Skip to main content Skip to journal navigation menu Skip to site footer
contact@ukscip.com
Article

Empowering Dual Teacher Classrooms in Vocational Education with Intelligent Agents: Challenges and Coping Strategies

Kaixuan Wang ORCID ,  Di Song ORCID ,  Xiaoli Zhao ORCID ,  Xiaoyang Liu ORCID ,  Yong Wang
    Received: 23 June 2026; Revised: 6 July 2026; Accepted: 22 July 2026; Published: 28 July 2026

    Abstract

    Discussions of intelligent agents in vocational education often hastily equate tool access with overall teaching efficiency, which is not supported by this study’s interview data. The practical adoption of intelligent agents is explored in technical and vocational education and training dual-teacher classrooms from four stakeholders, including dual teachers, industry experts, students and administrators. The analysis differentiates streamlined routine tasks from new burdens arising from content validation, technical interpretation and risk assessment. Specifically, fifteen semi-structured interviews are conducted across secondary and higher vocational institutions. Firstly, all interviews are recorded and transcribed in Chinese. AI-assisted English translations of these transcripts form the primary coding corpus. The key citations are cross-referenced against the original Chinese transcripts. Besides, the constraints–mechanisms–enablers–outcomes (CMEO) framework is generated through the iterative thematic coding via NVivo 14. The results demonstrate that the respondents acknowledged limited efficiency gains only in repetitive tasks, such as material drafting and error screening. Nevertheless, intelligent agents created additional workloads, where practitioners have to verify technical accuracy and adapt artificial intelligence (AI) outputs to local training equipment and safety standards with special AI content interpretation for students. Within high-risk workshop training environments, AI outputs are only permissible when teachers and experts hold final decision-making power. Therefore, this study redefines AI teaching efficiency as bounded and multi-stakeholder professional judgment, rather than a neutral and universally applicable indicator. The constructed CMEO framework extends existing single-perspective AI education research and provides targeted institutional governance strategies for local vocational digital teaching reform.

