How Can Large Language Models Support Learning?

We discuss a novel approach to Premodern Japanese Language Pedagogy (PJLP) with potential applications in other languages and fields. Drawing inspiration from grassroots initiatives such as the DIY and Maker movements, this paper advocates for an approach to AI in Education that fosters instructional and student agency over the pedagogical process. Specifically, we discuss a tutoring framework for textual analysis in the context of a graduate seminar in premodern Japanese literature, as well as a bilingual interactive dictionary and a conversational partner created for a language course in Classical Japanese. Created through prompt engineering as custom instances of a Large Language Model (LLM), these three tools are designed to counteract the tendency of out-of-the-box LLMs to either bypass student effort through over-explanation or misguide learners via hallucinations. To illustrate how this approach can promote active comprehension and pedagogical alignment, we provide transcripts (logs) of actual exchanges, sample instructions (system prompts), and guidance for instructors curious about exploring this approach in a variety of fields (starter kit).

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Publication Details

Journal
Japanese Language and Literature
Published
2026-10-29
DOI
https://doi.org/10.5195/jll.2026.480
Primary Topic
Artificial Intelligence in Education
Type
article
Field-Weighted Citation Impact
0.00
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article

How Can Large Language Models Support Learning?

Pelin Çılgın, Caleb Langley, Gavin Sherry, Ariel Stilerman et al.
3 links
Japanese Language and Literature
Artificial Intelligence in Education
article

How Can Large Language Models Support Learning?

Pelin Çılgın, Caleb Langley, Gavin Sherry, Ariel Stilerman, Sera Wang, Teisha Nishimitsu, Camilla Piana, Josh Eyre, Qianhe Qin, Andrew Nelson, Liaoliao Zhang, Huiting Liu, Alan Cheng
article en

Abstract

We discuss a novel approach to Premodern Japanese Language Pedagogy (PJLP) with potential applications in other languages and fields. Drawing inspiration from grassroots initiatives such as the DIY and Maker movements, this paper advocates for an approach to AI in Education that fosters instructional and student agency over the pedagogical process. Specifically, we discuss a tutoring framework for textual analysis in the context of a graduate seminar in premodern Japanese literature, as well as a bilingual interactive dictionary and a conversational partner created for a language course in Classical Japanese. Created through prompt engineering as custom instances of a Large Language Model (LLM), these three tools are designed to counteract the tendency of out-of-the-box LLMs to either bypass student effort through over-explanation or misguide learners via hallucinations. To illustrate how this approach can promote active comprehension and pedagogical alignment, we provide transcripts (logs) of actual exchanges, sample instructions (system prompts), and guidance for instructors curious about exploring this approach in a variety of fields (starter kit).

Japanese Language and LiteratureVol. 60(2)
Stanford University (US)
Quality education
Openalex Percentile: Top 6%
Artificial Intelligence in Education
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How Can Large Language Models Support Learning? — Pelin Çılgın, Caleb Langley, et al. · Japanese Language and Literature (2026) | TGRS Research Map | TGRS