APT) into three prompt-conditioned dimensions: Learning Community (LC), Knowledge Precision (KP), and Rigorous Thinking (RT). We implement these dimensions in an LLM tutor for Signal Detection Theory. In a pilot component-ablation study (N = 42), university students learned SDT and then interacted with one of four tutoring profiles. Low-LC produced lower immediate memory and higher perceived workload, while Low-RT produced lower memory with little short-term difference in transfer. Low-KP preserved factual accuracy while reducing immediate use of canonical formulations. It produced higher perceived workload; motivation and memory moved in opposite directions, although neither contrast reached conventional significance. A post-hoc audit using 18 fixed Chinese learner probes documented distinct outputs across prompt configurations. Together, these findings motivate treating pedagogical dialogue as a set of adjustable conversational levers.

ABSTRACT. Artificial intelligence is rapidly expanding into physical environments through Vision-Language-Action (VLA) systems. However, current primary robotics education remains largely rule-based, while existing AI education often emphasizes AI use rather than understanding AI reasoning. To address this gap, we developed PIE BRIDGE, a VLA-inspired educational web application for primary students. PIE BRIDGE serves as a pedagogical bridge between physical computing and physical AI education by simulating an inspectable VLA workflow through visible stages of Vision, Language, and Action. The system supports human–AI co-piloting by allowing students to review and revise AI decisions throughout the process. To accommodate classroom technical constraints and current vision model limitations, the system utilizes a discrete workflow rather than a continuous loop and restricts robot movements to a grid-board environment. A two-round modified Delphi study with ten experts yielded consensus across all domains, validating PIE BRIDGE as a pedagogically robust VLA-inspired learning environment for primary school students.

ABSTRACT. As artificial intelligence becomes increasingly integrated into research practice, research-active individuals increasingly rely on AI tools in activities such as research question generation, data analysis, and academic writing. However, validated instruments for assessing researchers' AI-related competencies remain scarce. This study introduces the concept of AI Research Literacy and develops a quantitative measurement scale. Drawing on AI literacy theory and the research activity process framework, we propose a five-dimensional model encompassing understanding of AI applicability in research, effective use, human–AI collaboration practices, evaluation of AI-generated results, and ethical awareness. Item generation was based on literature review and theoretical deduction, followed by expert evaluation and pilot testing, resulting in a 24-item scale. Exploratory and confirmatory factor analyses were conducted with a sample of 810 research-active individuals. The results support a five-factor structure with good model fit. The scale demonstrates satisfactory reliability as well as convergent and discriminant validity, and it performs significantly better than a single-factor model. The instrument provides a reliable tool for assessing AI-related competencies in research settings and offers empirical support for research training and governance practices in the context of AI integration.

ABSTRACT. Assessments in practical education vary across teachers and are difficult to encode as explicit rules. This paper presents an inference-time Retrieval-Augmented Generation (RAG) framework for adapting a Vision-Language Model (VLM) to a specific teacher without fine-tuning. The framework retrieves similar past assessment cases from the teacher’s assessment history and provides them as contextual examples during　inference. We construct a dataset of teacher-annotated egocentric farming videos. The proposed framework is evaluated on task category classification and Good/Bad quality assessment. Experimental results demonstrate that presenting assessment cases improves both task category classification and task quality assessment. These findings suggest that RAG is an effective approach for reflecting teacher-specific judgment in video-based skill assessment.

ABSTRACT. School teachers are expected to utilise data in instructional decision-making, but many teachers report facing limited preparation. This study evaluated a professional development (PD) programme on teacher data literacy in Singapore using an explanatory sequential mixed-methods design. Quantitative data was collected from N = 175 teachers, with matched pre-post data obtained from n = 29 participants across three PD cohorts. Semi-structured interviews with n = 10 teachers were subsequently conducted to contextualise quantitative findings. Quantitative analyses revealed statistically significant improvements in teacher self-efficacy across all three cohorts, alongside significant gains in data knowledge for two cohorts. Changes in perceived value of data use were more variable, with only one cohort demonstrating significant improvement. Qualitative findings indicated that hands-on analysis of authentic classroom datasets and collaborative discussion supported confidence development and engagement with data use. However, teachers continued to report difficulties translating analytical findings into concrete instructional strategies. Participants also identified structural barriers including limited time, competing responsibilities, and insufficient mentoring support. Findings suggest that PD may support proximal knowledge development more readily than deeper pedagogical or dispositional shifts.

ABSTRACT. Automated assessment in computing courses often focuses on final answers, selected options, short text responses, or conventional program output. However, many computing tasks require students to construct structured computational artefacts whose correctness depends on internal representation, local well-formedness, and domain-specific behaviour. This paper proposes an assessment design pattern for such tasks, developed and illustrated in the context of a third-year artificial intelligence course. In the proposed pattern, code is used as the submission medium, but the assessed object is the artefact produced by that code. The marking code can then inspect the artefact structurally, check local validity conditions, and evaluate its behaviour according to the semantics of the relevant topic. The pattern is illustrated through three AI cases: belief-network modelling, constraint-satisfaction problems, and alpha-beta pruning in games. These cases show how automated tests can distinguish between different failure modes, including representational errors, modelling errors, parameterisation errors, and errors in reasoning traces. The approach was implemented in Python and deployed through Moodle and CodeRunner using server-side execution. Deployment across formative and summative assessments suggests that the pattern is practical within existing assessment infrastructure and can support more diagnostic feedback than final-answer grading alone.

ABSTRACT. Reflection is essential for improving one’s skills in competitive games. However, for beginners, the perceived difference in skill between themselves and their opponents leads to a bias in their attention and in how they interpret their opponents' feedback, preventing them from engaging in effective reflection. Therefore, the opponent's skill level should be selected based on the learning stage and objectives. In this paper, we assign the roles of ‘professional’, ‘amateur’, and ‘beginner’ to a social robot as an opponent, and intentionally elicit the focus of reflection and the attitude towards receiving feedback from learners, thereby supporting their reflection effectively. The results of a case study show that learners playing Shogi matches to exchange opinions with the role-assigned robot can perceive the difference in skill as intended and take the intended attention and attitude towards receiving feedback from the robot.

ABSTRACT. Strategic competence, which is the ability to convey intended meaning through alternative expressions when appropriate vocabulary is unavailable, is essential for EFL learners but is often insufficiently supported in conventional speaking instruction. To promote three expressive achievement strategies (approximation, circumlocution, and restructuring), we developed HanaStrategy WebApp (HanaS), a browser-based platform implementing a strategy-focused speaking task: learners describe an illustration without using pre-specified "restricted expressions," a constraint that pushes them to search for and deploy the strategies as solutions. Between two attempts per task, HanaS provides AI feedback that visualizes how the learner's speech would be interpreted by a third party (rendered as an illustration), identifies the strategies used, and offers alternative model answers. The visualization is intended to make conveyance gaps salient and to elicit content-oriented reflection on what was insufficient. We conducted a two-month classroom implementation with 18 first-year undergraduates (CEFR B1-B2), comparing word count in the pre/post-speech tests, the number of strategies used in the first and last strategy-focused speaking tasks, and qualitatively coded reflections. Three findings emerged. First, word counts increased significantly from pre- to post-test. Second, circumlocution spread across more learners. Third, learners whose word count improved had named specific strategies less frequently in their improvement plans; content-oriented reflections were associated with greater gains, whereas naming strategies was associated with smaller gains. These findings suggest that AI feedback eliciting content-oriented re-expression, rather than mere strategy labeling, can scaffold strategic competence development.

ABSTRACT. This meta-analysis examined whether artificial intelligence (AI) supports second language (L2) speaking in children aged 3–12 years. Many AI tools, often using machine learning based natural language processing, can simulate conversational partners, deliver immediate feedback, and adapt tasks to learners, which may benefit child L2 pronunciation, fluency, and oral production. We searched nine databases and conducted citation tracing. After double-blind screening of 1,339 records, 11 peer-reviewed studies met the inclusion criteria. Random-effects meta-analysis with robust variance estimation showed a significant positive overall effect (effect size = 1.16, standard error = 0.36, t(18) = 3.20, p = .005), indicating that AI–based interventions improved child L2 speaking, with particularly significant effects on productive vocabulary and content. Moderator analyses suggested that simpler feedback and conversational formats were associated with larger gains. Overall, the findings support the potential of AI to address practical gaps in early L2 speaking instruction, including providing individualized and timely feedback that may be difficult to achieve in typical classrooms or in settings with limited opportunities for input and output. At the same time, conclusions are tempered by the small evidence base and variability in intervention designs and outcome measures, underscoring the need for more rigorous and well-powered empirical studies.

ABSTRACT. Although generative artificial intelligence (AI) is increasingly used in collaborative learning, existing research has mainly examined text-based systems or AI as a scaffold, leaving limited understanding of how speech-based AI shapes real-time group discourse. This study examines sequential discourse dynamics in triadic collaborative discussions in which two students interact with a speech-based AI positioned as a third group member. Grounded in hybrid intelligence and human–AI shared regulation perspectives, the study conceptualizes AI participation as part of a shared interactional system in which student and AI discourse moves may mutually shape one another. Data were collected from 66 online collaborative discussions involving 132 undergraduate students. Sessions were video- and audio-recorded, transcribed, segmented into turns, and coded for communicative function. Descriptive statistics were used to examine discourse distributions, and lag sequential analysis was conducted to identify significant transition patterns between student and AI moves. Results showed a broadly balanced but asymmetric interactional structure. Student questions strongly elicited AI answers, while AI questions opened space for student answers, further questions, and information-sharing. AI answers and information frequently led to subsequent AI questions, suggesting a provision–elicitation cycle. Socio-emotional exchanges formed a reciprocal discourse channel, and student information-sharing supported continued knowledge-building. These findings suggest that speech-based AI functioned not only as an information provider but also as a conversational participant that shaped the temporal organization of collaborative discourse. The study informs the design of AI agents that balance provision, elicitation, restraint, and socio-emotional responsiveness while preserving student agency.

ABSTRACT. This study examines how slight upward social comparison can be designed for K-12 student-facing dashboards without relying on grades or final outcomes. Although upward comparison has been used in learning analytics dashboards to promote engagement, score-based comparison may intensify competition and obscure students’ actual learning needs. To address this issue, the study focuses on knowledge states estimated through an Open Knowledge and Learner Model (OKLM), which links learning records and test results to knowledge components in a textbook-based knowledge map. Data were collected from 120 first-year junior high school students learning mathematics. Using concept-level mastery values in OKLM, time-series clustering was conducted to identify patterns of change in students’ knowledge states. Three transition patterns were identified: students struggling with applications, students with consistently high understanding, and students struggling with basics. A one-way ANOVA using final test scores showed significant differences among the clusters, but post-hoc comparisons indicated no significant difference between the two struggling groups. These results suggest that students with similar final scores may have different underlying knowledge-state trajectories. Based on these findings, the study proposes knowledge-state-based peer selection as a way to support attainable upward comparison, enabling learners to refer to peers who share similar difficulties but show slightly higher mastery.

ABSTRACT. This study investigates how university students’ perceptions of generative AI relate to their engagement in collaborative learning and to subsequent changes in those perceptions. The study was conducted in a first-year data science class at a Japanese university, involving 554 first-year students from the faculties of Medicine, Dentistry, Pharmaceutical Sciences, and Law. Students participated in a group-based AI literacy activity using generative AI tools, with discussions conducted through a chat-based system. Participation was measured using log data, including the number of posts and total character counts. The analysis consisted of pre–post comparisons using Wilcoxon signed-rank tests and regression models examining relationships between perceptions and participation. The results showed significant increases in institutional expectations toward AI, perceived choice in learning about AI, and interest in AI learning. Exploratory regression analyses indicated that students’ initial AI ethics-related perceptions were positively associated with posting frequency, whereas anxiety-related factors were not significantly related to participation. Furthermore, a higher number of posts was associated with a smaller increase in AI ethics-related perceptions, suggesting that active engagement in critical discussion may foster more nuanced and reflective understandings of AI. These findings suggest that the relationship between perceptions and participation is selective and dynamic, highlighting the value of structured collaborative activities for promoting critical and ethical reflection. The study contributes to learning analytics research by demonstrating how behavioral log data and perception measures can be integrated to better understand AI-supported collaborative learning.

