Unpacking the cognitive and ethical pathways of generative AI tools in higher education: a PLS-SEM study of learning performance mediation and moderation effects

  • Sony Yunior Erlangga Doctoral Program in Science Education, Faculty of Teacher Training and Education, Universitas Sebelas Maret, Surakarta, Indonesia
  • Sarwanto Doctoral Program in Science Education, Faculty of Teacher Training and Education, Universitas Sebelas Maret, Surakarta, Indonesia https://orcid.org/0000-0002-9602-5776
  • Harlita Doctoral Program in Science Education, Faculty of Teacher Training and Education, Universitas Sebelas Maret, Surakarta, Indonesia
DOI: https://doi.org/10.25304/rlt.v34.3563
Keywords: AI-based learning, digital literacy, cognitive mediation, technology ethics, higher education

Abstract

This study examines how Generative Artificial Intelligence Tools (GAIT) influence student learning performance (LP) through cognitive, affective and ethical pathways using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 292 Indonesian university students through a structured questionnaire. The results show that GAIT has a direct positive effect on LP (β = 0.920, p < 0.001). Mediation analysis identifies AI Knowledge (AIK) as the most dominant mediator (β = 0.715, p < 0.001), followed by AI Perception (AIP), Creativity (CRE), Fairness & Ethics (FE) and Cognitive Offloading (CO). Furthermore, AIK significantly moderates the GAIT–LP relationship (β = 0.006, p = 0.048). The model demonstrates high predictive power (R2 = 0.604) and good model fit (Standardized Root Mean Square Residual (SRMR) = 0.068). These findings highlight the central role of AI literacy and ethical awareness in maximising the benefits of GAIT for learning. This study contributes theoretically by integrating cognitive, affective and normative dimensions into a unified model of GAIT adoption and offers practical implications for designing AI literacy and ethics-oriented curricula in higher education.

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References


Acosta-Enriquez, B. G. (2025). The mediating role of academic stress, critical thinking and performance expectations in the influence of academic self-efficacy on AI dependence: Case study in college students. Computers and Education: Artificial Intelligence, 8, 100381. https://doi.org/10.1016/j.caeai.2025.100381




Anggoro, K. J. & Khasanah, U. (2024). Technology-infused teams-games-tournaments in English language class: A mixed method study on students’ achievement and perception. Research in Learning Technology, 32(1063519), 1–17. https://doi.org/10.25304/rlt.v32.3150




Atenas, J., Havemann, L. & Nerantzi, C. (2024). Critical and creative pedagogies for artificial intelligence and data literacy: An epistemic data justice approach for academic practice. Research in Learning Technology, 32(1063519), 1–16. https://doi.org/10.25304/rlt.v32.3296




Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147. https://doi.org/10.1037/0003-066X.37.2.122




Bergdahl, N. (2025). Attitudes, perceptions and AI self-efficacy in K-12 education. Computers and Education: Artificial Intelligence, 8, 100358. https://doi.org/10.1016/j.caeai.2024.100358




Bernabei, M. et al. (2023). Students’ use of large language models in engineering education: A case study on technology acceptance, perceptions, efficacy, and detection chances. Computers and Education: Artificial Intelligence, 5, 100172. https://doi.org/10.1016/j.caeai.2023.100172




Blancaflor, E. et al. (2023). A literature review of the legislation and regulation of deepfakes in the Philippines. In Proceedings of the March 2023 14th International Conference on E-business, Management and Economics (pp. 392–397). Association for Computing Machinery. https://doi.org/10.1145/3616712.3616722




Chen, Y. H. (2025). Impact of basic artificial intelligence (AI) course on understanding concepts, literacy, and empowerment in the field of AI among students. Computer Applications in Engineering Education, 33(1), e22806. https://doi.org/10.1002/cae.22806




Davis, F. D. (1989). Information technology perceived usefulness and perceived ease of use. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008




Fabia, J. N. V. (2024). Students satisfaction, self-efficacy and achievement in an emergency online learning course. Research in Learning Technology, 32(1063519), 1–18. https://doi.org/10.25304/rlt.v32.3179




