Bibliographic Details
Title: |
University students’ self-reported reliance on ChatGPT for learning: A latent profile analysis |
Authors: |
Ana Stojanov, Qian Liu, Joyce Hwee Ling Koh |
Source: |
Computers and Education: Artificial Intelligence, Vol 6, Iss , Pp 100243- (2024) |
Publisher Information: |
Elsevier, 2024. |
Publication Year: |
2024 |
Collection: |
LCC:Electronic computers. Computer science |
Subject Terms: |
ChatGPT, Artificial intelligence (AI), University students, Higher education, Latent profile analysis (LPA), Achievement goal orientation, Electronic computers. Computer science, QA75.5-76.95 |
More Details: |
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. |
Document Type: |
article |
File Description: |
electronic resource |
Language: |
English |
ISSN: |
2666-920X |
Relation: |
http://www.sciencedirect.com/science/article/pii/S2666920X24000468; https://doaj.org/toc/2666-920X |
DOI: |
10.1016/j.caeai.2024.100243 |
Access URL: |
https://doaj.org/article/266ce208622547e68d1d75f49db95e01 |
Accession Number: |
edsdoj.266ce208622547e68d1d75f49db95e01 |
Database: |
Directory of Open Access Journals |