ABSTRACT. Open-ended student comments provide valuable information for quality assurance in higher education. However, transforming fragmented comments into structured reports remains a challenging task. This study investigates the use of large language models to convert student feedback into structured academic quality assurance reports. Specifically, we design and compare five summarization strategies: direct summarization from raw comments, aspect-guided summarization, sentiment-guided summarization, aspect–sentiment-guided summarization, and a hybrid strategy that combines raw comments with structured comment groupings. Experimental results on a Vietnamese student feedback dataset show that direct summarization is a strong baseline and achieves the highest performance on automatic evaluation metrics. Meanwhile, the hybrid strategy obtains comparable results while providing a clearer structure for quality assurance interpretation. These findings suggest that preserving raw comments is important for maintaining the natural context of the input data, while aspect and sentiment information can serve as useful supporting signals for organizing and interpreting generated reports. This study provides an empirical analysis of large language model-based summarization strategies in the context of student feedback analysis and academic quality assurance.

ABSTRACT. Artificial intelligence literacy is increasingly essential for primary students, yet age-appropriate pedagogical approaches remain under explored. This mixed-methods study examined whether gamified storybook reading enhances AI literacy compared with storybook reading alone among 87 primary students in Hong Kong. Participants were randomly assigned to experimental (gamified) or control (non-gamified) groups. Quantitative analysis using ANCOVA and Wilcoxon signed-rank tests revealed that storybook reading alone significantly improved knowledge acquisition, while gamification enhanced attitudes, particularly for students with lower initial motivation. However, gamification appeared to interfere with cognitive learning, possibly due to increased extraneous cognitive load from time pressure and leaderboards. Qualitative interviews indicated that students valued both modalities for building interest. These findings suggest that storybooks and gamification serve complementary pedagogical functions, with implications for designing adaptive AI literacy interventions tailored to learner profiles.

ABSTRACT. Generative AI is reshaping teacher professional development. Vibe coding, an approach to developing interactive digital applications through natural language interaction with generative AI, offers new opportunities for educators without programming experience to create educational tools. Nevertheless, teachers still need to learn how to use vibe coding to develop educational tools that effectively meet their instructional needs. This study investigated pre-service teachers’ learning agency, vibe coding behaviors, and learning outcomes in a vibe coding-based educational tool development course, and compared the vibe coding behaviors and learning outcomes between high- and low-agency groups. The results showed that the high-agency group more frequently engaged in behaviors of testing webpages and dissatisfying with the webpage testing results, whereas the low-agency group more frequently checked the textbook. The high-agency group achieved better learning outcomes in terms of the interactive educational webpage quality scores for accuracy of content and support for learning goals. These findings suggest the relationship between pre-service teachers’ learning agency, their vibe coding behaviors, and learning outcomes. The study provides empirical evidence for teacher education and the integration of generative AI into instructional design, helping instructors provide appropriate support based on pre-service teachers’ different levels of learning agency and enhancing their ability to develop educational tools with vibe coding.

ABSTRACT. The proliferation of generative artificial intelligence (AI) in educational settings has intensified concerns around "cognitive delegation"—the habitual outsourcing of higher-order thinking to algorithmic systems. This tendency is particularly consequential during upper elementary school years, when critical and metacognitive capacities are still consolidating, yet existing AI education remains predominantly focused on tool operation rather than critical evaluation of AI-generated outputs. This study proposes and empirically evaluates the SLAP-FACT Matrix Framework, an instructional model integrating two complementary dimensions: SLAP (Source, Logic, Actuality, Perspective) as a multi-layered verification protocol for AI outputs, and FACT (Finding, Attitude, Context, Target) as a structured inquiry scaffold. The framework was implemented through a 10-session program with 23 sixth-grade students in South Korea, assessed via competency checklists and AI interaction log analyses. Finding demonstrated statistically significant improvements across all domains, with students shifting markedly from passive acceptance of AI outputs toward active, multi-dimensional verification and autonomous inquiry. These results offer a replicable, classroom-ready model for cultivating critical AI literacy at the elementary level, with broader implications for embedding intentional inquiry practices across all stages of formal AI education.

ABSTRACT. The rapid adoption of generative artificial intelligence (GEN-AI) in higher education has opened up new opportunities for supporting academic writing. However, many of the existing AI writing tools primarily focus on text generation and surface-level feedback with very little attention to writing development, learner agency and the broader academic literacy needs of international students who struggle in navigating the academic norms in English-dominant higher education environments. Thus, this study presents the design and formative evaluation of a pedagogically grounded AI writing tutor that intends to support international and multilingual learners throughout the academic writing process. The AI writing tutor was developed using a pedagogy first approach informed by the process-genre approach to writing, scaffolding and feedback literacy. Rather than generating complete answers, the tutor guides learners through the stages of planning, drafting, revising, editing and reflection allowing them to retain authorship. To evaluate the tutor’s pedagogical alignment, two Academic advisers with experience in supporting international students participated in an expert review session individually which consisted of tutor interactions, observation and semi-structured interviews. Data was analyzed using thematic analysis, where the findings revealed key themes and provided positive impression of the AI writing tutor’s alignment to pedagogy and framework selected and provided suggestions for improvement that will be implemented in future iterations. This study contributes to the growing field of AI-supported writing by showcasing how educational theory can inform the design of pedagogically grounded AI writing support and provides early formative support for the pedagogical direction of the tutor.

ABSTRACT. Bilingual instruction opens international collaborative learning to more students, but working in two languages is costly: every contribution must be produced twice, may not reach members who do not speak the language used, and is harder to make in a non-native language. This study asks whether avatar-anchored real-time transcription and translation (ARTT) reduces those costs. Eighteen students in a Japanese–English metaverse course compared sessions with and without ARTT, rating it positively for discussion efficiency (M = 4.11, positive response rate 83.3%), understanding others (M = 4.06, 77.8%), and reduced anxiety when speaking (M = 3.89, 77.8%). Gains on the production items were larger for domestic students. Difficulties concerned readability, start-up, and translation accuracy. ARTT may thus preserve what bilingual provision enables while reducing its costs.

ABSTRACT. This study investigates how prospective English-as-a-foreign-language (EFL) teachers developed self-directed learning (SDL) strategies and self-efficacy while using generative artificial intelligence (GenAI) tools to create digital English storybooks. Twelve prospective EFL teachers enrolled in an 18-week graduate course in Taiwan participated in a GenAI-supported materials design project. Data were collected from semi-structured interviews, prompt logs, instructor observation notes, and feedback documents, and were analyzed through thematic analysis. Findings show that participants developed operational, troubleshooting, and learning-related self-efficacy as they learned to use GenAI tools for text generation, illustration design, audio narration, and storybook production. They also enacted SDL through goal setting, resource management, autonomous tool exploration, and emotional regulation. Instructor guidance, peer support, and iterative feedback transformed technical difficulties into confidence-building experiences. The study suggests that GenAI-assisted digital storybook projects can strengthen AI literacy, professional confidence, and lifelong learning competence in teacher education.

ABSTRACT. Classroom videos are valuable for teacher professional learning, but large collections are often difficult to reuse due to challenges in organization and pedagogical retrieval. This paper presents the design of COREference, an AI-enabled classroom video corpus designed to support teacher inquiry through human–AI collaboration. Developed around a corpus of over 3,100 authentic, multi-subject classroom videos, the system transforms raw recordings into searchable, pedagogically meaningful multimodal resources using a two-layer approach. The first layer automates preprocessing via audio filtering, transcript generation, and alignment with expert-defined pedagogical coding schemes. The second layer provides a teacher-facing interface for multimodal querying and AI-supported interpretation of videos, transcripts, and annotations. This paper focuses on the affordances and benefits of the second layer, with initial exploratory feedback indicating that this interaction layer effectively supports teacher inquiries and reflection by streamlining the navigation and retrieval of authentic classroom interactions. Beyond technical processing, COREference demonstrates how an AI-supported corpus can scale professional learning and research reuse without relying entirely on labor-intensive manual review, contributing a sustainable framework for human–AI collaboration in educational infrastructure.

ABSTRACT. Artificial Intelligence (AI) is rapidly becoming infrastructural in education, yet “AI literacy” is often framed as a single competence, masking substantial variation in how AI is positioned in interaction. This scoping review reorganizes the 2020–2026 Large language models (LLM)-in-education evidence base through a role lens. We argue that the same AI can operate as a knowledge utility, dialogic tutor, reviewer, co-creator, teacher assistant, or reflective coach; each role bringing distinct success criteria, risk profiles, and required human competencies. Using targeted database searches, screening, and role-based coding, we synthesize findings across these interaction roles and contribute: (a) an interaction-role taxonomy for educational context, and (b) distinguishing role general co-existence skills from role specific interaction protocols. The paper contributes the notion of role fluency: the capacity to intentionally recognize, select, sustain, switch, and critically govern AI interaction roles in educational activity. The role-fluency lens offers a design and professional development vocabulary for moving beyond generic AI literacy toward accountable human-AI participation in intelligent learning ecologies.

ABSTRACT. For a generative AI tutor to adapt, the learner state must be updated from interpretable response evidence for the preceding question. This study introduces a response-evidence-gated architecture that separates the minimum cognitive demand of an AI question (Q-DOK) from the reasoning demonstrated in a student response (A-DOK), assigning A-DOK only when such evidence is present, and audits the resulting automatic classifications against human judgment. From logs of 37 sixth-grade students in two Korean elementary schools, we derived 999 question–response pairs; in a stratified sample of 200, response evidence was identified in only 37 cases (18.5%). In a conditional comparison sample of 97, exact agreement was 55.7% for Q-DOK (linearly weighted κ = .431) and 75.3% for A-DOK (κ = .560), with over-assignment dominant in both. Among 17 questions rated Q-DOK 3 or higher, 14 responses (82.4%) were A-DOK 1 or 2, supporting the separation of question demand from observed reasoning.

ABSTRACT. In the era of generative artificial intelligence and core competency-driven educational reforms, redesigning assessment poses a critical challenge for teacher professional development. While various technological environments offer distinct affordances for instructional design, how different digital platforms shape teachers’ collaborative reflection and cognitive networks regarding alternative assessments remains underexplored. This study used epistemic network analysis to investigate and compare the cognitive network structures of design feedback from two classes of in-service teachers across two distinct technological environments. The ENA results revealed statistically significant differences between the two groups’ cognitive networks. In summary, while generative AI excels at accelerating individualized technical micro-tuning, an asynchronous knowledge-building environment is essential for driving systemic coherence and community-wide alignment around holistic competencies. This has a crucial implication for future teacher professional development: first, leverage collaborative networks to advance high-level pedagogical concepts, and then harness generative AI tools to execute precise, tactical scaffolding.

ABSTRACT. Improving coding practices such as test-first development is an important goal in programming education. Although reflective learning is known to support improvement, learners often struggle to understand their own coding behaviors and translate reflection into actionable changes. This study proposes a visualization-based reflection and planning approach that supports learners in analyzing their coding activities and formulating plans for improvement. The proposed system visualizes learners’ coding processes using three indicators: test creation timing, test-first compliance, and failed-test persistence. An empirical study was conducted in a third-year undergraduate programming course involving 106 students. Learners engaged in repeated cycles of implementation, testing, reflection, and planning across multiple programming exercises. The results show that learners who engaged in visualization-based reflection and planning demonstrated substantial improvement in coding practices, particularly in test-first compliance, which increased by +18.9%, while learners who did not use visualization showed a decrease of -15.0%. Statistical analysis confirmed that this difference was significant (p = 0.003). However, improvements in other indicators were not statistically significant, suggesting that different aspects of coding practices require different types of support. These findings indicate that visualization alone is insufficient to support behavioral change and that the integration of reflection and planning is essential for improving coding practices.

ABSTRACT. Generative artificial intelligence (AI) is reshaping teaching, learning, assessment, and professional work in higher education. However, institutional approaches to AI integration often remain fragmented. Institutional policies are issued centrally, professional development is delivered as generic tool training, and often teachers are left to decide how AI should be used, disclosed, assessed, and governed in their own courses. The result is a clumsy institutional response to a shared technological transformation, in which educational quality becomes harder to assure, and policy makers, teachers, student support services, and students navigate AI from separate and often uncertain positions. This issue is particularly relevant to tertiary education where programmes are expected to prepare students for organisational roles in which AI increasingly influences analysis, communication, marketing, strategy, and managerial decision-making. Grounded in literature on teacher professional development, AI governance, and assessment design, this paper proposes a Governance-Capability-Curriculum framework for responsible AI integration in tertiary education. The framework links governance, teacher capability, and curriculum and assessment practice to showcase how institutional AI policies can become educationally meaningful. It further contributes a source-critical conceptual model supported by two practical tools: a five-level teacher capability maturity ladder and an assessment assurance cycle for AI-enabled tertiary educational tasks.

