Vol. 2 No. 1 (2027): AI Literacy, Workforce Learning, and Human–AI Futures
Articles

Enhancing vocational students’ professional competencies through AI agent-supported human–AI collaborative learning : A longitudinal mixed-methods study

PDF
  • Huizhu Tan
Huizhu Tan
Shenzhen Polytechnic University
DOI: https://doi.org/10.46787/ijaipil.v2i1.7311

Published 2026-05-08

Keywords

  • Generative artificial intelligence, AI agents, human–AI collaboration, vocational education, instructional quality, cross-border e-commerce

How to Cite

Tan, H. (2026). Enhancing vocational students’ professional competencies through AI agent-supported human–AI collaborative learning : A longitudinal mixed-methods study. International Journal of AI in Pedagogy, Innovation, and Learning Futures, 2(1). https://doi.org/10.46787/ijaipil.v2i1.7311

Copyright (c) 2026 Huizhu Tan

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

While generative AI (GenAI) integration in education often remains at the tool-assistance level, this study proposes and evaluates an AI agent-based collaborative teaching system designed to operationalize structured human-AI interaction in vocational training. Utilizing a multi-agent architecture featuring role-specialized agents—customer simulation, decision support, and instructional guidance—the system implements a triadic framework that enables coordinated engagement among students, instructors, and AI agents across the entire learning cycle. A mixed-method longitudinal evaluation of a "Cross-Border E-Commerce Customer Management" course (2022–2025, N=232) revealed a significant upward trend in instructional quality, with average teaching evaluation scores rising from 90.58 to 93.81. These results demonstrate that agent-oriented design and iterative optimization effectively transform GenAI from an auxiliary tool into a collaborative educational partner. Despite current technical limitations in emotional intelligence, the proposed architecture offers a replicable, human-centered framework for interactive competency development and instructional optimization in vocational education and related skill-based domains.

PDF

References

  1. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
  2. Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001.
  3. Hwang, Y., & Lee, J. H. (2025). Exploring students’ experiences and perceptions of human-AI collaboration in digital content making. International Journal of Educational Technology in Higher Education, 22, Article 44.
  4. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Instruction, 84, 101733.
  5. Kolb, A. Y., & Kolb, D. A. (2005). Learning styles and learning spaces: Enhancing experiential learning in higher education. Academy of Management Learning & Education, 4(2), 193–212.
  6. Lu, Y., & Tang, X. Y. (2025). Hierarchical levels and progression pathways of generative AI-empowered classroom instruction. Research in Educational Technology, 46(06), 75-82+106. (Original work published in Chinese)
  7. Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL IOE Press.
  8. Razmerita, L. (2024). Human-AI collaboration: A student-centered perspective of generative AI use in higher education. In F. Moreira (Ed.), Proceedings of the 23rd European Conference on e-Learning (ECEL 2024) (pp. 320–329). Academic Conferences and Publishing International.
  9. Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582-599.
  10. UNESCO. (2023). Guidance for generative AI in education and research. United Nations Educational, Scientific and Cultural Organization.
  11. Wang, W., & Deng, Y. C. (2025). Symbiotic logic and practical pathways for human-machine composite educators. Journal of Central China Normal University (Humanities and Social Sciences Edition), 64(04), 90-99. (Original work published in Chinese)
  12. Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2023). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90–112.
  13. Yi, K. Y., & Han, X. B. (2025). From blended learning to human-AI collaborative teaching: New pedagogical forms under generative AI transformation. China Distance Education, 45(04), 85-98. (Original work published in Chinese)
  14. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.