The Future of Learning: Large Language Models through the Lens of Students

• URL: http://arxiv.org/abs/2407.12723v1
• Date: Wed, 17 Jul 2024 16:40:37 GMT
• Title: The Future of Learning: Large Language Models through the Lens of Students
• Authors: He Zhang, Jingyi Xie, Chuhao Wu, Jie Cai, ChanMin Kim, John M. Carroll,
• Abstract summary: Students grapple with the dilemma of utilizing ChatGPT's efficiency for learning and information seeking. Students perceive ChatGPT as being more "human-like" compared to traditional AI.
• Score: 20.64319102112755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As Large-Scale Language Models (LLMs) continue to evolve, they demonstrate significant enhancements in performance and an expansion of functionalities, impacting various domains, including education. In this study, we conducted interviews with 14 students to explore their everyday interactions with ChatGPT. Our preliminary findings reveal that students grapple with the dilemma of utilizing ChatGPT's efficiency for learning and information seeking, while simultaneously experiencing a crisis of trust and ethical concerns regarding the outcomes and broader impacts of ChatGPT. The students perceive ChatGPT as being more "human-like" compared to traditional AI. This dilemma, characterized by mixed emotions, inconsistent behaviors, and an overall positive attitude towards ChatGPT, underscores its potential for beneficial applications in education and learning. However, we argue that despite its human-like qualities, the advanced capabilities of such intelligence might lead to adverse consequences. Therefore, it's imperative to approach its application cautiously and strive to mitigate potential harms in future developments.

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