ABSTRACT. This study examines how middle-school science teachers collaboratively address linguistic and cultural diversity through practice-based micro-Communities of Practice in Indian science classrooms. Drawing on Communities of Practice and micro CoP theory, we analyzed semi-structured interviews with 12 teachers from two urban schools. The findings identified three practice-based micro-CoPs: multilingual science language scaffolding, culturally contextualized science representation, and diagnostic participation-support planning. These micro-CoPs were organized around recurring pedagogical concerns rather than fixed stakeholder groups. Across the three micro CoPs, teachers worked with colleagues, parents, counsellors, school leaders, and external experts to make science concepts more accessible, culturally meaningful, and responsive to students’ participation barriers. The collaborations generated shared practices such as bilingual cue cards, visual word banks, localized examples, culturally sensitive discussion routines, barrier checklists, modified worksheets, home-support routines, and follow-up communication practices. Digital and material artifacts, including WhatsApp groups, home-language videos, online records, worksheets, models, and checklists, supported the movement of these practices across classroom, home, counselling, leadership, and community contexts. The study contributes a practice-centered account of how localized teacher collaborations generate, adapt, and circulate reusable repertoires for language- and culture-responsive science teaching in diverse classrooms.

ABSTRACT. Large-scale asynchronous online ICT literacy courses require learners to acquire diverse skills in a stepwise manner. We have been improving our courses by incorporating Keller’s Personalized System of Instruction (PSI) model. In previous implementations, PSI-based mechanisms, including block assignments, mastery requirements, and opportunities for resubmission, were introduced. However, despite these improvements, the course completion rate declined from 95.9% to 93.6%.

Within the PSI framework, proctors play a critical role by monitoring learners’ progress and providing appropriate guidance and feedback. In large-scale asynchronous online courses, however, it is difficult for instructors to provide individualized feedback to every learner. To address this issue, we introduced a peer-review mechanism as a means of supplementing the proctor function.

The purpose of this study is to investigate the effects of peer review on learners’ learning behaviors and course completion rates. Through peer review, learners are expected to gain greater awareness of their level of achievement. It is hypothesized that learners who receive low peer-evaluation scores will increase their engagement with learning materials and revise and resubmit their work accordingly. As a result, the introduction of peer review is expected to contribute to an improvement in the overall course completion rate.

ABSTRACT. In engineering education, learning activities often involve students working in the physical laboratory space and in groups. While recent advances in multimodal artificial intelligence and social robotics create new opportunities to support hands-on learning in science and engineering laboratories, many learning analytics platforms still rely primarily on digital trace data, limiting their ability to capture embodied, physical, and collaborative learning processes. This paper presents the design and pilot implementation of authoring multimodal robot-mediated interactions within the LA-ReflecT learning platform. The proposed approach integrates learning and robot management functions, learner interaction logging, and sensor data (like camera) to analyze robot-supported feedback possibilities and create a human-centered learning environment that connects physical task performance with reflective learning support. The pilot study, focusing on a circuit-building laboratory activity, involved 20 learners working in groups of 4 for a 3-hour lab session. The platform was used simultaneously by 2 groups, with the robot serving as an observing peer and collecting snapshots of observed task progress. A machine learning-based classifier was then used to tag each observed frame to understand human-object interactions. The pilot demonstrates the feasibility of embedding multimodal sensing and robot interaction into LA-ReflecT to support learning activities distributed in physical space. With the multimodal learning analytics, a design framework and initial implementation approach provided insights for extending the platforms toward physical, collaborative, and AI-supported laboratory learning contexts. Future work will refine the interaction model, improve automated interpretation of circuit-building actions, and evaluate the impact of robot-supported reflection on learners’ conceptual understanding and problem-solving behavior.

ABSTRACT. This study explored whether realistic drawing performance and spatial ability could predict 3D design performance among design students. A total of 79 undergraduate students from a design-related department participated in the study. Participants completed a spatial ability test, a realistic drawing task, and a 3D design project. Descriptive statistics, Pearson correlation analysis, and multiple linear regression were used to analyze the data. The results indicated that both realistic drawing performance and spatial ability were significantly and positively associated with 3D design performance. Regression analysis further showed that both variables independently predicted students’ 3D design outcomes. Interestingly, no significant correlation was found between realistic drawing performance and spatial ability, suggesting that they represent different but complementary abilities in the design learning process. Descriptive findings revealed that most students demonstrated moderate levels of performance across the four dimensions of 3D design assessment, whereas only a limited number achieved advanced performance levels. The findings imply that observational drawing skills and spatial cognitive abilities continue to play meaningful roles in students’ 3D design learning and should be considered when developing instructional strategies for design education. The study provides practical implications for educators by highlighting the value of incorporating both drawing-based observation training and spatial ability development into 3D design courses.

ABSTRACT. This work-in-progress poster presents our preliminary work on a pedagogical framework, Intelligent-supported Authentic Inquiry-driven Design Learning (IAIDL), that explores how to leverage AI and GPS technologies to support outdoor inquiry learning in design education, with the aim of enhancing their perceptual abilities, authentic inquiry competence, and iterative design thinking. The paper discusses the rationale and theoretical grounds (a crossover between inquiry-based learning and design-based learning) of IAIDL, as well as how it can be operationalized in a market research course in design education.

ABSTRACT. While peer assessment has been widely adopted in technology-enhanced learning (TEL), limited research has examined how learners' psychological dispositions shape peer-feedback behaviors in multimodal contexts. This study investigates the relationship between academic hardiness and peer-feedback engagement in a video-based TEL environment. Fifty-three undergraduate students in an Environmental Communication course participated in three rounds of anonymous peer assessment supported by a digital platform with timestamped annotations and rubric-guided feedback. Academic hardiness was measured across four dimensions (commitment, control-effort, control-affect, and challenge), and peer-feedback comments were coded into affective, cognitive, and metacognitive categories. Spearman's rank-order correlations and Mann-Whitney U tests were conducted. Results revealed selective relationships between academic hardiness and feedback types. Commitment was positively associated with affective feedback, whereas challenge was positively associated with cognitive feedback. No significant associations were found for metacognitive feedback. These findings suggest that academic hardiness supports emotional and cognitive engagement but does not automatically lead to higher-order reflective feedback. The study contributes to TEL research by demonstrating that learner dispositions may shape peer-feedback engagement in multimodal learning environments, highlighting the potential role of instructional scaffolding in supporting metacognitive feedback.

ABSTRACT. The current study investigated the impact of incorporating reflective learning and generative AI tools in Taiwanese high school writing classes on students' self-efficacy, engagement, and writing performance. The study involved 57 twelfth-grade students from an urban high school, divided into three instructional groups: AI-integrated learning(Control), reflective journaling (Experimental Group A), and a combination of AI-integrated and reflective journaling (Experimental Group B). Pre-course and post-course assessments measured outcomes using IELTS Task 1 writing tests, with data analyzed via one-way ANCOVA and thematic analysis. Results revealed that the AI-only control group maintained high self-efficacy and showed the highest behavioral engagement. Meanwhile, the combined group experienced a statistically significant decline in writing self-efficacy. There were no significant differences in writing performance across groups due to the short intervention. However, qualitative logs indicated the combined group successfully developed metacognitive awareness of their linguistic limits. In conclusion, the self-efficacy actually helps students to be more conscious of their learning limitations. The study highlights the importance of a phased pedagogical approach to AI integration, demonstrating that AI tools should be carefully balanced with metacognitive reflection to prevent an illusion of competence and foster genuine self-regulation.

ABSTRACT. In view of the positive learning effects of learner-centered error-correction activities and the prevalence of printed error-correction books, this work targets an online error-correction notebook automatically generated by the system, and examines students’ perceived benefits of this newly developed component for the support of Chinese language learning. A class of fifth-grade students (n=24) participated for six consecutive weeks. Results indicated that a predominant percentage of the students (n=22) affirmed the augmented feature as better supportive of their learning than the original (i.e., no system auto-compilation). Additionally, a chi-square test indicated that students’ selections between the two versions differed significantly, χ²(1) = 16.67, p < .001. Furthermore, thematic analysis highlighted the enhanced function’s usefulness in transforming the error-correction task into a more organized, efficient, and targeted review process while heightening error awareness.

ABSTRACT. In the smart era, teachers' online teaching ability has attracted much attention, and how to evaluate it objectively and effectively has become a key issue to promote development. Existing studies on teachers' online teaching ability evaluation rely on video analysis and peer observation, lacking the student perspective. This study proposes an intelligent evaluation method based on students' learning experience using pre-trained models. We construct the SOLE dataset (12,708 manually coded student comments from Chinese university MOOCs) and build a text classification system. Experiments show that the BERT-CNN model achieves optimal performance with 90.2% accuracy and F1 score of 0.8918. Based on the classification results, we provide personalized teaching ability portraits and comparative analysis across different MOOC courses, along with directions for future research.

ABSTRACT. Recent studies in Computing Education Research have highlighted the importance of cognitive processes such as code comprehension, tracing, debugging, and problem decomposition in introductory programming education. Meanwhile, English as a Foreign Language (EFL) instruction in higher education frequently engages students in activities that require structural understanding and analytical processing, including academic writing, reading comprehension, syntactic parsing, and revision. However, little attention has been paid to the possible relationship between skills fostered through foreign language learning and programming readiness among novice programmers. Drawing on findings from research on novice programmers and code comprehension, this paper explores potential connections between learning activities commonly found in EFL instruction and competencies associated with programming readiness. A conceptual framework is proposed to identify areas of overlap between academic writing, reading comprehension, syntactic parsing, rereading and revision, task interpretation, and cognitive processes emphasized in introductory programming education. This paper does not claim a direct transfer from foreign language learning to programming ability. Rather, it proposes a conceptual framework for examining how structural and analytical literacy developed through EFL instruction may relate to programming readiness and suggests directions for future empirical research.

ABSTRACT. This study investigates the spatial and temporal characteristics of facial expressions to classify academic emotions—boredom, confusion, engagement, and frustration—using the DAiSEE dataset. Two frame selection methods, Targeted and Changepoint, extracted three frames from each 10-second video, and deltas (Euclidean distance, cosine similarity) captured temporal changes in facial landmarks. Six feature selection techniques, including insights from teacher interviews, were tested with five machine learning algorithms: K-Nearest Neighbor (KNN), Decision Tree (DT), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Support Vector Machine (SVM). The findings suggest that Changepoint frame selection has a slight edge in classifying emotions involving dynamic expressions, such as engagement and frustration, whereas the Targeted method performs well across all emotions, especially more static ones like boredom and confusion. Interestingly, models trained on features from only the left side of the face performed comparably to those using features from the entire face, underscoring the relevance of specific facial regions in emotion classification. Additionally, these experiments validated teachers’ insights regarding which parts of the face typically reflect academic emotions. Whole feature selection generally excelled in boredom and engagement classification, while Teacher and Left feature selections were most effective for confusion and frustration, respectively. KNN and CNN consistently outperformed other algorithms, with KNN being most effective for boredom and CNN for confusion, engagement, and frustration. These insights underline the potential of integrating temporal dynamics and feature selection to improve academic emotion classification accuracy.

ABSTRACT. This study examines how emotional granularity and task-fit of large language model-based chatbot responses affect user trust. We propose a Conditional RAG framework that controls emotional expression by combining prompt-based emotion type specification with VAD-based retrieval of emotionally similar examples. Using this framework, chatbot responses were generated under seven conditions: joy (high/low), sadness (high/low), anger (high/low), and neutral. An online mixed-factor experiment with three scenarios and 175 participants was conducted to evaluate the trust. Significant condition effects were observed in all the scenarios. Task-congruent emotional responses generally increased trust compared to incongruent and neutral responses. However, a higher granularity is not always beneficial. In negative contexts, low-granularity responses sometimes produced higher trust than high-granularity responses, suggesting that restrained empathy may be more effective than intense emotional expression. These findings indicate that the emotional design of chatbots should prioritize contextual appropriateness rather than simply increasing expressive richness.

