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Lok Him Lam, The Hong Kong University of Science and Technology, Hong KongAbstract
A key feature of large language models (LLMs) is the ability to comprehend and generate human-like texts, and this feature undoubtedly endows LLMs with the capacity to revolutionize the education sector, particularly higher education. Although ChatGPT, the most well-known LLM, is still not available in Hong Kong to the public, universities in Hong Kong are no exception to this global race of embracing LLMs into teaching and learning. Although students' perceptions of LLMs are not typically an under-studied topic, such perceptions may constantly shift because of new developments in LLMs. In particular, LLMs have become more advanced, and there have been a growing number of LLMs. This study aims to understand the perceptions of Hong Kong university students on the adoption of LLMs in higher education. Thirty university students or recent graduates (within two years) constitute the sample of this study, who were asked to fill in a survey mostly consisting of qualitative questions that explore their perceptions of LLMs in regard to their usage, reliability/accuracy, negative impacts, and trade-offs. This study concludes that Hong Kong university students are generally aware of the known concerns of LLMs in education, and while most of them have used LLMs for educational purposes, the majority of those who have used them have not used them to directly write academic essays. At the same time, some students acknowledge that they are personally suffering from related adverse impacts from the use of LLMs, but most that acknowledge so would continue to use LLMs.
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Conference: ECE2025Stream: Learning Experiences
This paper is part of the ECE2025 Conference Proceedings (View)
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To cite this article:
Lam L. (2025) Embracing Large Language Models in Higher Education: A Case Study on Hong Kong’s Students’ Perceptions ISSN: 2188-1162 The European Conference on Education 2025: Official Conference Proceedings (pp. 719-730) https://doi.org/10.22492/issn.2188-1162.2025.56
To link to this article: https://doi.org/10.22492/issn.2188-1162.2025.56
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