Sustainable Education and Digital Transformation
Evaluating human–AI collaborative pedagogical model for sustainable skill development in technical education workshops
Victor Arinzechukwu Okanya 1, Japel Onyekachi Asogwa 1 *
More Detail
1 Faculty of Vocational and Technical Education, University of Nigeria, Nigeria
* Corresponding Author
Open Access Full Text (PDF)
ARTICLE INFO

Sustainable Education and Digital Transformation, 2026 - Volume 1 Issue 1, Article No: e43677
https://doi.org/10.33902/SEDT.202643677

Article Type: Research Article

Published Online: 04 Mar 2026

Views: 64 | Downloads: 52

ABSTRACT
The accelerating integration of artificial intelligence (AI) into educational environments has opened new possibilities for enhancing technical skill development, particularly in hands-on workshop-based settings. This study evaluated a Human–AI Collaborative Pedagogical Model designed to promote sustainable skill development among university-level building and woodwork technology education students in Nigeria. Employing a quasi-experimental design with a mixed-methods approach, the research recruited 120 participants drawn from four universities in South East Nigeria. Students were assigned to either a traditional instruction group or an artificial intelligence-supported instruction group over a six-week practical module. Mixed quantitative and qualitative data were collected through validated performance instruments and analyzed using inferential statistics and thematic analysis, respectively. Results demonstrated that students in the artificial intelligence-supported group achieved significantly higher task accuracy, reduced task completion time, lower error frequency, and superior skill transferability compared to their traditionally-instructed counterparts. Thematic analysis further identified the Scaffolded Human–AI Co-instruction Model, characterized by real-time artificial intelligence feedback, instructor mediation, and iterative experiential cycles, as the most effective pedagogical framework for workshop-based technical education. These findings inform sustainable digital transformation in TVET by highlighting how human-centered AI integration can improve skill outcomes while guiding instructor development, implementation governance, and equitable scaling in resource-constrained contexts.
KEYWORDS
AI-supported learning building technology human–AI collaboration pedagogical model sustainable skill development technical education workshop-based pedagogy
In-text citation: (Okanya & Asogwa, 2026)
Reference: Okanya, V. A., & Asogwa, J. O. (2026). Evaluating human–AI collaborative pedagogical model for sustainable skill development in technical education workshops. Sustainable Education and Digital Transformation, 1(1), e43677. https://doi.org/10.33902/SEDT.202643677
In-text citation: (1), (2), (3), etc.
Reference: Okanya VA, Asogwa JO. Evaluating human–AI collaborative pedagogical model for sustainable skill development in technical education workshops. Sustainable Education and Digital Transformation. 2026;1(1), e43677. https://doi.org/10.33902/SEDT.202643677
In-text citation: (1), (2), (3), etc.
Reference: Okanya VA, Asogwa JO. Evaluating human–AI collaborative pedagogical model for sustainable skill development in technical education workshops. Sustainable Education and Digital Transformation. 2026;1(1):e43677. https://doi.org/10.33902/SEDT.202643677
In-text citation: (Okanya and Asogwa, 2026)
Reference: Okanya, Victor Arinzechukwu, and Japel Onyekachi Asogwa. "Evaluating human–AI collaborative pedagogical model for sustainable skill development in technical education workshops". Sustainable Education and Digital Transformation 2026 1 no. 1 (2026): e43677. https://doi.org/10.33902/SEDT.202643677
In-text citation: (Okanya and Asogwa, 2026)
Reference: Okanya, V. A., and Asogwa, J. O. (2026). Evaluating human–AI collaborative pedagogical model for sustainable skill development in technical education workshops. Sustainable Education and Digital Transformation, 1(1), e43677. https://doi.org/10.33902/SEDT.202643677
In-text citation: (Okanya and Asogwa, 2026)
Reference: Okanya, Victor Arinzechukwu et al. "Evaluating human–AI collaborative pedagogical model for sustainable skill development in technical education workshops". Sustainable Education and Digital Transformation, vol. 1, no. 1, 2026, e43677. https://doi.org/10.33902/SEDT.202643677
REFERENCES
  • Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring cognitive and psychological engagement: Validation of the student engagement instrument. Journal of School Psychology, 44(5), 427–445. https://doi.org/10.1016/j.jsp.2006.04.002