ABSTRACT. This study examines media balance in inquiry learning using log data collected from RefNavi, a web-based platform for information collection and management. The dataset consisted of 15,378 information resources registered by 2,189 student users. Users were categorized into four groups according to the number of registered resources, and differences in media-use patterns were analyzed using chi-square tests and residual analyses. The results revealed significant differences among activity groups. Highly active learners used a wider range of media, including web resources, statistical data, reports, videos, and academic papers, whereas low-activity learners relied more heavily on academic papers and interviews. Differences were also observed in monthly registration patterns. These findings suggest that diversity of information sources, rather than reliance on a particular media type, may be associated with extensive information-gathering activity in inquiry learning.

ABSTRACT. While Large Language Models (LLMs) have vast knowledge, they often struggle to adapt their teaching style to individual students, treating a struggling learner the same as an advanced one. Motivated by the need for more adaptive and resource-efficient AI tutors, this study compares a standard monolithic Single-Agent workflow against a specialized Multi-Agent workflow. Using the Cambridge IGCSE Economics curriculum, we generated responses for 60 questions across four simulated student personalities. We compared a single 70B-active-parameter model (Llama 3.3 70B) against a chain of three smaller 17B-active-parameter models (Llama 4 Scout) to test if architectural specialization yields better results than raw size. Blind evaluations by both AI systems and human educators indicate that the 70B Single-Agent model consistently outperformed the Multi-Agent configuration in terms of Accuracy [+1.05], Personalization [+2.04], and Pedagogical Quality [+2.02], despite similar computational budgets. However, the Multi-Agent workflow proved to be more computationally efficient, using just 39% of the Single-Agent’s computational cost. These findings suggest that, for K–12 tutoring tasks of this scope, overall model capacity plays a more decisive role than agent specialization, and that multi-agent architectures may not inherently yield pedagogical benefits without sufficiently strong underlying models.

ABSTRACT. The generative artificial intelligence (GenAI) tools offer clear benefits for learning and administrative efficiency for both students and educators in higher education. However, GenAI tools can also pose serious risks to patient confidentiality, professional accountability, and data governance when used inappropriately. Hence, the study examines the ethical boundaries surrounding the use of GenAI in clinical and practice placement settings within health and social services education in New Zealand Polytechnics. As such, despite the importance of having clear guidelines on the ethical use of GenAI, there remains a lack of specific policies governing its use in clinical and practice placement settings. While professional standards policies exist, they do not explicitly address GenAI use in detail. Health New Zealand has also issued precautionary guidance restricting the use of GenAI in a clinical context, but these policies primarily focus on prohibitions and provide limited practical direction on the ethical use of GenAI in practice placement settings. Therefore, the study adopts a conceptual policy analysis approach to examine existing guidance on GenAI use and identify gaps in the need for student-focused, placement-specific guidance. In addition, the study introduces a conceptual framework called the clinical GenAI boundary model, which provides explicit guidelines with real-life examples to support students in navigating the ethical use of GenAI within health and social service contexts.

ABSTRACT. This study addresses the lack of engaging and age-appropriate instructional materials in marine sustainability education for elementary learners. Although environmental education often emphasizes topics such as plastic pollution and climate change, sustainable seafood concepts remain underrepresented and difficult for younger students to understand because they involve abstract ideas such as overfishing, resource depletion, seasonal consumption, and responsible decision-making. To address this issue, this study develops a game-based learning (GBL) digital system that integrates interactive storytelling, animation, feedback, and scenario-based tasks to support students’ understanding of sustainable seafood and marine environmental issues. The system is structured into four modules covering fishing methods, seasonal seafood knowledge, and sustainable consumption principles, guiding learners from conceptual understanding to real-world application. A mixed-methods approach is adopted, involving 30 elementary school students, pre- and post-tests, an ARCS-based learning motivation questionnaire, and a Technology Acceptance Model questionnaire. Descriptive statistics are used for data analysis, and inferential statistical methods are not applied, as this study is positioned as a pilot study aimed at obtaining preliminary insights for system refinement rather than statistical generalization. The findings suggest that the GBL system improves students’ conceptual understanding, learning motivation, and system acceptance. These findings will be used to further improve the game design and to support future research with larger samples and inferential statistical analysis. This study contributes to the design of game-based digital learning environments for sustainability education and provides practical implications for integrating GBL into environmental education for young learners.

ABSTRACT. Game-based assessment (GBA) has demonstrated considerable potential for supporting formative assessment; however, most existing scaffolding designs remain static and predefined, limiting their ability to address individual learners' diverse needs. This pilot study integrated a generative AI-based dynamic scaffolding feature as an AI mentor into an existing massive online card-based educational game GBA platform to examine its preliminary effectiveness. Twenty-one fifth-grade students engaged in a 40-minute session with the AI mentor activated. Quantitative results indicated that students demonstrated high technology acceptance and low activity anxiety and cognitive load toward the AI mentor. Qualitative coding revealed that most students perceived the AI mentor as helpful in providing hints, expressed willingness to interact with it, and demonstrated general trust in its responses. These findings provide preliminary support for the feasibility of integrating generative AI-based dynamic scaffolding within GBA contexts and inform the design of future experimental studies.

ABSTRACT. In self-regulated learning (SRL), planning is crucial for achieving goals, yet many learners struggle to create effective plans. Deciding “what to learn” is particularly challenging because it requires an objective assessment of knowledge states and decision-making among numerous options. This study developed and evaluated a learning-planning support dashboard that combines knowledge-state visualization using the Open Knowledge and Learner Model (OKLM), which estimates learner knowledge states by linking learning-activity logs to knowledge models, with recommendation features. We conducted an empirical study during a two-week winter break. The proposed dashboard provided visualizations of unit-level understanding, detailed planning, recommendations for learning units and resource types, and monitoring and reflection functions. Results showed that learners who engaged in monitoring and reflection had significantly longer study times than those who did not, suggesting that the dashboard may have helped sustain engagement in learning. However, changes in SRL skills were not significantly associated with short-term gains in proficiency. The recommendation feature had low usage rates and showed no significant effects on learning outcomes, partly because the knowledge-state visualization itself may have provided sufficient decision-making support. Qualitative analysis of free-response comments supported this interpretation, with learners reporting that the knowledge map helped them identify what to study at a glance. These results suggest that OKLM-based knowledge-state visualization supported learners' “what to learn” decision-making, which was associated with greater engagement during the self-study period, while further design improvements are needed to promote effective use of recommendation features.

ABSTRACT. Generative Artificial Intelligence (GenAI) can be misused to disguise plagiarised programming submissions on assignments, allowing GenAI use. The disguises tend to be pervasive, while the solutions might share GenAI-inspired program flow. Hence, we present a plagiarism detector that can handle pervasive changes using cosine similarity and focuses on token occurrences to ignore shared program flow. The detector is dedicated to data science assignments, where only identifier names unrelated to data science library names are generalised to handle identifier renaming. According to our evaluation of 350 GenAI-disguised copied submissions and 1,417 non-copied submissions, the plagiarism detector is somewhat effective. It can identify 78% similarity in copied submissions despite the complexities of some changes (e.g., statement reordering and changes in the control structure). Further, it achieves 80% top-5 precision. Compared to a baseline approach, the detector is slightly more effective. Pervasive changes by GenAI tend to substantially reduce the resulting degree of similarity.

ABSTRACT. Scratch lowered syntactic barriers and invited young people into creativeprogramming; generative AI now lowers code-production effort further. This conceptualpaper asks how programming education should respond when articulating goals,interpreting information, and evaluating generated behavior may become moreconsequential than producing code. We position systems thinking (ST) as anorganizing lens—not a substitute for programming—for making intended wholes,boundaries, and relationships explicit. Goal-to-Milestone implements a system-first,evidence-returning relational refinement cycle: learners maintain a project-systemmodel, narrow it to one relationship and observable completion signs, then build,preview, judge, and revise. A structured conceptual synthesis and prototype coherenceaudit informed the architecture. Relation-level scoping may shorten the path to aninterpretable preview while preserving learner authority; the framework remainsunevaluated.

ABSTRACT. As generative AI (GenAI) increasingly automates code production, programming education faces a critical question: how is computational thinking (CT) enacted when learners no longer write every line of code themselves? This study introduces AI orchestration—learners’ active process of articulating computational intent, directing GenAI outputs, evaluating generated artifacts, and refining prototypes—as a lens for reconceptualizing CT in GenAI-integrated programming education. A four-week course grounded in Design Thinking was implemented with 18 gifted middle school students in Computer Science, combining Gemini for Education with a Python-based web application framework (Streamlit). CT was assessed with the Computational Thinking Scales (CTS) before and after the course, and Wilcoxon signed-rank tests were conducted on CT and its five sub-components. CT increased significantly from pretest to posttest (p = .007, r = .69), with large effects observed for Algorithmic Thinking (p = .004, r = .76), Creativity (p = .011, r = .68), and Critical Thinking (p = .045, r = .64). No significant changes were detected for Problem Solving (p = .639) or Cooperativity (p = .128). These patterns suggest that CT in GenAI-integrated environments is enacted less through manual code production and more through the orchestration of GenAI particularly through articulating computational intent, evaluating outputs, and iteratively refining artifacts. The findings provide preliminary evidence that CT might be reconfigured around AI orchestration, and they point to the need for assessment tools that capture learners’ AI orchestration in GenAI-integrated programming education.

ABSTRACT. This exploratory study examined whether technology-assisted music training can facilitate L2 speech perception and production, with particular attention to pitch-related prosodic processing. Building on previous findings that Japanese learners of English (JLEs) experience perceptual difficulty when pitch shifts from a lower to a higher register (male→female speaker order), the study investigated whether music training involving alternating male and female vocal parts could improve F0-based speech processing. Four Japanese university students with different levels of musical aptitude participated in a pre-test/training/post-test design. Training consisted of singing practice using a karaoke application with real-time acoustic feedback. Participants completed an AX discrimination task and a speech production task before and after training. Production samples were analyzed acoustically for pitch range and speech duration and were evaluated by native English listeners for comprehensibility and accentedness. Results showed limited but consistent improvements in comprehensibility and perceptual accuracy for upward pitch shifts. Item-level analyses further suggested that discourse-level prosodic encoding remained difficult even for participants with high musical aptitude. These findings tentatively suggest that while short-term music training may facilitate acoustic and perceptual aspects of L2 prosody, discourse-level prosodic control may require additional abilities beyond accurate pitch processing alone.

ABSTRACT. This design-oriented paper presents TextQuest AI, a teacher-oriented authoring environment for creating short AI-NPC storyworld activities for text exploration. The system addresses a practical design problem: teachers may wish to transform readings, local-cultural materials, historical cases, or inquiry topics into interactive learning experiences, but they often lack time, technical skills, and narrative-design expertise. TextQuest AI supports teachers in converting source texts into locations, AI-powered non-player characters (NPCs), clues, tasks, and reflection prompts. In the student interface, learners enter a web-based game-like environment, interview AI-NPCs, collect evidence, compare perspectives, and produce a final interpretation within an approximately 30-minute activity. In the teacher interface, an AI design assistant helps analyze texts, suggest NPC roles, generate dialogue rules, propose tasks, and check alignment with learning goals. The initial evaluation will involve 26 teachers. After a 20-minute explanation and guided trial, they will use the system for 40 minutes to design an AI-NPC storyworld activity and then evaluate its usability, usefulness, pedagogical value, teacher control, and classroom feasibility. The paper describes the design rationale, system workflow, example scenario, and evaluation plan.

ABSTRACT. The increasing integration of generative artificial intelligence tools such as ChatGPT in higher education has reshaped how learners approach academic tasks and learning processes. This study explores how ChatGPT influences trainee teachers’ learning experiences through the lens of Self-Determination Theory (SDT) and basic psychological needs. Specifically, it examines how the use of ChatGPT shapes learners’ sense of autonomy, competence, and relatedness in their academic activities. This study adopted a qualitative phenomenological approach, involving semi-structured interviews with seven trainee teachers who actively use ChatGPT for learning purposes. The data were analyzed using thematic analysis to identify patterns in participants’ experiences. The findings indicate that ChatGPT functions as an academic support tool that enhances learners’ autonomy, competence, and relatedness in learning processes. Participants reported greater autonomy over their learning, a stronger sense of competence in completing academic tasks, and new forms of supportive interaction that shaped their sense of relatedness in learning. However, the findings also suggest that excessive reliance on ChatGPT may influence the depth of learning and reshape interpersonal interactions in learning contexts. Overall, the study highlights that ChatGPT plays a significant role in supporting learners’ sense of autonomy, competence, and relatedness while also presenting challenges that require balanced and critical use. The findings contribute to ongoing discussions on artificial intelligence in education and provide insights for the effective integration of AI tools in higher learning environments.