Gansser, O. A. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, 101535. https://doi.org/10.1016/j.techsoc.2021.101535




Geri, A. (2025). Predicting teachers’ intentions to use virtual reality in education: A study based on the UTAUT-2 framework. Research in Learning Technology, 33(1063519), 1–15. https://doi.org/10.25304/rlt.v33.3429




Hair, J. F., Howard, M. C. & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069




Hu, L., Wang, H. & Xin, Y. (2025). Factors influencing Chinese pre-service teachers’ adoption of generative AI in teaching: An empirical study based on UTAUT2 and PLS-SEM. Education and Information Technologies, 30, 12609–12631. https://doi.org/10.1007/s10639-025-13353-7




Islam, M. R. (2025). Generative AI, cybersecurity, and ethics. In, M. R. Islam (Ed.), Generative AI, Cybersecurity, and Ethics. Wiley, pp. 1–81. https://doi.org/10.1002/9781394279326




Jordan, J. (2023). Development of a lecture evaluation tool rooted in cognitive load theory: A modified Delphi study. AEM Education and Training, 7(1), e10839. https://doi.org/10.1002/aet2.10839




Kennedy, M. J. (2024). Cognitive load theory: An applied reintroduction for special and general educators. Teaching Exceptional Children, 56(6), 440–451. https://doi.org/10.1177/00400599211048214




Kim, B. J. (2025). The AI-environment paradox: Unraveling the impact of artificial intelligence (AI) adoption on pro-environmental behavior through work overload and self-efficacy in AI learning. Journal of Environmental Management, 380, 125102. https://doi.org/10.1016/j.jenvman.2025.125102




Kulangareth, N. V. et al. (2024). Investigation of deepfake voice detection using speech pause patterns: Algorithm development and validation. JMIR Biomedical Engineering, 9, e56245. https://doi.org/10.2196/56245




Lamberti, G. (2023). Hybrid multigroup partial least squares structural equation modelling: An application to bank employee satisfaction and loyalty. Quality and Quantity, 57, 683–705. https://doi.org/10.1007/s11135-021-01096-9




Lang, M. (2024). Fostering critical thinking, AI and data literacy, and global competence amongst business students. In, Proceedings of the Information Systems Education Conference, ISECON, 43–48. Retrieved from https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85218427075&origin=inward




Li, B. (2024). Construction of an AI literacy general education curriculum based on ‘knowledge-skills’ navigation. Journal of Library and Information Science in Agriculture, 36(8), 34–42. https://doi.org/10.13998/j.cnki.issn1002-1248.24-0670




Lundberg, E. & Mozelius, P. (2024). The potential effects of deepfakes on news media and entertainment. AI and Society, 40, 2159–2170. https://doi.org/10.1007/s00146-024-02072-1




Matli, W. (2024). Extending the theory of information poverty to deepfake technology. International Journal of Information Management Data Insights, 4(2), 100286. https://doi.org/10.1016/j.jjimei.2024.100286




Passmore, J., Olafsson, B. & Tee, D. (2025). A systematic literature review of artificial intelligence (AI) in coaching: Insights for future research and product development. Journal of Work-Applied Management, ahead-of-print(ahead-of-print). https://doi.org/10.1108/JWAM-11-2024-0164




Punyani, P., & Chhikara, R. (2023). Comparison of different machine learning algorithms for deep fake detection. In Proceedings of the March 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI) (pp. 58–63). IEEE. https://doi.org/10.1109/ICCSAI59793.2023.10421164




Rajput, T. & Arora, B. (2024). A systematic review of deepfake detection using learning techniques and vision transformer. In, Tanwar, S. et al. (Eds.). Lecture Notes in Networks and Systems: Vol. 991 LNNS. Springer Science and Business Media Deutschland GmbH, 217–235. https://doi.org/10.1007/978-981-97-2550-2_17




