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. Since generative AI is being more widely used to support inquiry, feedback seeking, content generation, and reflection, and since these functions can aid students in handling open-ended tasks in project-based learning (PBL), it is also important to recognize that unguided use of such tools may lead to over reliance. Therefore, this action research study investigates how generative AI can be designed as role-based scaffolding in a high school information technology course. The intervention grouped PBL into four clearly defined phases: project formulation, inquiry integration, creation optimization, and output transfer, and placed generative AI appropriately in each phase as a question assistant, learning partner, design assistant, and reflection partner, respectively. Three action research cycles were carried out with 37 students, and data were collected using a deep learning ability scale. A Depth of Knowledge (DOK)-based cognitive analysis, classroom observations, project artifact evaluations, and interviews were used in this study to investigate students' use of generative AI, revealing clearly that students' use shifted from answer seeking to verification-oriented and reflection-oriented use, and that students made positive gains in critical thinking, creative thinking, self-directed learning, and learning perseverance. More importantly, DOK results showed a definite move from recall and application toward strategic thinking and extended thinking. Thus, the study convincingly argues that generative AI can support deep learning in PBL when its use is mediated by teachers, embedded in task-specific roles, and paired with a critical verification cycle.

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. Computational thinking is an important competence in K-12 information technology education, but its development should not be limited to programming syntax or isolated algorithm instruction. This action research study examined how robotics-supported interdisciplinary collaborative learning could support sixth-grade students’ computational thinking development in a primary school information technology course.

Guided by the 6E learning process, three rounds of classroom activities were designed and implemented: Rock-Paper-Scissors Robot, Fun Helicopter Propeller, and Smart Sunshade. Robot kits, sensors, flowcharts, and block-based programs were used as shared artifacts to support students’ collaborative inquiry, programming, debugging, and reflection. Data were collected through a computational thinking scale, Bebras tasks, classroom observations, student artifact evaluations, and interviews.

The findings showed positive development in five dimensions of computational thinking: decomposition, abstraction, modeling, algorithm design, and evaluation. Classroom observations and artifact evaluations further indicated that students gradually moved from simply completing robot tasks to explaining rules, negotiating program logic, debugging systems, and optimizing solutions around shared artifacts. The study suggests that robotics-supported collaborative learning can make computational thinking more visible, discussable, and revisable in primary information technology classrooms. It also provides design implications for integrating computational thinking development with computer-supported collaborative learning through structured interdisciplinary activities.

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. Recent studies advocate integrating data-driven learning (DDL) and generative AI (GenAI) tools as complementary approaches to addressing EFL learners' challenges in argumentative writing; however, empirical evidence on learner engagement with such integrated platforms remains limited. This study investigated how 31 EFL university students engaged with an AI-powered corpus platform during argumentative writing. Students' behavioural, cognitive, and affective engagement was captured through screen recordings and post-task surveys. Lag Sequential Analysis revealed distinct behavioural sequencing patterns, and cluster analysis identified four engagement profiles: Active Engagers, Efficient Strategists, Self-assured Reviewers, and Reserved Users. The findings offer implications for the pedagogical design of integrated AI–corpus platforms.

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 study applies conjecture mapping to pedagogist competency development in early childhood education (ECE). Through qualitative interviews with three educational consultants at a multi-site ECE organization in Ontario, Canada, and systematic coding of 160 segments across 20 codes, three iterative conjecture maps are developed. Findings identify six design principles, three theoretical conjectures, and four design constraints governing pedagogist competency. The most consequential unanticipated mediating variable, the artist-inquiry disposition, is identified as a high-leverage competency not captured in any existing ECE framework. Implications for pedagogist professional development and self-assessment tool design are discussed.

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. While large language models (LLMs) are increasingly utilized for automated grading, their performance remains highly sensitive to task type, metric selection, and prompt architecture. We address the technical bottleneck of inconsistent STEM assessment by evaluating numerical scoring across three disciplines (physics, chemistry, and mathematics) using five widely-used LLM models (GPT-4o, Gemini 2.0 Flash, Claude 4.0, DeepSeek V2.5, and Grok 4) under two distinct prompting regimes: rubric-plus-exemplar and exemplar-only. Our analysis reveals that Claude 4.0 (without a rubric) excels in physics and chemistry, whereas Grok 4 (with a rubric) leads in mathematics. Crucially, exemplar-only prompting consistently yields higher exact-match rates and greater distributional overlap than rubric-inclusive prompts, suggesting a "context saturation" threshold for complex rubrics. Although subject-specific ensembles provide marginal (+1.5%) gains, Grok 4 is the sole model to significantly reduce AI–human bias below the human–human baseline. Ultimately, these findings establish an empirical foundation for deploying LLMs as decision-support tools in STEM education and offer actionable guidelines for prompt optimization.