ABSTRACT. With the proliferation of online learning, shallow reading which characterized by skimming and brief glances has become a common daily learning strategy. However, accurately identi-fying shallow reading poses a significant challenge. Concur-rently, eye-tracking technology, as the optimal feedback of psychological activity, emerges as one solution. Yet in studies using eye movements to identify student reading states, the selection of static features has limited the accuracy of shallow reading recognition. To enable page-level identification of shallow reading, this study proposes a shallow reading recog-nition model based on a DistilBERT-based encoding frame-work for multimodal data. This system builds upon multi-modal data for learning behavior recognition. Specifically, for identifying shallow reading, it not only relies on the I-VT al-gorithm to extract gaze, saccade, and fixation events from raw eye-tracking data but also leverages timestamps from ebook data to inject spatial information about these events. The trained EyeFormer model achieved an AUC of 0.963 ± 0.015 and an accuracy rate of 90.1% in a page-level task during a controlled experiment involving 63 non-native English-speaking university students. These results demonstrate the model's high precision, providing a reliable behavioral foun-dation for subsequent personalized reading interventions based on generative AI.

ABSTRACT. Today’s generative AI systems increasingly produce hallucinated or flawed representations in learning contexts, yet such outputs are typically treated as risks to be minimized. Grounded in AIED and learning sciences, this study examined whether hallucinated AI artifacts can instead function as productive resources for collaborative sensemaking. We report a quasi‑experimental classroom study in higher education in which student teams engaged with either hallucinated or canonical versions of a domain model prior to instruction. Using mixed‑methods analyses, we traced how early sensemaking, model revision, and collaborative understanding unfolded over time. Results indicate that exposure to hallucinated artifacts initially afforded confidence and surface-level coherence, but also introduced epistemic uncertainty that prompted questioning, critique, and revision during collaboration. Although these artifacts did not support early correctness, they resulted in deeper model‑based reasoning, stronger evaluative transfer, and more flexible epistemic stances. In contrast, canonical artifacts supported early convergence but encouraged more verification‑oriented reasoning. Interpreted via learning theories of productive failure, cognitive conflict, and epistemic vigilance, these findings suggest that AI hallucinations, when intentionally designed and pedagogically scaffolded, can function as failure‑driven epistemic perturbations. We briefly discuss implications for AIED system designs that prioritize sensemaking, epistemic agency, and responsible human-AI collaboration over short‑term accuracy.

ABSTRACT. As the necessity of text-based programming education for elementary students grows, learners often experience severe cognitive overload and demotivation during the transition from visual block-based environments to text-based languages like Python. Furthermore, traditional programming education frequently relies on abstract, contrived examples, making it difficult for students to perceive the practical value of coding and often neglecting the cultivation of Social-Emotional Learning (SEL) competencies essential for contemporary education. To address these challenges, this study proposes a 12-session instructional design for a "Smart Class Agent" based on a "Data-Logic Fusion" approach, targeting fifth-grade students. This pedagogical model meaningfully integrates authentic, real-world classroom data—such as school event schedules and peers' facial expressions—with core logical control structures. To mitigate the cognitive burden of unfamiliar syntax, the program systematically applies cognitive scaffolding strategies, including 1:1 structural contrast and template-based tinkering. Through this contextualized learning experience, students collect and manipulate data relevant to their daily lives to build a modular agent that resolves actual classroom inconveniences and provides emotional support to peers. The anticipated outcomes suggest that the Data-Logic Fusion model will not only facilitate a seamless, low-anxiety programming language transition but also simultaneously enhance learners' computing thinking (CT) by grounding algorithms in reality, and foster their SEL competencies by empowering them to contribute positively to their classroom community

ABSTRACT. Extensive research has examined e-learning adoption using the Technology Acceptance Model (TAM). The most relevant TAM elements for e-learning are computer self-efficacy, subjective/social norms, perceived enjoyment, and system quality, according to the literature. In this study, we developed a comprehensive TAM model that considers student privacy. We aimed to understand students’ perspectives on data privacy in higher education through a survey at a Japanese university (N=286). A theoretical TAM model was used and expanded to survey the students. The hypothesis tested through structural equation modeling linked students’ behavioral intention to use EdTech with their privacy perceptions. Based on these findings, we proposed research, design, and policy recommendations to protect student privacy in educational settings.

ABSTRACT. This study explores whether gamified chatbot instruction improves primary students’ engagement in different modes. A randomized controlled trial compared individual, collaborative, and traditional groups, using school engagement scales and interviews. Results showed a significant time × group interaction for overall engagement. Emotional and cognitive engagement improved in both gamified groups, while the control group declined. In conclusion, gamified chatbot can effectively enhance engagement, offering insights for designing engaging language instruction

ABSTRACT. Large language models often exhibit uneven performance across different knowledge domains, where a single general-purpose model may not perform equally well across all tasks. This study proposes an efficient expert-routing framework that dynamically directs multiple-choice questions to domain-specific expert models. The framework employs a routing module to classify questions into math, programming, or philosophy domains before forwarding them to specialized expert models for inference. Experiments were conducted on 300 samples extracted from the MMLU benchmark, with 100 questions evaluated per domain. The proposed framework achieved a domain routing accuracy of 99.0% and improved overall multiple-choice question answering accuracy from 52.3% to 63.3%, yielding an absolute improvement of 11.0 percentage points over the baseline model. The largest gain was observed in programming tasks, where performance increased by 18.0 percentage points and achieved statistical significance (p=0.0019). The overall improvement across all domains was also statistically significant (p=0.0013). These findings demonstrate that domain-aware routing can effectively enhance reasoning performance by leveraging specialized expert models instead of relying solely on a single general-purpose model.

ABSTRACT. This study addresses the gap between factual progress and cognitive perception arising when students fail to meet learning goals. We developed an interactive reflection support system incorporating the column method used in cognitive behavioral therapy (CBT) to foster adaptive thinking. In all, 10 university students participated in a 6-week crossover evaluation. The results indicated that the CBT-based chatbot structured reflections compared to the baseline. In particular, the intervention significantly improved the quality of introspective writing, shifting focus from mere description of the cause to the formulation of concrete, actionable strategies and adaptive reasoning. By addressing psychological barriers and cognitive biases, the system allowed students to translate negative emotions into proactive planning. These findings indicate that the integration of psychological frameworks with generative AI can enhance self-regulated learning, promoting deeper self-insight and a more resilient behavioral adaptation in academic research environments.

ABSTRACT. Given the educational efficacy of error correction, this work focuses on designing theory-guided error-correction learning activities to facilitate the attainment of significant educational goals. Noting the significance of social-emotional competence for long-term personal development, and in view of social awareness theory, a two-stage learning activity was proposed—correcting one’s own wrong answers in the first phase and correcting the class-wide most commonly incorrectly answered questions in the second phase. To examine its educational potential, one fifth-grade class (n=22) participated. Several major findings were obtained. First, results from one-sample t-tests indicated that perceived learning usefulness ratings were significantly higher than the scale midpoint value of 3 for both phases. Second, a paired-samples t-test found that the difference between the two phases was not statistically significant. Third, qualitative data indicated that students appreciated the two-stage error-correction learning activity for different reasons, highlighting their complementary functions: the first phase supporting personal error diagnosis, and the second phase supporting awareness of commonly shared misconceptions and difficult problem types. Overall, the lack of a statistically significant difference between the two phases, which were associated with distinct complementary functions, suggests that our theory-guided two-stage learning activity design has promising educational potential as an enriched error-correction activity that can contribute meaningfully to students’ learning.

ABSTRACT. In higher education, students often need accurate access to academic regulations, training policies, and administrative procedures. However, this information is usually organized in hierarchical structures, contains complex relationships, and is difficult to retrieve effectively using traditional text retrieval systems. This study proposes a student support chatbot based on Graph Retrieval Augmented Generation to improve the retrieval and interpretation of academic information in universities. The system constructs a knowledge graph from academic regulation documents, in which concepts and relations are represented as subject, relation, and object triples. Based on this structure, the system combines knowledge graph-based retrieval with semantic retrieval using a vector database. This approach allows the system to use both textual content and the relationships among regulation units, thereby providing more reliable context for the large language model during answer generation. Experiments on a set of questions related to student regulations show that the proposed system improves information retrieval compared with the baseline language model. Specifically, the system achieves a 21.20% improvement in Recall and outperforms the baseline in Recall in 74.60% of the cases. These results demonstrate the potential of Graph Retrieval Augmented Generation for building learning support and academic advising systems that can provide accurate, grounded, and student relevant information in higher education.

ABSTRACT. This study applies BERTopic, a neural topic modeling framework, to 51 empirical studies on creative, arts-based, and AI-enhanced pedagogies in health professional education, grounded in a sociomaterial theoretical framework. Seven coherent topics (49/51 documents) emerge across three axes: empathy development (n=12, 24.5%), clinical skills training (n=21, 42.9%), and methodological framing (n=16, 32.7%). Cross-method convergent validity is established through correspondence with deductive meta-analytic outcome domains. Critically, BERTopic surfaces a structural knowledge gap: equity, decolonial, and AI ethics vocabulary is absent across all topic outputs, constituting a sociomaterial condition in which certain communities and epistemologies remain unenrolled in the field's knowledge production infrastructure.

ABSTRACT. This paper proposes a machine learning-based adaptive self-assessment method for intersubjective evaluation using checklists and rubrics. The proposed method aims to reduce learners’ response burden while maintaining evaluation performance by sequentially imputing unanswered checklist items based on prediction confidence and subsequently estimating the rubric-based overall evaluation. Simulation experiments were conducted using university self-assessment data on eight soft skills collected between 2020 and 2025 under two settings: Single Self-Assessment (SSA), which uses only current responses, and Dual Self-Assessment (DSA), which additionally incorporates historical self-assessment data. The results demonstrated that the proposed method reduced the number of checklist items while maintaining acceptable evaluation performance across most skills. In particular, the DSA setting achieved higher reduction rates than the SSA setting, suggesting that historical self-assessment data can support more efficient adaptive self-assessment with fewer learner responses. These findings indicate that the proposed method has the potential to support sustainable and scalable intersubjective self-assessment practices in educational settings.

ABSTRACT. The premise of this study was to examine how the use of Information and Communications Technology (ICT) in inquiry-based learning (IBL) predicts academic achievement in mathematics, reading, and science, both directly and when mediated through student curiosity. To do this, we analysed the data from Finland and South Korea in the Programme for International Student Assessment (PISA) 2022. In the present study, ICT use in IBL consists of three different components: 1) Standard activities, 2) Investigative activities, and 3) Project management. Moreover, student gender and the index of economic, social and cultural status (ESCS) were included as covariates to consider possible confounding effects. Our results show that ICT use in standard IBL activities is a consistently positive indicator of academic proficiency in all subject domains in Finland and South Korea. Moreover, the predicted total effect was even greater in all three subject domains in Finland when mediated through student curiosity. ICT use in investigative activities, however, predicted a wholly negative effect on student academic achievement and in Finland, using ICT in investigative activities reduced student curiosity and further decreased the predicted academic proficiency in all three subjects. Our findings indicate that the way that ICT is used in IBL can influence both student curiosity and academic proficiency in PISA 2022.

ABSTRACT. This study investigates linking national digital skill standards (DSS-P) to knowledge maps of IT subjects, examining whether such links help learners develop career visions and understand learning steps. We developed a prototype system in which selecting a career role displays linked knowledge map learning items and their importance levels. A preliminary evaluation with university students (n = 9) suggested usefulness for career vision development, although more specific learning support is needed.

