Time tells the tale: tracing the interplay between self-regulated learning and deep approaches to learning with GenAI through individual differences
Abstract
The integration of Generative AI (GenAI) is reshaping pedagogical practices and learning paradigms, including self-regulated learning (SRL) and deep approaches to learning (DAL). Although SRL and DAL are linked to improved outcomes, their interaction within AI-supported contexts is underexplored, as is the evolving nature of SRL, its impact on DAL over time, as well as the role of individual differences. To address these gaps, this longitudinal study examined temporal relationships among SRL phases (forethought, performance, self-reflection) and DAL, alongside the moderating effects of Big Five personality traits and gender. Using cross-lagged panel modeling and latent profile analysis, results revealed that (a) SRL operates as a dynamic, recursive process, with early performance and reflection predicting later forethought; (b) all SRL phases consistently enhanced DAL, which also showed temporal stability; and (c) personality traits, especially extraversion and neuroticism, significantly influenced SRL development during forethought, while gender showed no significant effect. These findings suggest that university students actively engage in self-regulation as a dynamic, evolving process that fosters DAL with GenAI over time. Educators should place greater emphasis on individual differences to offer tailored, continuous support that strengthens students’ SRL in AI-rich environments.
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Ma, X., Zhu, S., Li, C., Xu, J., Liu, M., Lin, Y., & Li, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-9492175/v1
- Authors
- Xia, Q.
- Affiliations
- Zhejiang University (CN)
- Corresponding author
- Xia, Qi
- ORCID
- Xia, Qi
- Venue
- Interactive Learning Environments
- Publisher
- Informa UK Limited
- Published
- 2025-09-29
- Volume
- 34
- Issue
- 5
- Pages
- 3067-3085
- ISSN
- 1049-4820 (ISSN-L)
- 1744-5191
- OpenAlex
- W4414614324
- Cited by
- 5
- References
- 81
- Citations by year
- 2026: 5
- FWCI
- 9.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Interactive Learning Environments (publishedVersion)
- Funders
- the Zhejiang Province Soft Science General Project (2025C35084)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- student self-regulation, deep learning approach, generative AI, individual differences, time course, AI-assisted learning
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