Guest Editors

• Wanli Xing, University of Florida, USA
• Andrew Lan, University of Massachusetts- Amherst, USA
• Scott Crossley, Vanderbilt University, USA
• Zhou Yu, Columbia University, USA
• Paul Denny, University of Auckland, New Zealand
• Nia Nixon, UC-Irvine, USA
• John Stamper, Carnegie Mellon University, USA

Background

Large language models (LLMs) are based on deep neural networks and often engineered with transformer architectures. They are built with hundreds of millions and even billions of parameters and pre-trained with large quantities of language data. LLMs have made significant strides in recent years on a wide range of natural language processing (NLP) tasks including language generation, summarization, comprehension, and classification (Brown et al., 2020). Recent LLMs, such as GPT-4 (OpenAI, 2023) and LLaMA (Touvron et al., 2023), have demonstrated a remarkable ability to understand and generate human-like text either with proprietary API services or in open-sourced approaches, making them valuable tools for a variety of applications, including education. Since LLMs demonstrate transferability through inheriting semantic and contextual understanding ability from pretraining, it fits well in the context of learning engineering and learning analytics (Baker, 2023) because they provide reusable and scalable technical architectures in various subjects (e.g., math, Scarlatos & Lan, 2023, Shen et al., 2021; science, Cooper, 2023; medicine, Luo et al., 2022). Early integrations of LLMs into educational settings have demonstrated promising results to augment learning through item response and student knowledge tracing models for open-ended questions (Liu et al., 2022), socio-emotional support (Li & Xing, 2021), automatically generating educational content (Sarsa et al., 2022), especially questions (Wang et al., 2021), and automatic contextual feedback (see the review of Hahn et al., 2021). The potential extension of LLMs to process multimodal data further empowers researchers and practitioners to support students’ learning with various data sources and formats. 

Despite the promise of LLMs in education, there is still a need to explore their potential impact, limitations, and ethical considerations. For example, little is known empirically about the learning experience design of LLM-enabled educational applications and their impacts on students’ motivation, engagement, self-efficacy, and learning outcomes. Additionally, LLMs have been predominantly trained with English and adult texts, while relatively less non-English and K-12 data has been involved in LLM development, potentially leading to equality and equity issues in education (Abid et al., 2021; Ariely et al., 2022; Kasneci et al., 2023). Finally, there are ethical concerns (e.g., factuality, safety, fairness, and transparency) in LLMs, resulting in uncertainties in building sustainable and trustworthy AI systems in education (Kasneci et al., 2023; Li et al., 2022). This special issue aims to collect, review, and publish research that investigates the use of LLMs in educational contexts, addresses the challenges and opportunities associated with their deployment, and furthers our understanding of how LLMs might change the nature of teaching and learning (e.g., forms of assessment, computing education).

Rationale, Motivation, and Scope of the Special Issue

In order to advance our understanding of the role, technicality, and ethics of LLMs in education, IJAIED is pleased to announce a special issue on “Use of Large Language Models in Education.” The rationale of this special issue is to bring together cutting-edge research that explores the technical extensions of LLMs in AIED, investigates the design and development of LLM-powered implementations in educational settings, highlights the challenges and opportunities associated with their use, and provides insights into how LLMs can be effectively integrated into educational practices and how and under which conditions they might change educational practices, perhaps fundamentally so. We welcome contributions that align with the aims and scope of IJAIED, focus on the use of LLMs in education, and provide evidence of their impact on teaching and learning. 

We are particularly interested in research that addresses the following questions: 

• How can LLMs effectively facilitate high-quality educational content creation, organization, or dissemination?
• What are the implications of using LLMs for personalizing learning experiences and promoting learner engagement?
• What can prior research on technology effectiveness in education inform the effective use of LLMs?
• How can LLMs be used to improve experimental research in the AIED (e.g., ChatGPT as a teammate in collaborative problem-solving experiments)?
• How can LLMs improve assessment and evaluation processes in education?
• In what ways can LLMs foster creativity, innovation, and collaboration among learners?
• In what ways and under what circumstances might the use of LLMs transform learning and teaching?
• What are the ethical considerations and potential risks associated with the use of LLMs in education, and how can they be mitigated?
• What are the real-world obstacles and hindrances to the deployment of LLMs in educational environments, and what strategies can be employed to address and surmount them?
• What approaches can be taken to establish policies and frameworks that promote the conscientious and ethical application of LLMs within the educational context?

We also welcome systematic literature review papers that provide a comprehensive overview of the current state of research on LLMs in education and offer insights into future research directions.

Additional Details of the Topics

For this special issue, we consider all AI methods on-topic if they involve the use of LLMs to support education and discuss educational implications. Examples include (but are not limited to):

• Personalized feedback and assessment using LLMs
• Generation, summarization, or adaptation of educational content and learning materials using LLMs
• Interactive learning experiences facilitated by LLMs (e.g., educational chatbots, conversational agents)
• Ethical considerations and potential biases in the use of LLMs in education
• Privacy and security concerns associated with the use of LLMs in educational settings
• The impact of LLMs on student engagement, motivation, and learning outcomes
• The role of LLMs in remote and online learning environments
• Applications of LLMs for domain modeling and knowledge extraction
• Recommender systems using LLMs for educational resources and personalized learning pathways
• Monitoring and supporting student well-being using LLMs
• Addressing language and cultural diversity in education using LLMs

We encourage submissions that provide empirical evidence of the impact of LLMs on education and that engage with the challenges and opportunities associated with their use. Prospective authors are encouraged to contact the guest editors if they have any questions about the suitability of their work for this special issue.

Timing & Process:

• Authors submit extended abstracts (1000 words) to EasyChair: Jul. 15, 2023
• Editorial decision on full paper invitation: Aug. 1, 2023
• Authors of accepted extended abstracts submit full paper: Oct. 1, 2023
• 1st review cycle and editorial decision (revision/rejection): Dec. 1 2023
• Authors submit revised manuscript: Jan. 15, 2024
• Final editorial decision (acceptance/rejection): Mar. 1, 2024
• Publication of the special issue: 2024

Submission Guidelines

Please submit your abstract via EasyChair. If invited, you will submit your full manuscript through IJAIED Editorial Manager: https://www.editorialmanager.com/aied following the instruction here (under Submission Guidelines).

For a full reference of the CFP, please find it at https://www.springer.com/journal/40593/updates/25372572