ABSTRACT. . Artificial Intelligence (AI) is increasingly influencing educational systems by providing innovative opportunities for teaching, learning, and institutional decision-making. As Technical and Vocational Education and Training (TVET) institutions continue their digital transformation efforts, understanding faculty members’ readiness for AI adoption has become a critical concern. Despite growing interest in AI integration within education, limited research has focused on AI readiness in TVET environments, particularly within the Saudi Arabian context. This paper proposes a conceptual framework for analyzing Artificial Intelligence readiness in Saudi TVET institutions using machine learning approaches. The framework integrates key determinants of AI readiness, including digital competence, institutional support, technology readiness, perceived usefulness, and prior experience with AI technologies. Machine learning techniques are discussed as potential analytical tools for identifying patterns, evaluating relationships among readiness factors, and supporting data-driven decision-making. The proposed framework aims to provide a theoretical foundation for future empirical investigations and practical implementation strategies. By linking educational technology adoption theories with machine learning-based analytics, the study contributes to the growing body of knowledge on AI integration in vocational education. The framework may assist policymakers, institutional leaders, and educational practitioners in designing effective strategies to enhance AI readiness and facilitate successful digital transformation initiatives within TVET institutions.

ABSTRACT. Responsible educational use of artificial intelligence requires not only technological knowledge but also the ability to evaluate ethical risks. This study examined self-reported ethical competence and AI technological knowledge among 358 pre-service and 55 in-service teachers from Poland. Competence was measured using the Ethics and AI-TK dimensions of Celik’s AI-TPACK scale. Pre-service teachers reported higher levels of both ethical competence and AI technological knowledge than in-service teachers, although the between-group difference was more robust for AI-TK. Age was weakly and negatively associated with ethical competence, whereas teaching experience and year of study were unrelated to it. Comparisons with previous AI-TPACK studies showed that the Polish results were broadly comparable to those reported for other teacher samples, although contextual differences warrant caution. The findings support explicit, scenario-based ethics training in both initial teacher education and continuing professional development.

ABSTRACT. In K-12 biology education, Generative Artificial Intelligence (GenAI) is sometimes employed to support learning. However, the focus is only on allowing its use. GenAI can generate questions to help students learn several courses. Hence, this paper presents BioQuest, an Indonesian GenAI-supported interactive biology learning platform for middle school students. GenAI acts as a learning material generator to support a learning platform, rather than an agent to interact with. The generated questions are unique as they consider the student to have a particular role (e.g., a bear in the context of global warming) and are combined to form a journey for that role. According to four evaluations, BioQuest functions well. Further, the generated questions are relatively acceptable, receiving a quality score of 4.5 from GenAI and 3.2 from humans. Moreover, it shows potential for biology learning from middle school students’ perspective.

ABSTRACT. The increasing use of Artificial Intelligence (AI) in higher education brings unique challenges and opportunities to Māori communities, particularly in respect to mātauranga Māori, Indigenous data governance, and Te Tiriti o Waitangi obligations. Hence, the study intends to develop a Māori Values-Based Framework for the use of AI in Higher Education using a comparative policy analysis approach. The data will be collected from publicly available AI policies from New Zealand higher education institutions and Māori governance and ethical frameworks. To evaluate the existing AI guidelines through with Māori ethical values and cultural expectations, the study utilised the ethical framework developed by Māori scholar Sir Hirini Moko Mead which emphasises relationship, collective wellbeing, and cultural integrity. The findings showed that while current AI policies demonstrate strong commitments regarding transparency, accountability, and academic integrity, they provide limited recognition of Māori data sovereignty, Indigenous governance, cultural protection, and collective wellbeing in AI usage in higher education. This limitation may not only hinder the effective use of AI but also increase the risk of unintended misuse when engaging with Māori knowledge, cultural values, Indigenous data, and Te Tiriti o Waitangi obligations. Based on the results, the study suggests AI guidelines that integrate the principles of Rangatiratanga, Manaakitanga, Whanaungatanga, Kaitiakitanga, and Kotahitanga that are in accordance with Te Tiriti o Waitangi commitments.

ABSTRACT. Artificial intelligence (AI) and coding are increasingly being integrated into K–12 education through global curriculum reforms. This study examines Indonesia's readiness for AI literacy by mapping its national K–12 Informatics curriculum alongside the recent Coding and AI elective against major global frameworks, including UNESCO K–12 AI Curricula, UNESCO Guidance for Generative AI, OECD Learning Compass 2030, and ISTE Standards. The preliminary findings indicate that Indonesia’s policy aligns strongly with global standards in computational logic, data literacy, and foundational programming. However, structural challenges remain regarding infrastructure equity in 3T regions, large-scale upskilling of non-specialist teachers, and age-appropriate AI ethics guidelines. This paper provides strategic insights for policymakers and educators in developing nations aiming to scale K-12 AI literacy.

ABSTRACT. This study aims to investigate and compare semantic similarity levels in nursing students’ academic writing at Instituto Superior Cristal, Timor Leste, written with search engines and generative AI tools. Employing a quantitative approach, the analysis was undertaken on 761 documents, covering 400 pairs of the search-engine group and 361 pairs of the generative-AI-powered group. This semantic similarity assessment was combined with thematic cluster mapping based on network analysis to evaluate the structure of the discourse. The statistical test results demonstrated equivalent levels of similarity with no significant difference (p = 0.9439). The mean semantic similarity in the search-engine group was 0.2405, compared with 0.2418 for the generative-AI group. The resulting low scores in both groups reflected varied ideas, lexical diversity, and an independent exploration of divergent topics by the students.

ABSTRACT. This study developed and pilot-tested an eight-session school–home-linked AI literacy program for elementary students based on the four domains of the OECD/EC AILit Framework. Thirty-nine students in Grades 4 and 6 took part over two weeks. Paired pre–post data from 32 students, collected on a 25-item self-report scale, showed a significant increase in overall AI literacy (3.80 to 4.21, dz = .99), with significant gains in all four domains. Home activity records showed students identifying everyday AI, formulating responsible-use principles, and proposing AI solutions for family members' needs. The small single-group design limits causal claims, but the findings offer preliminary evidence for the feasibility of linking classroom learning with AI literacy practice in family contexts.

ABSTRACT. This research aims to analyze and determine significant statistical differences of lexical variations between articles written with the help of Generative AI (such as Chat GPT) and internet browsing. Lexical density is measured with Type-Token Ratio (TTR) metrics, the ratio of the number of unique words to the word count from 79 articles in Tetum language (national language of Timor Leste). This research used a quantitative method using Independent-Sample t-test. The results have shown that Generative AI-assisted articles have a higher mean TTR of 40.67%, compared with those produced through internet browsing, which was 36.36%. Statistical testing showed that the difference was extremely distinctive (p < 0.001). The patterns of articles by internet browsing tend to be repetitive; additionally, when it is written under the same topic. On the other hand, Generative AI tools were found to enhance the distribution of the vocabulary, employing varieties of synonyms. .

ABSTRACT. This is a design paper that describes an Moodle learning sequence in which learners will experiment with an artificial intelligence algorithm using a manipulable HTML simulation and later code the same algorithm in C++ with CodeRunner. Large language models and coding agents, such as Codex, have been used to generate code for simulations, exercise variations, reference solutions, and test cases. The AI Garden prototype has five unlocked levels where the learners will adjust two neural weights, see the effect of the changes on three outputs, and minimize the error on a small dataset. The project is grounded in the perspectives of constructivist and active learning, scaffolding, and formative assessment. The paper demonstrates how technology can be used to design a coherent learning environment.

ABSTRACT. Technology-enhanced learning systems generate rich traces of how learners interact with digital materials. However, turning these interaction logs into meaningful learning evidence remains a challenge, particularly in contexts where reading is an important part of learning. This showcase presents the integration of BookRoll, an interactive e-book environment, within the Learning Analytics for Adaptive Education (LEAD) platform in Thailand. BookRoll usage records cover 48 distinct courses over 21 months (October 2024–June 2026), with 180,037 aggregated course-level monthly user counts. The LEAD platform has been deployed to 1,574 teachers across 750 schools and has more than 58,308 users. In a user-feedback survey (N=780), BookRoll received a mean satisfaction rating of 4.50/5.00, with 90.8% of respondents rating it 4 or 5 out of 5. The approach organizes digital reading interactions into multiple levels of learning evidence, including observable reading behavior, active reading engagement, and cognitive-oriented reading activities. These findings demonstrate practical deployment and positive user acceptance, but do not establish learning gains. The relationship between reading evidence and learning outcomes remains an important direction for future empirical research, alongside learner and cognitive analytics, adaptive support, and AI-supported learning analytics.

ABSTRACT. Automated coding of classroom discourse typically treats human-annotated labels as a reliable gold standard. This paper questions that assumption. We apply a manual-grounded prompt---the official TalkMoves coding manual, verbatim and unmodified---to two LLMs (DeepSeek V4 Pro and Claude Opus 4.7) on 400 teacher utterances. Both models exceed the prior best reported kappa of 0.58, confirming the strategy's effectiveness. We then treat the 104 AI-human disagreements not as model errors, but as cases for audit. Manual review finds that 42% reflect annotation inconsistencies in the gold standard, not AI mistakes. This finding is supported by cross-model evidence: cases where both models agree against the gold standard carry a 54% inconsistency rate versus 13% for single-model disagreements, with 89% of cross-model cases involving both models predicting the exact same alternative label. An independent second rater further agrees with the AI in 56.7% of disputed cases versus 30.0% for the original labels. These results suggest that manual-grounded LLM coding can serve as a practical annotation quality audit tool for educational discourse datasets.

ABSTRACT. Whether AI tools support genuine self-regulation—or merely simulate it—depends on how human-AI interaction is structured. We present ScholarMate, a four-agent LLM reading assistant instantiating a co-regulation loop: Navigator (system-initiated external regulation) works in concert with three learner-activated agents—Lexicon, Dialectician, and Reviewer—whose @mention selection requires learners to diagnose their own difficulty type. Deployed over ten weeks in a graduate course at Beijing Normal University (N = 35, 12,442 interaction records), ScholarMate generated two structurally distinct external regulation signals: section-transition triggers (metacognitive challenge questions at chapter boundaries) and idle-reminder triggers (redirection after extended dwell time). Lag sequential analysis revealed qualitatively different co-regulation effects: section triggers produced high learner activation (78.1% response rate, Agent-selection profile matching the overall voluntary baseline), while idle-reminder triggers produced lower activation (33.8%) but a significant directional shift toward Dialectician (+6.2 pp over baseline), suggesting learners infer deeper processing difficulty from extended dwell time. K-means clustering (k = 3) identified three co-regulation profiles—Low-Engaged (n = 8), Deep-Engaged (n = 14), and Surface-Active (n = 13). Message-length asymmetry confirmed qualitatively distinct self-regulatory acts across Agents (Lexicon M = 795 vs. Dialectician M = 71 characters; H = 925.51, p < .001). Reviewer's persistent underuse (M = 4.8) identifies self-reflective regulation as the primary co-regulation gap. Four design principles for authentic co-regulation loops in LLM-based learning environments are proposed.

ABSTRACT. In intelligent tutoring systems (ITSs), learners may enter learning deadlocks, often referred to as wheel-spinning, where productive learning stalls despite continued interaction. While prior work has explored affective and cognitive indicators of such states, feedback triggering is still commonly based on observable performance outcomes. This paper proposes a sensor-agnostic architecture for in-process feedback that explicitly separates when to intervene from what feedback to provide. Intervention timing is inferred from process-level signals such as affective and embodied cues, while feedback content is conservatively diagnosed using an explicit symbolic domain model. Building on prior work using EEG-based timing inference, we examine whether the same architectural principle holds with vision-based sensing. We implement the proposed architecture in an ITSs for arithmetic word problem posing, using a Transformer-based vision model to detect potential learning deadlocks and a domain knowledge model to diagnose content-level issues. A small-scale preliminary evaluation with university students indicates that, although timing detection is imperfect and exhibits a high false-negative rate, the feedback content generated when intervention occurs remains appropriate and non-disruptive. These results suggest that separating timing inference from content diagnosis provides a robust and safe design principle for in-process feedback, enabling practical deployment across different sensing modalities.

