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

• URL: http://arxiv.org/abs/2411.18708v1
• Date: Wed, 27 Nov 2024 19:19:34 GMT
• Title: Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students
• Authors: Tiffany Zhu, Kexun Zhang, William Yang Wang,
• Abstract summary: Large language models (LLMs) like OpenAI's ChatGPT have opened up new avenues in education. Despite school restrictions, our survey of over 300 middle and high school students revealed that a remarkable 70% of students have utilized LLMs. We propose a few ideas to address such issues, including subject-specific models, personalized learning, and AI classrooms.
• Score: 53.20318273452059
• License:
• Abstract: 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.

Related papers

• Position: LLMs Can be Good Tutors in Foreign Language Education [87.88557755407815]
  We argue that large language models (LLMs) have the potential to serve as effective tutors in foreign language education (FLE) Specifically, LLMs can play three critical roles: (1) as data enhancers, improving the creation of learning materials or serving as student simulations; (2) as task predictors, serving as learner assessment or optimizing learning pathway; and (3) as agents, enabling personalized and inclusive education.
  arXiv  Detail & Related papers  (2025-02-08T06:48:49Z)
• Is ChatGPT Massively Used by Students Nowadays? A Survey on the Use of Large Language Models such as ChatGPT in Educational Settings [0.25782420501870296]
  This study investigates how 395 students aged 13 to 25 years old in France and Italy integrate Large Language Models (LLMs) into their educational routines. Key findings include the widespread use of these tools across all age groups and disciplines. Results also show gender disparities, raising concerns about an emerging AI literacy and technological gender gap.
  arXiv  Detail & Related papers  (2024-12-23T11:29:44Z)
• Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs [49.18567856499736]
  We investigate whether large language models (LLMs) can be supportive of open-ended dialogue tutoring. We apply a range of knowledge tracing (KT) methods on the resulting labeled data to track student knowledge levels over an entire dialogue. We conduct experiments on two tutoring dialogue datasets, and show that a novel yet simple LLM-based method, LLMKT, significantly outperforms existing KT methods in predicting student response correctness in dialogues.
  arXiv  Detail & Related papers  (2024-09-24T22:31:39Z)
• "The teachers are confused as well": A Multiple-Stakeholder Ethics Discussion on Large Language Models in Computing Education [17.25008833760501]
  Large Language Models (LLMs) are advancing quickly and impacting people's lives for better or worse. In higher education, concerns have emerged such as students' misuse of LLMs and degraded education outcomes. We conducted a case study consisting of stakeholder interviews in higher education computer science.
  arXiv  Detail & Related papers  (2024-01-23T02:43:00Z)
• Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges [60.62904929065257]
  Large language models (LLMs) offer possibility for resolving this issue by comprehending individual requests. This paper reviews the recently emerged LLM research related to educational capabilities, including mathematics, writing, programming, reasoning, and knowledge-based question answering.
  arXiv  Detail & Related papers  (2023-12-27T14:37:32Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning [4.376598435975689]
  We discuss the challenges associated with employing large language models to enhance students' mathematical problem-solving skills. LLMs can generate the wrong reasoning processes, and also exhibit difficulty in understanding the given questions' rationales when attempting to correct students' answers.
  arXiv  Detail & Related papers  (2023-10-20T16:05:35Z)
• Democratizing Reasoning Ability: Tailored Learning from Large Language Model [97.4921006089966]
  We propose a tailored learning approach to distill such reasoning ability to smaller LMs. We exploit the potential of LLM as a reasoning teacher by building an interactive multi-round learning paradigm. To exploit the reasoning potential of the smaller LM, we propose self-reflection learning to motivate the student to learn from self-made mistakes.
  arXiv  Detail & Related papers  (2023-10-20T07:50:10Z)
• "With Great Power Comes Great Responsibility!": Student and Instructor Perspectives on the influence of LLMs on Undergraduate Engineering Education [2.766654468164438]
  The rise in popularity of Large Language Models (LLMs) has prompted discussions in academic circles. This paper conducts surveys and interviews within undergraduate engineering universities in India. Using 1306 survey responses among students, 112 student interviews, and 27 instructor interviews, this paper offers insights into the current usage patterns.
  arXiv  Detail & Related papers  (2023-09-19T15:29:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.