  • Asogwa, J. O., Okanya, V. A., Edozie, C. C., & Nnamani, V. S. (2023). Optimizing service delivery in TVET institutions through incentive administration for technical college teachers: A workforce retention agenda. Journal of CUDIMAC, 11(1), 115–125. http://cudimac.unn.edu.ng/volume-11/
  • Asogwa, J. O., Okanya, V. A., & Kanife, E. C. (2024). Entrepreneurial skills required by building technology graduates in building maintenance enterprise for self-reliance in Enugu State. Journal of Centre for Technical Vocational Education, Training and Research, 6(1), 160–168.
  • Atchley, T., Winstead, A., & Cook, T. (2024). Artificial intelligence as a critical collaborator: Metacognition and collaborative AI literacy in higher education. Cognitive Research: Principles and Implications, 9(1), 45. https://doi.org/10.1186/s41235-024-00573-z
  • Bowen, J. A., & Watson, C. E. (2024). Teaching with AI: A practical guide to a new era of human learning. Johns Hopkins University Press.
  • Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
  • Chen, X., Zou, D., Xie, H., & Wang, F. L. (2025). AI-agent-supported collaborative learning for programming education: A quasi-experimental study. Computers & Education, 205, 105120. https://doi.org/10.1016/j.compedu.2024.105120
  • Chung, E. Y. (2019). Facilitating learning of community-based rehabilitation through problem-based learning in higher education. BMC Medical Education, 19(1), 433. https://doi.org/10.1186/s12909-019-1868-4
  • Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business & Information Systems Engineering, 61(5), 637–641. https://doi.org/10.1007/s12599-019-00595-2
  • Fan, Y., Hou, M., & Yang, X. (2025). Collaborative AI literacy and metacognitive laziness: An experimental study on the effects of AI feedback in learning. Educational Technology Research and Development, 73(1), 115–134. https://doi.org/10.1007/s11423-024-10350-x
  • Holstein, K., & Aleven, V. (2022). Designing for human-AI complementarity in K-12 education. AIMagazine, 43(2), 239-248. https://doi.org/10.1002/aaai.12058
  • Izuakor, C. F., Emeghebo, U. M., Nwakile, T. C., Omego, G. N., Odewale, O. T., Attah, B. I., Ezeaku, M. N., & Ogbonna, I. J. (2026). Promotion incentives and the dynamics of research motivation among technical and vocational education and training lecturers in Nigerian universities. Discover Education, 5, 120. https://doi.org/10.1007/s44217-026-01201-w
  • Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice Hall.
  • Lawal, U., Okanya, A. V., Yakubu, A., Ezugwu, C., Emmanuel, E. O., & Ohanu, I. B. (2023). Addressing curriculum challenges in building and woodwork technology programs at Nigerian universities for graduate employability. Industrial Technical Education Journal, 5(1), 104–112.
  • Lee, J. D., & Moray, N. (1994). Trust, self-confidence, and operators' adaptation to automation. International Journal of Human-Computer Studies, 40(1), 153–184. https://doi.org/10.1006/ijhc.1994.1007
  • Li, Y., Gao, B., Zhao, J., & Chen, L. (2025). Human–AI collaboration in hybrid intelligence learning environments: A synergy degree model. Computers & Education: Artificial Intelligence, 7, 100230. https://doi.org/10.1016/j.caeai.2024.100230
  • National Board for Technical Education. (2021). Strategic plan for TVET development in Nigeria (2021–2025). Author.
  • Nwakile, T. C., Ugwuoke, C. U., Okadi, A., Ali, C. C., Nnadi, H. S., Ikehi, M., Ekwueme, S. U., Okanya, V. A., Eya, C. C., & Emeka, O. (2025). Retrospective assessment of academic and psychological readiness of final-year technical and vocational education students in Nigeria after the 2022 ASUU strike. Discover Education, 4, 409. https://doi.org/10.1007/s44217-025-00893-w
  • Nwobodo-Anyadiegwu, E. N., & Okanya, V. A. (2026). Impact of student-centred learning on motivation and learning outcomes among engineering and building technology students in a Nigerian university. Discover Education, 5, 135. https://doi.org/10.1007/s44217-026-01231-4
  • Ogbonna, G. N., & Okanya, V. A. (2023). Strategies required for improving the building construction management skills of building contractors in Enugu State. Journal of Centre for Technical Vocational Education, Training and Research, 5(1), 166–174.
  • Okanya, V. A., Kuwanta, G. I., & Yohanna, K. P. (2023). Enhancing integration of emerging technologies in Technical Vocational Education and Training (TVET) programmes for sustainable development. Industrial Technology Education Research Journal, 6(1), 73–85.