ABSTRACT. Adversarial thinking is to adopt an intruder's perspective to plan a break-in. In crime prevention, this approach helps residents generate security measures from diverse viewpoints without being constrained by limited perspectives. However, most existing training systems for adversarial thinking only provide environments to encourage thinking like an intruder. They do not support the derivation of concrete security measures. The objective of this study is to develop an adversarial thinking training system which identifies a learner's limited perspectives based on their proposed security measures and recommends specific situations to help them overcome these perspectives. The system uses a 3D house model as a target to set security measures. First, the learner sets security measures from a resident's perspective. Next, the learner plans a break-in from an intruder's perspective. In this step, the system analyzes the learner's initial security measures to identify limited perspectives and recommends tools for breaking into the house to prompt perspectives the learner missed. Last, the learner generates new security measures from different viewpoints based on the break-in planned with these tools.

ABSTRACT. Knowledge Tracing (KT) research in Intelligent Tutoring Systems (ITS) has long focused on estimating learners' knowledge states from response data, with recent advances in deep learning and Cognitive Diagnostic Models improving both accuracy and interpretability. However, a fundamental challenge remains: conventional KT approaches estimate knowledge states only at the moment of each response, and largely ignore the information contained in blank intervals—the time spans between consecutive responses. Moreover, existing models do not adequately capture the non-autonomous, nonlinear nature of learner dynamics, wherein the system's properties change over time as learning progresses. This paper proposes a dynamical systems framework for tracking learners' internal understanding states, motivated by how human teachers estimate learner understanding in practice. We argue that human teachers implicitly maintain an internal model of the learner and continuously refine it through prediction and error-based correction. From this perspective, we introduce a framework consisting of two components: a Mirror System Model and a Confidence Model. In the proposed framework, the learner is modeled as a non-autonomous nonlinear dynamical system whose internal state evolves over time. The learner's understanding is characterized by three global states—Not-Understanding (NU), Plateau (P), and Understanding (U)—each corresponding to a distinct attractor in phase space. The ITS maintains a Mirror agent that shares the same state variables as the genuine learner and generates predicted behaviors to compare against observed responses. The discrepancy between predicted and observed behavior, termed Surprise, is used to update the Mirror model dynamically. The Confidence Model manages the reliability of the current state estimate, capturing the degree to which the Mirror model approximates the true learner dynamics. To evaluate the proposed framework, computational simulations were conducted using three toy learner scenarios representing different learning trajectories. Results demonstrate that the Mirror agent with Surprise successfully tracks the learner's understanding state from observed response data. The framework offers high interpretability without requiring pre-training and highlights the importance of interaction-based refinement over static high-accuracy estimation as a design principle for ITS.

ABSTRACT. Artificial intelligence (AI)-assisted tools can help students enter complex engineering workflows, but fluent outputs may hide wrong commands, fabricated values, and weak engineering assumptions. This paper reports a practice-driven mixed-methods design case from the first offering of a project-based postgraduate course on low-altitude unmanned aerial vehicle (UAV) perception. The course treated AI output as a hypothesis to be checked against documentation, code, calibration files, logs, metrics, visual outputs, and repositories. Students worked with DockerHub demos, Docker and Robot Operating System (ROS), ORB-SLAM3, OpenSplat, U-Net-style segmentation, public UAV datasets, and a group final project. Data included non-matched beginning- and end-of-semester surveys, 31 reflection reports, and GitHub traces from two required individual assignments, with final project reports, slides, optional repositories, and leaderboards analysed as contextual group evidence. The post-course cohort reported higher AI Literacy, Critical Thinking, and Problem Solving scores. Reflections showed that students used AI to enter difficult workflows, met unreliable outputs, and recovered through documentation checks, metric comparison, visual inspection, one-variable tests, basic consistency checks, and manual debugging. The study contributes a verification-oriented accountability cycle and three practice principles for responsible AI-supported engineering education.

ABSTRACT. The development of Computational Thinking has become a key priority in contemporary education, particularly in teacher education where future educators are expected to design meaningful and problem-based learning experiences. Although gamification has been widely recognised for enhancing engagement, its role in supporting higher-order cognitive development remains insufficiently understood. This study investigates how CT develops within a gamified STEM learning environment among teacher trainees. An explanatory sequential mixed-methods design was employed involving 87 Bachelor of Education students enrolled in an Educational Gamification course. Quantitative data were collected using a 30-item performance-based multiple-choice instrument administered before and after a 14-week intervention. Qualitative data were obtained through semi-structured interviews to explore participants’ learning experiences and cognitive processes. The quantitative results indicated an improvement in CT performance, with mean scores increasing from 59.32 (SD = 11.55) to 75.48 (SD = 8.47), accompanied by a large effect size (d = 1.22). The qualitative findings identified four key themes: gamification as a cognitive structuring mechanism, the embedding of CT within game mechanics, the role of authentic STEM problem-solving contexts, and the transformation of teacher trainees’ pedagogical perspectives. The integration of findings suggests that the effectiveness of gamification extends beyond motivation, functioning as a structured environment that shapes how learners engage in problem-solving processes. By aligning game mechanics with CT components and situating learning within authentic STEM contexts, the intervention enabled meaningful and transferable cognitive development. This study contributes to the literature by providing empirical evidence on the mechanisms through which gamified learning environments support computational thinking and offers practical implications for instructional design in teacher education.

ABSTRACT. This work examines how school-based micro-Communities of Practice (CoP) co-create, adapt, and circulate teaching artifacts to support diversity-responsive middle-school science teaching. Drawing on classroom observations, semi-structured interviews with middle school science teachers, and artifact data related to linguistically diverse Indian classrooms, an analysis is presented on how bilingual flashcards, diagrams, mnemonics, checklists, and digital routines are developed and used across classroom, school, home, and community contexts. The findings show that micro-CoPs involving teachers, counsellors, school leaders, parents, and external experts transform recurring participation barriers into reusable artifacts and routines. These artifacts function not merely as teaching aids but as participation infrastructure. The study contributes an artifact ecology account of language- and context-responsive teaching by showing how material, visual, linguistic, procedural, and digital artifacts mediate learner participation while carrying shared pedagogical repertoire across actors and contexts.

ABSTRACT. HHistorically, Japanese companies have relied heavily on On-the-Job Training (OJT) for human resource development. However, recent demographic and social changes have contributed to rising turnover among early-career employees, creating a growing need to reconsider OJT from a broader developmental perspective. This study reconceptualizes OJT as a form of onboarding support and explores its potential from a job crafting perspective. An exploratory case study was conducted in a Japanese small and medium-sized enterprise (SME). The findings suggest that OJT can support workplace adaptation not only by transferring job-related knowledge and skills but also by fostering relationship building, meaning-making, and proactive learning. These findings highlight the potential of understanding OJT as an onboarding practice that supports employee development and adaptation.

ABSTRACT. Educational findings often lack generalisability due to substantial learner heterogeneity, shifting focus from “what works?” to “who benefits?” in recent years. However, attempts to estimate heterogeneity of treatment effects (HTE) remain scarce in education, and prior work relies on costly large-scale experiments, which also face ethical challenges. To address this issue, this paper explores the potential of their estimation by the secondary use of real-world data (RWD), an approach that remains underexplored in the literature. Our case study uses RWD of secondary-school students collected in the LEAF (Learning and Evidence Analytics Framework) system, including log data of reading behaviour and learning outcomes (𝑁=357), and focuses on the effect of bookmark revisiting behaviour on learning outcomes. Because learning is complex and dynamic, we use a non-parametric machine learning method for estimating individual conditional treatment effects. We find that, despite no significant average treatment effect, students with frequent revisits and many sessions tend to benefit from bookmark revisiting behaviour. This suggests the potential impact of encouraging them to effectively use bookmarks. However, the model fails to provide robust statistical evidence for HTE, likely due to the limited sample size and quality of covariates. Overall, the case study demonstrates the potential of the secondary use of educational RWD, while highlighting the clear need for scaling up RWD sharing in terms of volume and variety. To promote data sharing, we release the dataset used in this study to the research community using ReLEAF, a recently proposed trustworthy data sharing platform.

ABSTRACT. Computer-based learning environments (CBLEs) place substantial metacognitive demands on learners who must independently plan, monitor, and evaluate their own learning while navigating non-linear, multimedia-rich content. Although metacognitive prompts have been shown to address this challenge in reading and writing contexts, their design and empirical evaluation in structured engineering problem-solving remain largely unexplored. To address this gap, we have designed and developed a learning environment, MetaGuru, embedded five metacognitive prompts: orientation, planning, monitoring, evaluation, and reflection to foster metacognitive strategies during circuit analysis problem-solving. This paper introduces the design and development of MetaGuru. For the development of MetaGuru, we used the ADDIE model of iterative instructional design, which provides a principled framework for translating theoretical design decisions into empirically validated instructional interventions through successive cycles of formative evaluation. Following the development of MetaGuru, a study was conducted with first-year engineering students (n = 45) to evaluate the system's effectiveness in supporting learning in circuit analysis. A pre-test and post-test design were used to assess learners' conceptual knowledge before and after interacting with MetaGuru. Post-test scores were significantly higher than pre-test scores (pre-test M = 9.64, SD = 3.31; post-test M = 11.91, SD = 2.79; W = 31.5, p < 0.001, Cohen's d = 0.93), with a mean normalized learning gain of g = 0.19, suggesting that MetaGuru supports initial conceptual learning in circuit analysis problem-solving.

ABSTRACT. This study proposes TriFusion-EduVec, a multimodal transformer model that jointly represents statistical features, behavior sequences, and time-interval information to improve both the performance and the post-hoc interpretability of dropout prediction. Instead of treating timestamps superficially, we explicitly model the temporal rhythm of student engagements. Empirical evaluation on two large-scale MOOC datasets demonstrates that TriFusion-EduVec significantly outperforms recent baselines. More importantly, through an in-depth analysis of the model's attention distribution and masking experiments, we find that the critical indicator of student dropout lies not in the execution of individual actions or specific behavior sequences, but in the underlying learning rhythms and time-interval dynamics. While dropout students exhibit fragmented, high-frequency, and jittery rhythms, persisting students maintain a structured and stable learning cadence. These empirical findings provide solid statistical interpretations that align with self-regulated learning and cognitive load theories, offering researchers and platform designers deep insights into how temporal regularities, rather than behavioral content alone, reflect learning persistence.

ABSTRACT. Virtual Reality Learning Environments (VRLEs) offer strong potential for inquiry-based science learning, yet the temporal dynamics of learner interaction, such as behavioral onset, duration, and evolving transition structures, remain underexplored. This exploratory study analyzes interaction traces of learners engaging with a VR module on Electromagnetic Induction (EMI), comparing high-performing (HP) and low-performing (LP) groups (n=10 per group, post-test tertiles). Raincloud plots and Transition Network Analysis (TNA) with five successive 200-second sliding windows reveal distinct temporal signatures. HP learners exhibited efficient onboarding, early establishment of a tightly coupled Inquiry Diamond loop (parameter manipulation → positioning → experimentation → evaluation), and evaluation-led task closure. LP learners showed prolonged onboarding with premature evaluation, and persistent experimentation without convergence (wheel-spinning). A complete temporal inversion in dominant behaviors across temporal phases is identified between HP and LP. These findings suggest that performance in VRLEs is driven less by time-on-task and more by the structural systematicity of inquiry processes, with implications for precisely timed adaptive scaffolding that targets the right intervention at the right moment in each learner's session trajectory.

ABSTRACT. The computer mouse is a ubiquitous human-computer interaction device that requires delicate and fine-grained motor control. Explicit training of mouse motor skills could improve the efficiency and effectiveness of tasks performed with a computer. To empower the next generation of mouse tutoring systems, we need reliable computational models that can help a system distinguish between user behavior, task features, and noise. We re-implemented a state-of-the-art model, the DisMouse disentanglement framework, on the Balabit Mouse Dynamics Challenge Dataset, and evaluated whether disentangled representations can support test-taker identity signatures and integrity-risk flagging from interaction logs. We observed that the model’s identity space forms clear user-specific clusters, while the model’s dynamics space remains largely user-agnostic. We then aggregated window-level representations into session-level signatures, and applied clustering methods to flag behaviorally aberrant sessions with 71-73% accuracy. Our results suggest that disentangled identity embeddings from mouse movement are interpretable and can serve as a practical signal for behavior detection.

