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Students' attitudes toward LLMs and its association with metacognitive abilities: A cross-sectional study
Shihui Natalie Yeh1, Chiew-Jiat Rosalind Siah1
1National University of Singapore, Singapore.
Aim:
This study evaluated the students' attitudes toward LLMs and its association with metacognitive abilities.
Background:
In recent years, the widespread adoption of large language models (LLMs) has been found to offer personalized and immediate information that can facilitate learning. However, there remains a limited understanding of their use and its impact on metacognitive learning among nursing undergraduates.
Methods:
An exploratory, cross-sectional, descriptive quantitative design was employed to examine the relationships between variables, specifically how students' attitudes and metacognitive abilities are related when using LLMs.
Results:
204 nursing undergraduates were recruited during the period October 2024 to January 2025. Findings reported weaker cognitive awareness (mean difference = -1.11, t = -10.62, p < .001, Cohen's d ≈ -0.51, 95 % CI -0.81 to -1.41) but stronger regulatory skills (mean difference = 10.77, t = 29.46, p < .001, d ≈ 1.97, 95 % CI 10.02-11.52).
Conclusion:
The study found that nursing students widely use LLMs, which can enhance learning through higher regulation but may potentially lead to overreliance and reduced knowledge level. Therefore, this study recommends the need for explicit instruction in digital literacy and adopting metacognitive strategies when integrating structured activities into curricula to mitigate overreliance and support metacognitive development.
No Patient Or Public Contribution:
The paper focused on undergraduates within the academic institution.
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