The present study aims to construct and validate a five-dimensional structure model for pre-service teachers' engagement in generative artificial intelligence (GenAI)-driven online learning. A total of 1,478 pre-service teachers were selected as research participants, and the measurement scale encompasses five core dimensions: social, collaborative, behavioral, emotional, and cognitive engagements. The study first established the measurement item system through exploratory factor analysis, followed by confirmatory factor analysis to assess data suitability and cross-group measurement equivalence. Results demonstrate: Firstly, the five-dimensional model exhibits strong suitability, with all fit indices meeting standards after modification, and each dimension demonstrates robust unidimensionality and converge validity. Secondly, the model shows high cross-sample consistency between the estimation and validation groups. The present study provides empirical evidence supporting the five-dimensional structure model of GenAI-based online learning engagement among 1,478 pre-service teachers. Finally, the relevant issues are discussed based on the findings, and directions for future research are proposed.

Xu Yuanli, Li Yuxuan, Ma Wanzhi. Construction and Validation of a Five-Dimensional Structure Model for the Participation Level of Pre-service Teachers in Generative AI-Based Online Learning—An Empirical Study Based on Social, Collaborative, Behavioral, Emotional, and Cognitive Dimensions. Frontiers in Educational Research (2026), Vol. 9, Issue 5: 60-68. https://doi.org/10.25236/FER.2026.090510.

[1] Baidoo-Anu D, Ansah L O. Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning[J]. Journal of AI, 2023, 7(1): 56.

[2] Kasneci E, Seßler K, Küchemann S, et al. ChatGPT for good? On opportunities and challenges of large language models for education[J]. Learning and individual differences, 2023, 103: 102274.

[3] Achiam J, Adler S, Agarwal S, et al. Gpt-4 technical report[J]. arXiv preprint arXiv:2303.08774, 2023.

[4] Dimitriadou E, Lanitis A. A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms[J]. Smart Learning Environments, 2023, 10(1): 12.

[5] Astin A W. Student involvement: A developmental theory for higher education[M]//College student development and academic life. Routledge, 2014: 258.

[6] Fredricks J A, Blumenfeld P C, Paris A H. School engagement: Potential of the concept, state of the evidence[J]. Review of educational research, 2004, 74(1): 68.

[7] Dixson M D. Creating effective student engagement in online courses: What do students find engaging?[J]. Journal of the Scholarship of Teaching and Learning, 2010: 9.

[8] Lam S, Jimerson S, Wong B P H, et al. Understanding and measuring student engagement in school: the results of an international study from 12 countries[J]. School Psychology Quarterly, 2014, 29(2): 213.

[9] Coates H. The value of student engagement for higher education quality assurance[J]. Quality in higher education, 2005, 11(1): 29.

[10] Garrison D R, Anderson T, Archer W. Critical inquiry in a text-based environment: Computer conferencing in higher education[J]. The internet and higher education, 1999, 2(2-3): 98.

[11] Johnson D W, Johnson R T. Cooperation and competition: Theory and research[M]. Interaction Book Company, 1989.

[12] Vygotsky L S, Cole M. Mind in society: Development of higher psychological processes[M]. Harvard university press, 1978.

[13] Pekrun R. Emotions as drivers of learning and cognitive development[M]//New perspectives on affect and learning technologies. New York, NY: Springer New York, 2011: 27.

[14] Chan C K Y, Hu W. Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education[J]. International journal of educational technology in higher education, 2023, 20(1): 43.

[15] Yan Qiao, Chen Changfeng. Boundary Issues in Human-Machine Collaborative Content Production: Construction, Dissolution, and Reshaping [J]. Youth Journalist, 2025(7).

[16] Yan Yan C, Jafri N B. Factors influencing the intention to use generative artificial intelligence in educational systems: a meta-analysis[J]. BMC psychology, 2026.

[17] Celik I, Kontkanen S, Laru J, et al. Co-constructing adaptive lesson plans with GenAI: Pre-service teachers' Intelligent-TPACK and prompt engineering strategies[J]. Computers & Education, 2025: 105485.

[18] Prilop C N, Mah D K, Jacobsen L J, et al. Generative AI in teacher education: Educators’ perceptions of transformative potentials and the triadic nature of AI literacy explored through AI-enhanced methods[J]. Computers and Education: Artificial Intelligence, 2025: 100471.

[19] Nguyen N N, Barbieri W. Generative AI in work‐integrated learning: Supporting pre‐service teachers' emotional labour and self‐management in Australian initial teacher education[J]. British Journal of Educational Technology, 2026.

[20] Runge I, Hebibi F, Lazarides R. Acceptance of pre-service teachers towards artificial intelligence (AI): The role of AI-related teacher training courses and AI-TPACK within the technology acceptance model[J]. Education Sciences, 2025, 15(2): 167.

[21] Chen X, Liu J, Liu Y,  et al. Can ChatGPT outperform college students in critical thinking skills during argumentation activities? A case study[J]. Thinking Skills and Creativity, 2026: 102166.

[22] Deci E L, Ryan R M. Conceptualizations of intrinsic motivation and self-determination[M]//Intrinsic motivation and self-determination in human behavior. Boston, MA: Springer US, 1985:32.

[23] Fredricks J A, Blumenfeld P C, Paris A H. School engagement: Potential of the concept, state of the evidence[J]. Review of educational research, 2004, 74(1): 78.

[24] Redmond P. An online engagement framework for higher education[J]. Online learning, 2018.

[25] Getenet S, Cantle R, Redmond P, et al. Students' digital technology attitude, literacy and self-efficacy and their effect on online learning engagement[J]. International Journal of Educational Technology in Higher Education, 2024, 21(1): 3.

[26] George D, Mallery P. IBM SPSS statistics 25 step by step: A simple guide and reference[M]. Routledge, 2019.

[27] Rong Taisheng. AMOS and Research Methods [M]. Chongqing: Chongqing University Press, 2009:131.

[28] Browne M W, Cudeck R, Bollen K A, Long J S. Alternative ways of assessing model fit[J]. Testing Structural Equation Models, 1993, 154(4): 147.

[29] Peterson C. Accommodation, prediction and replication: Model selection in scale construction[J]. Synthese, 2019, 196(10): 4338.

[30] Henderson P, Islam R, Bachman P, et al. Deep reinforcement learning that matters[C]//Proceedings of the AAAI conference on artificial intelligence. 2018, 32(1).

[31] Pekrun R. Emotions as drivers of learning and cognitive development[M]//New perspectives on affect and learning technologies. New York, NY: Springer New York, 2011: 29.

[32] Knafl G J, Grey M. Factor analysis model evaluation through likelihood cross-validation[J]. Statistical methods in medical research, 2007, 16(2): 87.

[33] Bandura A. Bandura-Social Learning Theory[J]. Theories of Development: Concepts and Applications, 1977: 186.