University students’ self-reported reliance on ChatGPT for learning: A latent profile analysis

Autor: Ana Stojanov, Qian Liu, Joyce Hwee Ling Koh
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Computers and Education: Artificial Intelligence, Vol 6, Iss , Pp 100243- (2024)
Druh dokumentu: article
ISSN: 2666-920X
DOI: 10.1016/j.caeai.2024.100243
Popis: Although ChatGPT, a state-of-the-art, large language model, seems to be a disruptive technology in higher education, it is unclear to what extent students rely on this tool for completing different tasks. To address this gap, we asked university students (N = 490) recruited via CloudResearch to rate the extent to which they rely on ChatGPT for completing 13 tasks identified in a previous pilot study. Five distinct profiles emerged: ‘Versatile low reliers’ (38.2%) were characterised by low overall self-reported reliance across the tasks, while ‘all-rounders’ (10.4%) had high overall self-reported reliance. The ‘knowledge seekers’ (16.5%) scored particularly high on tasks such as content acquisition, information retrieval and summarising of texts, while the ‘proactive learners’ (11.8%) on tasks such as obtaining feedback, planning and quizzing. Finally, the ‘assignment delegators’ (23.1%) relied on ChatGPT for drafting assignments, writing homework and having ChatGPT write their assignment for them. The findings provide a nuanced understanding of how students rely on ChatGPT for learning.
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