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Cited by 83Artificial Intelligence in EducationInnovative Teaching and Learning MethodsTechnology Adoption and User Behaviour

Generative AI tool use enhances academic achievement in sustainable education through shared metacognition and cognitive offloading among preservice teachers

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Iqbal, Javed, Hashmi, Zarqa Farooq, Asghar, Muhammad Zaheer, Abid, Muhammad Naseem·2025·Scientific Reports
DOI 10.1038/s41598-025-01676-xPMC PMC12075473
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Abstract

The integration of generative artificial intelligence tools in education has emerged as a transformative approach to enhancing learning outcomes, particularly in the context of sustainable development goals (SDG4). Therefore, the present study investigates the connection between generative artificial intelligence tool usage (GenAITU) and academic achievement (AA) in the context of SDG4. We assessed the mediating role of shared metacognition (SMC) and cognitive offloading (COL) in this relationship among preservice teachers (PSTs). The indicators, including performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), and use behavior (UB), are derived from adapting the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) for GenAITU. The authors surveyed 465 students from five universities in Wuhan, China, using a 7-point Likert scale through a time-lag design. Statistical analysis was performed through partial least squares structural equation modeling (PLS-SEM), to determine the relationship between variables. Findings indicated that two components of GenAITU, namely PE and UB, showed significant positive associations with AA, while the other two, EE and FC, did not show significant and positive relationships with AA. Results also showed that three dimensions of GenAITU, namely EE, FC, and UB have a positive and significant association with SMC while PE has a positive and significant connection with SMC. All four components of GenAITU like PE, EE, FC, and UB have positive and significant links with COL. SMC and COL have a positive and significant relationship with AA. Results also indicated that SMC mediated the connections between GenAITU (EE, FC, and UB) and AA. Outcomes also indicated that COL mediated the connections between GenAITU (PE, EE, FC, and UB) and AA. The current study shows that SMC and COL were strong mediators of the association between GenAITU and AA. The results of our study provide guidance to teachers, curriculum planners, and university management to successfully integrate GenAITU into the education for PSTs.

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  • This pattern indicates that scaffolded GenAI use extends beyond the higher-order skills emphasised in recent literature, operating as a broader mechanism consistent with Melanou and Beege's finding of gains distributed across the full cognitive hierarchy rather than concentrated at higher-order outcomes.

    “This pattern indicates that scaffolded GenAI use extends beyond the higher-order skills emphasised in recent literature (Iqbal et al., 2025), operating instead as a broader mechanism that is consistent with Melanou and Beege's (2026) finding of gains distributed across the full cognitive hierarchy rather than concentrated at higher-order outcomes.”

    From <b>Effects of Partial vs. Full Cognitive Offloading with Generative AI on Learning Retention </b> (2026) · cites this paper as Iqbal et al., 2025

  • The UNESCO AI CFT limits discussion on epistemic considerations such as critical inquiry, cognitive offloading and metacognition.

    “In addition, the framework limits discussion on the epistemic considerations, such as critical inquiry, cognitive offloading and metacognition (Iqbal et al., 2025;Jose, Cherian, et al., 2025).”

    From An integrated AI literacy framework for higher‑education teachers: a critical review and conceptual synthesis (2026) · cites this paper as Iqbal et al., 2025

    The Literature review discusses shared metacognition and cognitive offloading in preservice teachers’ use of generative AI, not limits of the UNESCO AI CFT. Searches for “UNESCO,” “critical inquiry,” and the framework’s alleged limits found no passage in our copy addressing that claim; page numbers are unavailable. (LLM, conf=0.76)

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  1. [1]Islam, M. M. in Economics and Finance Readings. (eds Evan Lau, Widya Paramita, Kai-Hong Tee, & Lee Ming Tan) 103–117 (Springer Nature Singapore).
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  6. [40]Amar, S. & Benchouk, K. in In General Aspects of Applying Generative AI in Higher Education: Opportunities and Challenges. 39–53 (eds Lahby, M.) (Springer Nature Switzerland, 2024). Yassine Maleh, Antonio Bucchiarone, & Satu Elisa Schaeffer)
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Authors
Iqbal, J., Hashmi, Z. F., Asghar, M. Z., & Abid, M. N.
Affiliations
  • Zhejiang International Studies University (CN)
  • Huazhong University of Science and Technology (CN)
  • University of Helsinki (FI)
Corresponding author
Asghar, Muhammad Zaheer
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2025-05-13
Volume
15
Issue
1
Pages
16610-16610
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-025-01676-x
PMC
PMC12075473
OpenAlex
W4410319159
Cited by
82
References
60
Citations by year
2026: 68 · 2025: 14
FWCI
85.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Työväentutkimus Vuosikirja (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
pre-service teachers, academic performance, cognitive offloading, sustainable education, UTAUT2, technology acceptance, performance expectancy, effort expectancy, facilitating conditions, use behavior, mediating effect

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