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Dongkeun Han

Publications and source records attributed to Dongkeun Han.

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Report from Workshop on Dialogue alongside Artificial Intelligence

Educational dialogue -- the collaborative exchange of ideas through talk -- is widely recognized as a catalyst for deeper learning and critical thinking in and across contexts. At the same time, artificial intelligence (AI) has rapidly emerged as a powerful force in education, with the potential to address major challenges, personalize learning, and innovate teaching practices. However, these advances come with significant risks: rapid AI development can undermine human agency, exacerbate inequities, and outpace our capacity to guide its use with sound policy. Human learning presupposes cognitive efforts and social interaction (dialogues). In response to this evolving landscape, an international workshop titled "Educational Dialogue: Moving Thinking Forward" convened 19 leading researchers from 11 countries in Cambridge (September 1-3, 2025) to examine the intersection of AI and educational dialogue. This AI-focused strand of the workshop centered on three critical questions: (1) When is AI truly useful in education, and when might it merely replace human effort at the expense of learning? (2) Under what conditions can AI use lead to better dialogic teaching and learning? (3) Does the AI-human partnership risk outpacing and displacing human educational work, and what are the implications? These questions framed two days of presentations and structured dialogue among participants.

cs.CY

Exploring Effective Strategies for Building a User-Configured GPT for Coding Classroom Dialogues

This study investigated effective strategies for developing a custom GPT to code classroom dialogue. While classroom dialogue is widely recognised as a crucial element of education, its analysis remains challenging due to the need for a nuanced understanding of dialogic functions and the labour-intensive nature of manual transcript coding. Recent advancements in large language models (LLMs) offer promising avenues for automating this process. However, existing studies predominantly focus on training large-scale models or evaluating pre-trained models with fixed codebooks, the outcomes of which are often not applicable, or the methods are not replicable for dialogue researchers working with small datasets or employing customised coding schemes. Using MyGPT - a GPT-4-based customised GPT system configured for dialogue analysis - as a case, this study evaluates its baseline performance in coding classroom dialogue with a human codebook and examines how performance varies with different example inputs under a controlled variable design. Through a design-based research approach, this study explores a set of practical strategies - based upon MyGPT's unique features - for configuring an effective tool with limited data. The findings suggest that, despite a few limitations, a custom GPT developed using these specific strategies can serve as a useful coding assistant by generating coding suggestions.

cs.AI