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. This study integrated an in-house AI writing system with the 8P theoretical framework to develop an effective instructional approach aimed at enhancing college students’ creativity and English story writing proficiency. 32 college students participated in this study. Data were collected using pre- and post-creativity scales and English story-writing tests. The results demonstrated significant improvements in students’ creativity and substantial improvement in English story-writing proficiency. The 8P instructional approach in AI-supported learning provided clear learning goals and structured environmental demands that encouraged the students to apply their creativity in completing original digital picture books. During mental operations for producing stories in English, students received AI support that helped them brainstorm ideas, select appropriate words, check for grammatical errors, and refine story structure. These findings suggest that integrating the 8P instructional approach with AI-supported features can effectively foster college students’ creativity and enhance their writing proficiency.

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. 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. The role of smartphones in schools has been widely debated in recent years, and this study foregrounds students’ voices on how smartphones shape learning ecologies in both classroom and self-directed study contexts. Data were collected from 57 students in two Finnish lower secondary schools through open-ended questionnaires. Drawing on Laclau and Mouffe’s discourse theory, we examined how meanings around smartphone use are constructed through articulation. The findings showed that smartphones were used both as teacher directed tools and as flexible, student driven resources. According to students, phone use was mainly guided by teachers for information retrieval and quiz based activities, while students also independently used their phones for a wider range of purposes, including watching instructional videos, learning via social media, using AI for explanations, and handling practical tasks such as note taking. At the same time, students described several tensions related to smartphone use, including the dual role of phones as both learning resources and sources of distraction, the trade off between fast information access and its perceived unreliability, and mismatches between students’ practices and teachers’ rules. This suggests a need for clearer classroom practices that encourage more purposeful smartphone use while ensuring it supports, rather than disrupts, learning.

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. 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. AI coding tools can help young learners create software from natural-language prompts, but their dominant interaction pattern often optimizes for code production rather than learner-owned progression. This short paper argues that, for K-12 creative programming, the central design problem is how to preserve the motivation of a personally meaningful project while turning that project into a learning process. Drawing on constructionist computing education, non-formal K--12 computing, self-determination theory, scaffolding, cognitive load theory, metacognition, and emerging work on AI-supported programming, we propose a Goal-to-Milestone framework. The framework treats backward path planning and the next bounded milestone as the primary units of AI support. After a learner names an idea, the learner and AI co-construct a project flowchart, make design judgments, choose an accessible first milestone, articulate done criteria, sketch logic, build, preview, debug from observed behavior, receive a mini-explanation, and select the next milestone. We do not report empirical outcomes; instead, we offer a literature-grounded position, two compressed design scenarios, and a conjecture map for future research.

ABSTRACT. Generative AI music tools (e.g., Suno, Udio) have significantly lowered technical barriers to music creation, stimulating broad societal discourse on “technological democratization.” This study analyzes 15,406 valid danmaku (bullet-screen comments) collected from Bilibili to investigate public cognitive trajectories and their implications for music aesthetic education. Employing NLP, K-Means clustering, and temporal sentiment evolution modeling, we identify four key findings: (1) discourse exhibits a markedly uneven four-dimensional structure — technology spectacle dominates (54.4%), followed by evaluation of specific musical elements (33.3%), with ethical controversy (6.3%) and traditional aesthetic resistance (6.0%) at the margins; (2) public sentiment follows a three-stage logic spanning a latent period (2017–2023), a Suno-driven explosive growth period (2024), and a differentiation period marked by aesthetic reflection and ethical anxiety (2025–2026); (3) a fundamental paradox exists within democratization discourse — tool accessibility has not eliminated the knowledge gap in aesthetic evaluation, revealing a decoupling of technical access from aesthetic democratization; (4) platform interactions have spontaneously formed a “danmaku shadow classroom” facilitating informal music knowledge transmission. Based on these findings, we argue that contemporary music aesthetic education urgently requires repositioning: its core should shift from traditional “creative practice” toward cultivating “critical listening” and “aesthetic metacognition,” while formally integrating cross-media aesthetics and AI ethics into educational frameworks.

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. 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. 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 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 implementation and advancement of China's digitalization strategy, the digital transformation of higher education is becoming an important driving force for urban-rural integration and development. Based on the panel data of 31 provinces in China from 2011 to 2023, this study constructs an econometric model to empirically examine the impact of the digital transformation of higher education on urban-rural integration and development, and depicts its spatiotemporal evolution characteristics. The research findings are as follows: (1) The digital transformation of higher education and the level of urban-rural integration and development in China are on the rise, but the issue of spatial injustice and imbalance has become increasingly prominent; (2) The digital transformation of higher education significantly promotes urban-rural integration and development, with the shift in teaching and research, as well as human modernization, being of primary importance. Digital support and scale demonstrate weak effects due to not touching the core; (3) The promoting effect of the digital transformation of higher education exhibits heterogeneity in time and space. On the one hand, its benefits rely on the improvement of infrastructure and macro policy guidance, and are influenced by the interplay of major public events and economic and social fluctuations. On the other hand, the connotative drive of high-end talent and industrial governance in the eastern region has become an ecological reconstruction force. The digital infrastructure and talent release in the central region yield higher marginal benefits but are limited by the gap in local innovation capability and achievement transformation efficiency. The western region, relying on external resources and policy tilting, has achieved significant results but faces the challenge of fragile endogenous systems. Therefore, suggestions are proposed from the dimensions of strengthening overall ecological investment in the digital development of higher education, promoting different digital strategic actions according to local conditions, establishing relevant policy guarantees and monitoring and evaluation.

