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Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students

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arxiv 2411.18708 v1 pith:NZGFP35D submitted 2024-11-27 cs.HC cs.AI

classification cs.HCcs.AI
keywords studentsllmseducationlanguagecapabilitiesincludinglargemodels
verification ladder T0 review T1 audit T2 compute T3 formal
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The impressive essay writing and problem-solving capabilities of large language models (LLMs) like OpenAI's ChatGPT have opened up new avenues in education. Our goal is to gain insights into the widespread use of LLMs among secondary students to inform their future development. Despite school restrictions, our survey of over 300 middle and high school students revealed that a remarkable 70% of students have utilized LLMs, higher than the usage percentage among young adults, and this percentage remains consistent across 7th to 12th grade. Students also reported using LLMs for multiple subjects, including language arts, history, and math assignments, but expressed mixed thoughts on their effectiveness due to occasional hallucinations in historical contexts and incorrect answers for lack of rigorous reasoning. The survey feedback called for LLMs better adapted for students, and also raised questions to developers and educators on how to help students from underserved communities leverage LLMs' capabilities for equal access to advanced education resources. We propose a few ideas to address such issues, including subject-specific models, personalized learning, and AI classrooms.

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Cited by 2 Pith papers

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  1. People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text

    cs.CL 2025-01 conditional novelty 6.0 of 10

    Expert annotators who frequently use LLMs for writing detected AI-generated non-fiction articles with 99.3% accuracy by majority vote, outperforming all but one commercial detector.

  2. When LLM Essays Outscore Student Essays: What a Korean Writing Rubric Rewards and Where Readers Disagree

    cs.CL 2026-01 conditional novelty 5.0 of 10

    Rubric-guided calibration lifted three Korean-language experts' majority-vote accuracy at detecting LLM essays from 60% to 90% on disjoint 30-essay sets (10/10 on a 10-essay subset), with agreement rising from Fleiss ...

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