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

TL;DR

This study examines how students interact with ChatGPT, a representative large language model, to elucidate its effects on learning. Through semi-structured interviews with 14 participants and reflexive thematic analysis, it identifies two learning modes—intentional and incidental—along with trust and ethical considerations in educational use. The findings show that ChatGPT can accelerate targeted learning and inspire ideation while raising concerns about accuracy, sourcing, and dependence. The work highlights the need for supervised integration, prompt design training, and governance to maximize educational benefits while mitigating potential harms.

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.