ABSTRACT. This study explores the relationship between generative AI (GenAI) literacy and Technology Acceptance Model (TAM) constructs among English as a Foreign Language (EFL) learners using a sequential explanatory mixed-methods design. In the quantitative phase, survey data were collected from 225 university students in Japan and analyzed using partial least squares structural equation modeling (PLS-SEM) and importance–performance map analysis (IPMA). In the qualitative phase, semi-structured interviews were conducted with six participants to provide in-depth insights into the quantitative findings. The results revealed that GenAI literacy had significant indirect effects on attitude, behavioral intention, and actual use, with perceived usefulness serving as the primary mediator. Among the GenAI literacy components, perceived benefits emerged as the most influential predictor of behavioral intention, whereas ethical awareness showed high performance but limited impact. Qualitative findings further demonstrated that although learners recognized the benefits of GenAI, they used it selectively as a supplementary tool while maintaining critical and ethical awareness. In addition, learners’ behavioral intention was found to be multi-layered, involving decisions not only about whether to use GenAI, but also when, where, and how to use it. These findings suggest that learners’ acceptance of GenAI is more complex than assumed in traditional TAM. The study highlights the need to extend TAM by incorporating multi-layered attitudes, behavioral intention, and GenAI literacy to better capture learners’ engagement with GenAI.

ABSTRACT. Spherical video-based virtual reality (SVVR) has attracted increasing attention in second or foreign language (FL) education, but the extent of its pedagogical integration remains unclear. This study reports a systematic review of 23 empirical studies on SVVR in FL education retrieved from the Web of Science Core Collection in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. Drawing on the Substitution, Augmentation, Modification, and Redefinition (SAMR) model, the review examines both the research profile of the field and the levels at which SVVR has been integrated into pedagogical practice. The results show that SVVR research has expanded notably since 2020, with most studies conducted in East Asia and in higher education contexts. Mixed-methods and quasi-experimental designs were predominant, and speaking was the most frequently targeted language skill. In terms of SAMR classification, most studies were situated at the Augmentation and Modification levels, fewer reached Redefinition, and only one was classified as Substitution. These findings indicate that SVVR is commonly used to enhance or redesign language learning tasks, but more limited evidence exists for fully transformative uses. The study contributes an up-to-date synthesis of SVVR research in FL education and provides implications for the design of more pedagogically meaningful immersive learning activities.

ABSTRACT. The assumption that generations born in the twenty-first century naturally possess digital literacies remains contested. This study is driven by the continuing disagreement observed in empirical studies on whether students need to be digitally instructed. Via the lens of the Dunning-Kruger effect, the current study explores the disparities between self-perceived and tested digital literacies among English major students in the Chinese higher education sector. 227 students participated in one self-reported questionnaire and an actual test regarding digital competence. Grouping by quartile in test results, discrepancies were observed by comparing the standardised values of self-reported digital literacies with the scores in the tested digital literacies. The ANOVA analysis indicates a significant Dunning-Kruger effect (p < .001, η² = .416), with the low-score group (Quartile 1) overestimating their digital competence (M = 1.17, SD = 1.15), and the high-score group (Quartile 4) underestimating it (M = -1.24, SD = 0.92). The Tukey post-hoc analysis supports significant differences between all groups in pairs. It is suggested that systematic misperception can be found in EFL students’ digital literacies, challenging the meaning of digital natives. This implies that digital literacies education needs to pay attention to self-recalibration in students.

ABSTRACT. This study investigated how dashboard-based reading-speed feedback influences self-monitoring during time-aware reading practice for university entrance examination preparation. Effective time management is an important component of reading performance in examination settings; however, learners often receive limited support for monitoring their reading pace. To address this issue, a Words Per Minute (WPM) dashboard was developed as part of a Time-Aware Reading Practice (TARP) framework designed to support both reading practice and time management. Dashboard logs and reading performance data from Japanese high school English classes were analyzed. The results show that the students actively engaged in self-monitoring, accessing the dashboard a median of nine times across ten reading tasks. Even when the timer was hidden, 34% of students accessed the dashboard, indicating heightened awareness of reading pace. Differences in self-monitoring behavior were observed across learner groups, suggesting that learners engaged with dashboard feedback in different ways depending on their reading proficiency. These findings suggest that temporal feedback combined with social comparison information can encourage reflective reading behaviors and promote time-aware learning strategies in examination-oriented contexts. The study contributes to the field of learning analytics (LA) by demonstrating how an LA-supported TARP framework can facilitate both reading fluency development and metacognitive regulation. The results also highlight the potential of dashboard-based feedback to support learners with different levels of reading proficiency through adaptive self-monitoring opportunities.

ABSTRACT. Tablet-based augmented reality (AR) provides a new pathway for developing resources that support authentic problem-based inquiry in primary science classrooms. To address the difficulty of sustaining variable manipulation, evidence collection, and collaborative explanation in conventional classrooms, this study adopted a design case approach and developed a set of tablet-based AR resources for primary science inquiry, using the Grade 5 topic of “simple machines” as an example. Drawing on scientific inquiry, situated learning, and multimedia learning theories, the resources were designed around four consecutive components: problem, operation, feedback, and explanation. Three scenarios, namely the inclined plane, lever, and wheel and axle, were developed through a mapping relationship among authentic problems, scientific variables, AR interactions, feedback evidence, and explanation tasks. The resources were implemented using Unity and Vuforia, with marker cards used to trigger tabletop AR scenarios. Students could manipulate variables in groups, observe visual and numerical feedback, and construct explanations. Expert and teacher evaluations, a student technology acceptance pre-experiment, and a formative classroom trial indicated that the resources were feasible for use in primary science classrooms. They also revealed key design issues, including variable visibility, operation-feedback coupling, recognition stability, and device sharing. This study further extracts preliminary design principles for tablet-based AR science inquiry resources, providing transferable design references for the development of mobile AR resources oriented toward authentic problem contexts.

ABSTRACT. Using behavioral logs from 313 Japanese high school students over an eight-month period in mobile learning across mathematics, chemistry, Japanese, English, and physics, this study examines learning efficiency variations by subject. Linear mixed-effects models show that optimal study times are subject-dependent rather than universal. Session duration reveals a U-shaped curve for all subjects—where efficiency initially drops before recovering in longer sessions. The effect of subject sequencing is domain specific. While switching into English from a different subject helps maintain efficiency, repeated engagement in English undermines efficiency. These findings offer empirical parameters for optimizing study schedules tailored to subject-specific cognitive demands and temporal rhythms, informing adaptive learning systems and self-regulated learning strategies to enhance learning efficiency and academic outcomes in mobile learning contexts. Limitations include missing psychological variables and fixed time-slot categorization, suggesting future research should integrate mixed methods and data-driven temporal segmentation. Overall, this study advances the understanding of multi-subject learning dynamics in ubiquitous environments, providing actionable insights for educators, learners, and developers to foster effective time management in K-12 education.

ABSTRACT. Compared with conventional classroom settings, outdoor learning environments are typically more open and less structured, requiring students to engage more actively in planning, monitoring, and regulating their own learning. Despite growing evidence that digital outdoor learning can promote self-directed learning (SDL), the mechanisms underlying this effect remain underexplored. This study investigated how technology-supported outdoor learning shapes high-school students’ SDL in the Citizenship and Social Development (CSD) curriculum by examining the relationships among cognitive load, motivation, learning engagement, self-efficacy, and SDL. Data were collected through an SDL questionnaire-based instrument from 396 Hong Kong senior secondary students after a two-hour CSD-related outdoor learning activity supported by an outdoor learning platform, EduVenture. The results showed that motivation, learning engagement, and self-efficacy significantly predicted SDL. Cognitive load did not directly affect SDL; however, it indirectly influenced SDL through motivation, learning engagement, and self-efficacy. Furthermore, cognitive load significantly moderated the relationship between motivation and learning engagement, suggesting that excessive cognitive load may weaken the positive effect of motivation on learning engagement. These findings suggest that effective outdoor learning design should balance motivational support with manageable cognitive demands. Well-designed scaffolding strategies, such as staged task release, context-aware prompts, and embedded reflection cues, may help maintain optimal cognitive load while strengthening students’ learning engagement, self-efficacy, and SDL capability. The study contributes to a deeper theoretical understanding of SDL in technology-supported outdoor learning and offers practical implications for the design of digitally mediated experiential learning in citizenship education.

ABSTRACT. We investigate the impact of LLM-generated feedback on first-year students’ programming activities by analyzing interaction logs from the Run-Code platform. We collected log data from 37 students across 22 programming exercises. Data were collected asynchronously over five weeks as part of an introductory programming course on Java in a university in the Philippines. In this preliminary analysis of the data, we attempted to answer two research questions: What was the relationship between students’ feedback usage and the student performance on the next problem? What was the relationship between students’ use of feedback and future programming performance? Our results show that feedback usage was not a statistically significant predictor of next-task performance, next-task error count, or future mean task scores, but it was a statistically significant positive predictor of future mean error count. These findings suggest that feedback access alone does not translate into measurable improvements in programming performance, and the positive association with future error counts likely reflects underlying learner difficulty rather than a detrimental effect of feedback.

ABSTRACT. Digital mathematics assessments provide screen-level process data that capture how students regulate attention and coordinate information across functionally distinct task screens during mathematical problem solving. Using TIMSS 2023 Grade 4 digital mathematics data from nine countries, this study conceptualized screen-level time allocation as an observable indicator of representational coordination within mathematics task blocks. The analytic sample included 84,811 student-by-block records from 42,528 students. Screens within each block were classified into primary cognitive screens and auxiliary representation screens. Cognitive–representation allocation was operationalized as the proportion of eligible block time spent on auxiliary representation screens. The study examined overall allocation patterns, compared linear and quadratic allocation models for two performance outcomes, and tested whether first-pass processing and revisiting moderated these associations. Students spent most block time on primary cognitive screens, while auxiliary representation screens accounted for a smaller but meaningful share. Quadratic models fit better than linear models for both outcomes, indicating an inverted-U association between allocation and performance. Predicted performance was highest when students maintained a moderate balance between primary task processing and auxiliary representation use. Moderation evidence was limited and mainly appeared for revisit burden in positive-score performance. Heavier revisiting made higher auxiliary-screen allocation less favorable among positive-score observations. These findings suggest that screen-level time allocation reflects not only how long students spend on tasks, but also how they coordinate functionally distinct representations during digital mathematical problem solving.

ABSTRACT. In a tertiary-level Research Methods in Computing course, students perform cognitive tasks, primarily literature review and synthesis, to formulate research questions (RQs) that are feasible and aligned with identified gaps. The iterative development of these RQs necessitates continuous pedagogical guidance to maintain alignment with the eventual research scope and design. The fluent linguistic and reasoning abilities of Large Language Models (LLMs) make them prime candidates to assist students in this process. This paper describes a study in designing learning activities that explicitly instruct students to employ Generative AI (GenAI) in refining their RQs and overall proposal document while mandating a formal declaration of AI usage. Findings from a mixed methods survey and analysis of student artifacts reveal critical gaps in AI literacy, specifically regarding how students interpret AI feedback and integrate it in refining their work. While students utilized GenAI for various research tasks, their self-assessments indicated disparity in AI usage collaborative practices, perceived reliance, and awareness of academic policies. Findings from this study suggest a two-pronged approach to AI literacy: (1) empowering educators to leverage LLMs for immediate, scalable feedback on student work, and (2) training learners to apply critical thinking when evaluating AI-generated output. The paper concludes with pedagogical insights and recommendations for educators seeking to position LLMs as a pedagogical agent, specifically critic and collaborator, rather than a mere writing tool within the research process.

ABSTRACT. Input device proficiency may act as a hidden barrier in data literacy education, where students struggle with pointer operations rather than statistical content. A prior study (N = 11) found that mouse operation time was significantly correlated with Excel assessment scores (r = −.692, p = .018). The present study compared mouse and touchpad use by analyzing EEG-based cognitive load and pointer trajectory data from the same experiment (N = 10). Theta/alpha ratio was used as a cognitive load index. Pause count and pause time were extracted as hesitation indicators. Touchpad operations produced significantly more pauses than mouse operations across all six tasks (Holm-corrected p = .030, Cliff's d = 0.64–1.00), with the largest differences observed in CopyPaste and Drag tasks. Pause count was correlated with Excel scores in both conditions, suggesting hesitation affects scores regardless of device. However, EEG theta/alpha ratio was associated with Excel scores only in the touchpad condition (rs = −.690, p = .027), not in the mouse condition. These findings suggest that lack of input device proficiency may impose cognitive demand that affects learning outcomes. Implications for instructional design are discussed.