    Keywords

    References

    1. 1. Drobin, A.; Zarishniak, I.; Sharan, O.; et al. The Impact of Digital Technology on Educational Reform: The Role of Artificial Intelligence in Education. Brain Broad Res. Artif. Intell. Neurosci. 2025, 16, 31–41.
    2. 2. Biru, S. Artificial Intelligence for the Simplification of Bilateral and Syndicated Loans: An Integrated Framework and Empirical Evaluation. J. Adv. Artif. Intell. 2025, 2, 8–16.
    3. 3. Sun, X.; Tian, H. AI-Enhanced Skill Assessment in Higher Vocational Education: A Systematic Review and Meta-Analysis. Informatics 2026, 13, 20.
    4. 4. Long, Y.; Zhang, X.; Zeng, X. Application and Effect Analysis of Virtual Reality Technology in Vocational Education Practical Training. Educ. Inf. Technol. 2025, 30, 9755–9786.
    5. 5. Liu, Y.; Zhan, Q.; Zhao, W. A Systematic Review of VR/AR Applications in Vocational Education: Models, Affects, and Performances. Interact. Learn. Environ. 2024, 32, 6375–6392.
    6. 6. Liu, F. Empowering Education in Western China with AI Technology: A Case Study from Gansu Province. J. Educ. Humanit. Soc. Sci. 2025, 61, 395–401.
    7. 7. Liu, H.; Liu, X. The Role of Advanced Algorithms in Supporting Vocational Education Teachers’ Decision-Making in Intelligent Education Systems. Int. J. High Speed Electron. Syst. 2026, 35, 2540572.
    8. 8. Yang, Y.; Zhang, H.; Jiang, Y. An Intelligent Teaching Assistant System for Enhanced Online Engineering Education: A Dual-Teacher Model. Comput. Appl. Eng. Educ. 2025, 33, e70098.
    9. 9. Li, Z.; Chen, J.; Li, Q. The Relationship between Teacher Professional Development and Students’ Social and Emotional Skills: Examining the Mediating Roles of Teacher Self-Efficacy and Teaching Practices. Curr. Psychol. 2025, 44, 12661–12678.
    10. 10. Masoumi, D.; Noroozi, O. Developing Early Career Teachers’ Professional Digital Competence: A Systematic Literature Review. Eur. J. Teach. Educ. 2025, 48, 644–666.
    11. 11. Decius, J.; Decius, L.; Beausaert, S. Integrating Multiple Theoretical Perspectives on Informal Field-Based Learning: The Self-Regulated Informal Learning Cycle (SILC). Hum. Resour. Dev. Rev. 2025, 24, 247–278.
    12. 12. Wang, D.; Huang, X. Transforming Education through Artificial Intelligence and Immersive Technologies: Enhancing Learning Experiences. Interact. Learn. Environ. 2025, 33, 4546–4565.
    13. 13. Sakr, A.; Abdullah, T. Virtual, Augmented Reality and Learning Analytics Impact on Learners, and Educators: A Systematic Review. Educ. Inf. Technol. 2024, 29, 19913–19962.
    14. 14. Saks, K.; Hunt, P.; Leijen, Ä.; et al. From Zero Collaboration to Teamwork: Forms of Teacher Collaboration and Factors That Support or Hinder It. Educ. Sci. 2025, 15, 87.
    15. 15. Su, X.; Xu, X.; Wang, T. Innovation of Teaching and Learning Scenes and Models Empowered by Artificial Intelligence: Practice and Experience of AI-Powered Programming Courses. Front. Digit. Educ. 2026, 3, 4.
    16. 16. Duggleby, W.; Charest, M.; Pritchard, W. Interpretive Description as a Qualitative Research Framework in Speech-Language Pathology: A Scoping Review. Int. J. Speech-Lang. Pathol. 2026, 28, 245–257.
    17. 17. López-Santana, M.; Tanca, D.E. Comparative Policy Analysis, Subnational Research, and Small-N Qualitative Research: Research Design Considerations. J. Comp. Policy Anal.: Res. Pract. 2024, 26, 567–584.
    18. 18. David, A.; Terstriep, J. Against All Odds—Migrant Entrepreneurs in Entrepreneurial Ecosystems with Constraints. J. Enterp. Communities 2025, 19, 248–275.
    19. 19. Amirah, N.A.; Him, N.F.M.; Rashid, A.; et al. Fostering a Safety Culture in Manufacturing through Safety Behavior: A Structural Equation Modelling Approach. J. Saf. Sustain. 2024, 1, 108–116.
    20. 20. Bouguettaya, S.; Zedadra, O.; Pupo, F.; et al. Toward Collaborative and Adaptive Learning: A Survey of Multi-Agent Reinforcement Learning in Education. Artif. Intell. Sci. Eng. 2026, 2, 1–19.
    21. 21. Alaeifard, M.; Safaei, M.; Zadeh, E.K. Advancing Human-Agent Interaction: Bridging the Gap between Vision and Reality. Int. J. Adv. Hum. Comput. Interact. 2024, 3, 23–32.
    22. 22. Chen, A.; Wei, Y.; Le, H.; et al. Learning by Teaching with ChatGPT: The Effect of Teachable ChatGPT Agent on Programming Education. Br. J. Educ. Technol. 2026, 57, 163–184.
    23. 23. Yue, M.; Jong, M.S.-Y.; Dai, Y.; et al. Students as AI Literate Designers: A Pedagogical Framework for Learning and Teaching AI Literacy in Elementary Education. J. Res. Technol. Educ. 2026, 58, 593–614.
    24. 24. Yu, J.-F.; Zhang-Li, D.; Zhang, Z.-Y.; et al. From MOOC to MAIC: Reimagine Online Teaching and Learning through LLM-Driven Agents. J. Comput. Sci. Technol. 2026, 41, 394–414.
    25. 25. Song, Y.; Kim, J.; Liu, Z.; et al. Students’ Perceived Roles, Opportunities, and Challenges of a Generative AI-Powered Teachable Agent: A Case of Middle School Math Class. J. Res. Technol. Educ. 2026, 58, 535–554.
    ×