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The Impact of Generative AI Use on Graduate Students’ Research Competence: The Mediating Role of Critical Thinking and the Moderating Role of Research Self-Efficacy
Open accessAbstract
With the development of the digital intelligence era, generative AI is being widely used in scientific research, and its impact on graduate students’ research competence has attracted much attention from the academic community. Based on cognitive distribution theory and self-efficacy theory, this study classifies AI applications into three levels from basic to advanced—technical support AI use, text development AI use, and transformation AI use—explores their effects on graduate students’ research competence, and examines the mediating effect of critical thinking and the moderating effect of research self-efficacy. The results of the empirical analysis show that all three types of AI use behaviors are significantly correlated with research competence, with the strongest correlation for text development type and the weakest for technical support type. In the relationship between the three types of AI use behaviors and research competence, critical thinking plays a significant positive mediating role, and research self-efficacy plays a significant moderating role. Universities and tutors should guide students to focus on higher-order AI use behaviors in the text development and transformation categories, promoting the use of critical thinking to avoid technology misuse and improving research self-efficacy to help students accumulate confidence and support their research.
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“Studies on pedagogical partnerships, research competence, and writing support indicate that the educational value of GenAI depends on whether students remain the active agents of meaning making rather than outsourcing that role to the system (Wang & Zhang, 2026; Zhu & Yang, 2026).”
From Generative AI, Cognitive Offloading, and Learner Agency in Higher Education: A Scoping Review (2026) · cites this paper as Zhu & Yang, 2026
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“These findings are especially important because they show that self-regulation in GenAI environments is not limited to effort management or planning; it also includes epistemic monitoring, verification, and the ability to resist the automatic acceptance of plausible AI output.”
From Generative AI, Cognitive Offloading, and Learner Agency in Higher Education: A Scoping Review (2026) · cites this paper as Zhu & Yang, 2026
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Cited by
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Generative AI, Cognitive Offloading, and Learner Agency in Higher Education: A Scoping Review
Wang, G., Wang, W., Yang, D., & Ren, J. (2026) · Behavioral Sciences · doi:10.3390/bs16071150 · Cited by 20
“These constructs help organize why GenAI may serve as a scaffold for some learners and as a source of dependence for others, although the heterogeneous evidence base does not support a formal ranking of their causal importance (Xu et al., 2025; Zhu & Yang, 2026; C. Zhou & Wang, 2026).”
InfluentialMarin-Rodriguez, W., de Salinas, F. D. M. G. T., Ramírez, E. T. S., & Girón, E. C. A. (2026) · Iberoamerican Journal of Science Measurement and Communication · doi:10.47909/ijsmc.372 · Cited by 1
Gao, L., Sun, Y., & Khan, S. U. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1847369 · Cited by 1
Yan, Z., Fang, Z., & Zou, W. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1939286 · Cited by 1
AGUITE, A., Jr. (2026) · Research Square · doi:10.21203/rs.3.rs-9565332/v1
Metadata
- Authors
- Zhu, H., & Yang, S.
- Affiliations
- Normal College, Shihezi University, Shihezi 832003, China
- Venue
- Behavioral Sciences
- Publisher
- MDPI AG
- Published
- 2026-02-21
- Volume
- 16
- Issue
- 2
- Pages
- 304
- ISSN
- 2076-328X (ISSN-L)
- PMC
- PMC12937814
- OpenAlex
- W7130933570
- Cited by
- 13
- References
- 79
- Citations by year
- 2026: 13
- FWCI
- 108.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Behavioral Sciences (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Doctoral Education Challenges and Solutions
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI, graduate students, research skills, critical thinking, research self-efficacy, cognitive load theory, self-efficacy theory, textual development, technology misuse
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