AI Meets the Classroom: When Do Large Language Models Harm Learning?
Not on the map. Why?
Abstract (arXiv)
The effect of large language models (LLMs) in education is debated: Previous research shows that LLMs can help as well as hurt learning. In two pre-registered and incentivized laboratory experiments, we find no effect of LLMs on overall learning outcomes. In exploratory analyses and a field study, we provide evidence that the effect of LLMs on learning outcomes depends on usage behavior. Students who substitute some of their learning activities with LLMs (e.g., by generating solutions to exercises) increase the volume of topics they can learn about but decrease their understanding of each topic. Students who complement their learning activities with LLMs (e.g., by asking for explanations) do not increase topic volume but do increase their understanding. We also observe that LLMs widen the gap between students with low and high prior knowledge. While LLMs show great potential to improve learning, their use must be tailored to the educational context and students' needs.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 6 of its 66 references that are in the map. First posted on arXiv 29 Aug 2024.
Builds on
6 of 66 references.
References · 6
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,332
- 2021A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Levelcited by 96
- 2023Learning gain differences between ChatGPT and human tutor generated algebra hintscited by 42
- 2024The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performancescited by 14
Built on by
Citing papers per year: 2026: 5.
5 papers.
- 2026Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Riskscited by 3
- 2026SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMscited by 3
- 2026Beyond Access: Guided LLM Scaffolding for Independent Learning in Undergraduate Statisticscited by 0
- 2026Designing Against Deskilling: Metacognitive Feedback Reduces Cognitive Offloading to LLM Assistantscited by 0
- 2026Examining Variation in How Guided AI Tutors Resolve Student Impassescited by 0
- 2026Examining Variation in How Guided AI Tutors Resolve Student Impasses29 Sep 2026
- 2026Designing Against Deskilling: Metacognitive Feedback Reduces Cognitive Offloading to LLM Assistants17 Sep 2026
- 2026Beyond Access: Guided LLM Scaffolding for Independent Learning in Undergraduate Statistics31 May 2026
- 2026SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs24 Apr 2026
- 2026Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks6 Mar 2026