  • Okereke, G. K. O., Okanya, V. A., Joy, O. A., Kontei, E., Victoria, E. C., Dauda, D. K., & Lawal, U. (2025). Sustainable collaborative practices for outcome-based learning in TVET institutions in Enugu State, Nigeria. Industrial Technology Education Research Journal, 8(1), 28–40.
  • Orji, C. T. (2025). Efficacy of problem-based intervention on engagement and practical skills acquisition among electrical/electronic technology education students. Discover Education, 4, 522. https://doi.org/10.1007/s44217-025-00736-8
  • Proença, S., Ribeiro, V. C., & Santos, L. A. (2024). Innovative learning practices as a driver of students' performance. European Conference on Innovation and Entrepreneurship, 19(1), 655–663. https://doi.org/10.34190/ecie.19.1.2628
  • Rabiu, A., Bawa, K., & Saminu, S. (2025). Artificial intelligence adoption for skills development in Nigeria: A systematic review and roadmap for TVET transformation. International Journal of Research and Innovation in Social Science, 9(8), 645–657. https://doi.org/10.47772/IJRISS.2025.90845
  • Rosyadi, M. I., Kustiawan, I., Tetehfio, E. O., & Joshua, Q. (2023). The role of AI in vocational education: A systematic literature review. Journal of Vocational Education Studies, 6(2), 244–263. https://doi.org/10.26555/joves.v6i2.7214
  • Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38. https://doi.org/10.1177/001872675100400101
  • Ukala, C. C., & Iheukwumere, O. C. (2025). Integrating AI in Nigerian TVET institutions: Bridging skills gaps for the workforce. International Journal of Scientific Research in Education, 18(1), 35–43.
  • UNESCO-UNEVOC. (2023). The future of vocational education and training in a changing world. Author.
  • Vincent, D. A., Okanya, A. V., Asogwa, J. O., & Benjamin, U. O. (2022). Appraisal of sensor technology as innovative pedagogy in building technology for improving safety management of building construction sites in Nigeria. International Journal of Engineering Research and Advanced Technology, 8(3), 12–25. https://doi.org/10.31695/IJERAT.2022.8.3
  • Baxter, G., & Sommerville, I. (2011). Socio-technical systems: From design methods to systems engineering. Interacting with Computers, 23(1), 4–17. https://doi.org/10.1016/j.intcom.2010.07.003
  • Clegg, C. W. (2000). Sociotechnical principles for system design. Applied Ergonomics, 31(5), 463–477. https://doi.org/10.1016/S0003-6870(00)00009-0
  • Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the Longwall method of coal-getting. Human Relations, 4(1), 3–38. https://doi.org/10.1177/001872675100400101
  • Ba, S., Yang, L., Yan, Z., Looi, C. K., & Gasevic, D. (2025). Unraveling the mechanisms and effectiveness of AI-assisted feedback in education: A systematic literature review. Computers and Education Open, 9, 100284. https://doi.org/10.1016/j.caeo.2025.100284
  • Cabral, L., Pinto, R. & Gonçalves, G. AI-powered learning analytics dashboards: a systematic review of applications, techniques, and research gaps. Discover Education, 4, 525. https://doi.org/10.1007/s44217-025-00964-y
  • Healey, M., & Jenkins, A. (2000). Kolb’s experiential learning theory and its application in geography in higher education. Journal of Geography, 99(5), 185-195. https://doi.org/10.1080/00221340008978967
  • Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice Hall.
  • Morris, T. H. (2020). Experiential learning—A systematic review and revision of Kolb’s model. Interactive Learning Environments, 28(8), 1744-5191. https://doi.org/10.1080/10494820.2019.1570279
  • Wijnen-Meijer, M., Brandhuber, T., Schneider, A., & Berberat, P. O. (2022). Implementing Kolb’s experiential learning cycle by linking real experience, case-based discussion and simulation. Journal of Medical Education and Curriccular Development, 9, 23821205221091511. https://doi.org/10.1177/23821205221091511
  • Simiyu, C. N., Malanga, K. N., & Wabwoba, F. (2026). Bridging the AI chasm: A strategic framework for generative AI in higher education. Sustainable and Pedagogical Technologies, 2(1), e42185. https://doi.org/10.33902/SPT.202642185
  • Sarkar, R. (2025). Factors affecting the intent to adopt artificial intelligence in learning: An empirical study. Sustainable and Pedagogical Technologies, 1(1), e41094. https://doi.org/10.33902/SPT.202541094
  • Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, 100215. https://doi.org/10.1016/j.caeai.2024.100215
  • OECD. (2023). Opportunities, guidelines and guardrails on effective and equitable use of AI in education. Author.
  • United Nations Educational, Scientific and Cultural Organization [UNESCO]. (2024). The guidelines for the governance of digital platforms and generative artificial intelligence: Companion document. Author.
LICENSE
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.