Perceived Usefulness and Higher-Order Thinking: A PLS-SEM Investigation of the Chaoxing AI Learning Companion Platform among Vocational College Students
This study focuses on vocational college students’ adoption of the “embedded” Chaoxing AI learning companion platform and the influence of AI learning companion adoption on higher-order thinking. Unlike general-purpose AI tools, the Chaoxing AI learning companion platform is embedded in institutional teaching platforms and course-based learning processes. However, existing research has largely examined willingness to use general-purpose generative AI tools, and empirical evidence on how AI learning companion functions embedded in educational platforms affect vocational college students’ higher-order thinking remains scarce. Drawing on the Technology Acceptance Model (TAM), this study constructs a research model of “perceived ease of use and perceived usefulness—AI learning companion adoption—higher-order thinking” and conducts PLS-SEM analysis on 75 valid questionnaires using SmartPLS. The results show that the revised measurement model exhibits good reliability and convergent validity. The structural model results indicate that perceived usefulness significantly and positively affects AI learning companion adoption (β = 0.748, P < 0.001), whereas perceived ease of use does not significantly affect AI learning companion adoption (β = 0.092, P = 0.517); AI learning companion adoption significantly and positively affects higher-order thinking (β = 0.540, P < 0.001). The mediation results further show that AI learning companion adoption exerts a significant indirect effect between perceived usefulness and higher-order thinking (β = 0.404, P = 0.001), whereas the indirect effect of perceived ease of use is not significant. The findings suggest that, in the vocational education context, whether students perceive the AI learning companion as “genuinely useful” matters more for their adoption than whether it is “easy to use,” and this adoption is further associated with the development of higher-order thinking. By applying TAM to platform-based AI learning companion scenarios, this study extends the model’s explanatory scope to embedded educational agents and offers practical implications for vocational colleges to optimize AI-supported learning tools and promote more inclusive and sustainable education (SDG 4).
[1] Crompton H, Burke D, 2023, Artificial Intelligence in Higher Education: The State of the Field. International Journal of Educational Technology in Higher Education, 20(1): 1–22. https://doi.org/10.1186/s41239-023-00392-8
[2] Küchemann S, Avila KE, Dinc Y, et al., 2025, On Opportunities and Challenges of Large Multimodal Foundation Models in Education. npj Science of Learning, 10(1): 1–11. https://doi.org/10.1038/s41539-025-00301-w
[3] Wu X, Zhu P, Zhang J, et al., 2026, ChatGPT’s Impact on Student Learning Outcomes: A Meta-analysis of 35 Experimental Studies. Humanities and Social Sciences Communications, 13(1): 684. https://doi.org/10.1057/s41599-026-07019-z
[4] Hemminki-Reijonen U, Hassan NMAM, Huotilainen M, et al., 2025, Design of generative AI-powered Pedagogy for Virtual Reality Environments in Higher Education. npj Science of Learning, 10(1): 31. https://doi.org/10.1038/s41539-025-00326-1
[5] Tlili A, Shehata B, Adarkwah MA, et al., 2023, What if the Devil is my Guardian Angel: ChatGPT as a Case Study of Using Chatbots in Education. Smart Learning Environments, 10(1): 15. https://doi.org/10.1186/s40561-023-00237-x
[6] Létourneau A, Deslandes Martineau M, Charland P, et al., 2025, A Systematic Review of AI-driven Intelligent Tutoring Systems (ITS) in K-12 Education. npj Science of Learning, 10(1): 29. https://doi.org/10.1038/s41539-025-00320-7
[7] Davis FD, 1989, Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3): 319–340. https://doi.org/10.2307/249008
[8] Venkatesh V, Davis FD, 2000, A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2): 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
[9] Karran AJ, Charland P, Trempe-Martineau J, et al., 2025, Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education. npj Science of Learning, 10(1): 44. https://doi.org/10.1038/s41539-025-00333-2
[10] Su M, Dang B, Nguyen A, et al., 2025, Choice-making in an Adaptive Learning System with Motivational Pedagogical Agents. npj Science of Learning, 10(1): 77. https://doi.org/10.1038/s41539-025-00366-7
[11] Alshammari SH, Alrashidi ME, Alshammari MH, et al., 2025, Determinants of Student Adoption of Artificial Intelligence Applications in Higher Education. Science Reports, 15(1): 35921. https://doi.org/10.1038/s41598-025-19851-5
[12] Yan L, Greiff S, Teuber Z, et al., 2024, Promises and Challenges of Generative Artificial Intelligence for Human Learning. Nature Human Behaviour, 8(10):1839–1850. https://doi.org/10.1038/s41562-024-02004-5
[13] Zhao G, Sheng H, Wang Y, et al., 2025, Generative Artificial Intelligence Amplifies the Role of Critical Thinking Skills and Reduces Reliance on Prior Knowledge while Promoting in-depth learning. Education Sciences, 15(5): 554. https://doi.org/10.3390/educsci15050554.
[14] Tobón S, Luna-Nemecio J, 2021, Complex Thinking and Sustainable Social Development: Validity and Reliability of the COMPLEX-21 Scale. Sustainability, 13(12): 6591. https://doi.org/10.3390/su13126591
[15] Hair JF, Hult GTM, Ringle CM, et al., 2021, Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook, in Classroom Companion: Business. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-80519-7
[16] Fornell C, Larcker DF, 1981, Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1): 39–50. https://doi.org/10.1177/002224378101800104
[17] Henseler J, Ringle CM, Sarstedt M, 2015, A New Criterion for Assessing Discriminant Validity in Variance-based Structural Equation Modeling. Journal of the Academy of Marketing Science, 43(1): 115–135. https://doi.org/10.1007/s11747-014-0403-8
[18] Qiu Y, Lu Y, Xia G, et al., 2025, Leveraging Green AI Technology to Build Sustainable Schools: A Conceptual Model Based on AI Agents. Journal of Electronic Research and Application, 9(2): 150–158. https://doi.org/10.26689/jera.v9i2.9957
