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

arXiv: https://arxiv.org/abs/2407.12723 · HTML: https://mlsanity.com/paper/2407.12723/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2024.

## Built on by (most cited first, 4 of 4)

- [Training LLM-based Tutors to Improve Student Learning Outcomes in Dialogues](https://mlsanity.com/paper/2503.06424/) (2025)
- [A Survey on Collaborating Small and Large Language Models for Performance, Cost-effectiveness, Cloud-edge Privacy, and Trustworthiness](https://mlsanity.com/paper/2510.13890/) (2025)
- ["Would You Want an AI Tutor?" Understanding Stakeholder Perceptions of LLM-based Systems in the Classroom](https://mlsanity.com/paper/2503.02885/) (2025)
- [FOR-Prompting: From Objection to Revision via an Asymmetric Prompting Protocol](https://mlsanity.com/paper/2510.01674/) (2025)

## Abstract (arXiv)

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.