ABSTRACT. Drawing on basic psychological needs theory (BPNT: competence, autonomy, relatedness), we explored 20 EFL undergraduates’ need satisfaction when using an AI‑powered corpus‑based writing platform (AI‑Corpus Writing). Using Q methodology, analysis of 20 Q‑sorts revealed four distinct basic psychological needs (BPNs) satisfaction profiles: (1) cautious users preserve autonomy by directing AI suggestions rather than being directed; (2) relational resistors satisfy competence and relatedness almost exclusively through human feedback, rejecting AI’s social utility; (3) pessimistic dependents exhibit a triple BPN deficit (low autonomy, low competence, absent relatedness), passively copying AI outputs; (4) skill‑empowered optimists reframe AI proficiency as a higher‑order competence, maintaining autonomy by filtering suggestions. Across all profiles, learners agreed that AI‑corpus suggestions are reliable and expand expression, but excessive suggestions overload decision‑making, directly threatening autonomy. We conclude that psychologically calibrated AI writing tools must limit suggestion overload, preserve learner agency, and leave room for human feedback, or risk widening BPN‑based learning disparities. The findings are expected to inform pedagogical practices and designs for building psychologically supportive AI-supported writing 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. 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. 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. 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. Collaborative learning is a complex socio-emotional process. A lack of effective regulation mechanisms often leads to collaboration failure; therefore, Socially Shared Regulation of Learning has become a frontier of research in the learning sciences. This study adopted a design-based research methodology, aiming to explore how to support shared emotion regulation in collaborative learning through the design of specific emotion scaffolds. The results indicate that emotion regulation scaffolds can effectively promote the teams’ socially shared regulation. Furthermore, content analysis reveals that the frequency of socio-emotional behaviors exhibit differences across the various collaboration stages. This study confirms that emotion scaffolds effectively maintain a positive collaborative atmosphere, reduce task uncertainty, and improve collaboration efficiency. These findings provide a theoretical and practical foundation for designing emotional interventions in future CSCL environments.

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. 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. 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. 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. 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. Self-regulated learning (SRL) is critical for translation performance, yet traditional training struggles with individual and contextual factors. While generative AI (GenAI) offers personalized feedback potential, concerns include AI hallucination, unknown training data sources, and uncritical student acceptance. AI-supported SRL has focused on reading and writing, leaving translation underexplored. This study examined how translation students' SRL skills and engagement changed using AI-Corpora, a platform integrating GenAI with corpora. Findings revealed different trajectories: forethought improved substantially early. Performance and reflection showed delayed but significant gains. Three dimensions of engagement progressed steadily. The study highlights GenAI-corpora integration's potential for fostering SRL and engagement while underscoring the need to support critical thinking and prevent over-reliance.

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. Inclusion in technology-enhanced learning is often framed in terms of access, accessibility, or the provision of digital tools. These concerns remain essential, but they are insufficient in learning environments shaped by platforms, analytics, adaptive systems, and artificial intelligence. Learners may be able to enter a digital system while still being unable to participate meaningfully, benefit fairly, or challenge data-driven decisions that affect their learning. This paper develops a conceptual framework for analyzing inclusion in intelligent technology-mediated learning ecologies. Drawing on a conceptual review informed by critical interpretive synthesis, it synthesizes research on accessibility, Universal Design for Learning, digital equity, online and hybrid learning, AI in education, and educational data governance. The review identifies four shifts: from accessibility compliance to inclusive design, from technical access to meaningful participation, from technology provision to digital equity and fair outcomes, and from human-centered design to participatory socio-technical governance. Building on these shifts, the paper proposes the Access-Participation-Equity-Agency (APEA) framework. APEA conceptualizes inclusion across four connected ecological layers: material conditions, pedagogical interaction, social distribution, and governance. An illustrative case of generative AI writing tools shows how the same technology can expand access and participation while also creating new equity and agency risks. The paper argues that inclusion in AI-mediated learning should not be reduced to access, usability, or personalization alone. It must also involve meaningful participation, fair distribution of benefits and risks, and the capacity of learners and educators to understand, influence, refuse, adapt to, or contest technology-mediated arrangements. The framework is particularly relevant to Asia-Pacific and ICCE-related contexts, where multilingual learning, uneven infrastructure, and differences in institutional capacity shape the adoption of intelligent technologies.

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.