Richmond, L. L. & Taylor, R. G. (2025). The benefits and potential costs of cognitive offloading for retrospective information. Nature Reviews Psychology, 4, 312–321. https://doi.org/10.1038/s44159-025-00432-2




Sarstedt, M., Ringle, C. M. & Hair, J. F. (2022). Partial least squares structural equation modeling BT. In, Homburg, C., Klarmann, M. & Vomberg, A. (Eds.). Handbook of Market Research. Springer International Publishing, 587–632. https://doi.org/10.1007/978-3-319-57413-4_15




Senent, R. M. & Bueso, D. (2022). The banality of (automated) evil: Critical reflections on the concept of forbidden knowledge in machine learning research. Recerca, 27(2), 6147. https://doi.org/10.6035/recerca.6147




Shahzad, M. F. (2025). Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education. Education and Information Technologies, 30(3), 3691–3716. https://doi.org/10.1007/s10639-024-12949-9




Shahzad, M. F., Xu, S. & Baheer, R. (2024). Assessing the factors influencing the intention to use information and communication technology implementation and acceptance in China’s education sector. Humanities and Social Sciences Communications, 11(1), 1–15. https://doi.org/10.1057/s41599-024-02777-0




Shahzad, M. F. et al. (2024). Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning. Heliyon, 10(8), e29523. https://doi.org/10.1016/j.heliyon.2024.e29523




Shum, S. B. (2024). Generative AI for critical analysis: Practical tools, cognitive offloading and human agency. CEUR Workshop Proceedings, 3667, 205–213. Retrieved from https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85192017258&origin=inward




Srinivasan, S. (2023). Understanding user perception of biometric privacy in the era of generative AI. In Proceedings of the 2023 4th International Conference on Communication, Computing and Industry 6.0 C216 2023 (pp. 1–6). IEEE. https://doi.org/10.1109/C2I659362.2023.10430931




Sweller, J., van Merriënboer, J. J. G. & Paas, F. (2019). Cognitive architecture and instructional design: 20 Years later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5




Tan, F. C. J. H. et al. (2021). The association between self-efficacy and self-care in essential hypertension: A systematic review. BMC Family Practice, 22(1), 1–12. https://doi.org/10.1186/s12875-021-01391-2




Wang, S. et al. (2021). Determinants of active online learning in the smart learning environment: An empirical study with PLS-SEM. Sustainability (Switzerland), 13(17), 1–19. https://doi.org/10.3390/su13179923




Wang, S., Sun, Z. & Chen, Y. (2023). Effects of higher education institutes’ artificial intelligence capability on students’ self-efficacy, creativity and learning performance. Education and Information Technologies, 28(5), 4919–4939. https://doi.org/10.1007/s10639-022-11338-4




Westphal, M. et al. (2023). Decision control and explanations in human-AI collaboration: Improving user perceptions and compliance. Computers in Human Behavior, 144, 107714. https://doi.org/10.1016/j.chb.2023.107714




Wu, Z. (2021). AI creativity and the human-AI co-creation model. Lecture Notes in Computer Science, 12762, 171–190. https://doi.org/10.1007/978-3-030-78462-1_13




Yim, I. H. Y. (2024). A critical review of teaching and learning artificial intelligence (AI) literacy: Developing an intelligence-based AI literacy framework for primary school education. Computers and Education: Artificial Intelligence, 7, 100319. https://doi.org/10.1016/j.caeai.2024.100319




Zhang, X. et al. (2024). Association between social media use and students’ academic performance through family bonding and collective learning: The moderating role of mental well-being. Education and Information Technologies, 29(11), 14059–14089. https://doi.org/10.1007/s10639-023-12407-y
Published
2026-03-13
How to Cite
Erlangga , S. Y., Sarwanto, & Harlita. (2026). Unpacking the cognitive and ethical pathways of generative AI tools in higher education: a PLS-SEM study of learning performance mediation and moderation effects. Research in Learning Technology, 34. https://doi.org/10.25304/rlt.v34.3563
Issue
Vol. 34 (2026)
Section
Original Research Articles
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