STAKEHOLDERS’ AWARENESS, PERCEPTIONS, UTILIZATION AND OBSERVED CHALLENGES OF ARTIFICIAL INTELLIGENCE INTEGRATION IN BUILDING CONSTRUCTION EDUCATION IN TECHNICAL COLLEGES IN EDO STATE
Keywords:
Artificial Intelligence, Building Construction Education, Stakeholders’ Awareness, Perceptions, Utilization, Observed Challenges, Technical CollegesAbstract
AbstractThis study investigated stakeholders’ awareness, perceptions, utilization, and observed challenges of Artificial Intelligence (AI) integration in Building Construction Education in Technical Colleges in Edo State, Nigeria, using a descriptive survey research design. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), the population comprised 344 stakeholders, including students, teachers, administrators, parents, and employers involved in building construction education in government-owned Technical Colleges in Edo State. Given the manageable size of the population, a census approach was employed for students, teachers, and administrators, while convenience sampling was used to reach parents and employers who were available and willing to participate during data collection. A total of 333 valid responses were retrieved and analyzed. Data were collected using a structured questionnaire titled Stakeholders’ AI Integration in Building Construction Education Questionnaire (SAIBCEQ), with a Cronbach’s alpha reliability coefficient of .82. Mean and standard deviation were used to answer the research questions, while one-way Analysis of Variance (ANOVA) was used to test the hypotheses at the .05 level of significance. Where significant differences existed, Tukey HSD post hoc tests were conducted to identify the specific stakeholder groups responsible for the observed differences. Findings revealed moderate awareness (M = 2.59), favourable perceptions (M = 3.46), very low utilization (M = 1.49), and high perceived challenges (M = 3.60) regarding AI integration among stakeholders. Significant differences existed among stakeholder groups in awareness, perceptions, and utilization, while no significant difference was found in observed challenges. The study recommended improved digital infrastructure, teacher training, and curriculum integration to enhance AI adoption in Technical Colleges in Edo State.
References
### References
Abdulmumin, A., & Adebiyi, R. (2023). Critical factors for effective BIM-enabled education: An adaptive structuration theory perspective. *Buildings, 13*(12), 3044. https://doi.org/10.3390/buildings13123044
Aghimien, D. O., Aigbavboa, C. O., Oke, A. E., & Musenga, C. (2022). Barriers to digitalization in the construction industry: A developing country perspective. *Journal of Engineering, Design and Technology, 20*(2), 453–471. https://doi.org/10.1108/JEDT-03-2021-0145
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, J. M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. *Journal of Building Engineering, 32*, 101827. https://doi.org/10.1016/j.jobe.2020.101827
Ayoola, A. A. (2025). Harnessing artificial intelligence on building technology undergraduates’ skills acquisition for sustainable employability in Oyo State, Nigeria. *Science and Development Journal, 10*(1). https://journals.ug.edu.gh/index.php/sdj/article/view/4717
Babatunde, S. O., Ekundayo, D., & Olanrewaju, A. (2020). Barriers to BIM adoption in Nigeria. *Construction Innovation, 20*(4), 789–805. https://doi.org/10.1108/CI-09-2019-0087
Baker, T., & Smith, L. (2019). *Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges*. Nesta. https://www.nesta.org.uk/report/education-rebooted
Bilal, M., Oyedele, L. O., Akinade, O. O., Ajayi, A. O., Delgado, J. M. D., Owolabi, H. A., … Pasha, M. (2021). Big data architecture for construction waste analytics. *Automation in Construction, 125*, 103234. https://doi.org/10.1016/j.autcon.2021.103234
Bower, M. (2021). Technology-mediated learning theory. *British Journal of Educational Technology, 52*(4), 1642–1659. https://doi.org/10.1111/bjet.13152
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges, and implications of generative conversational AI for research, practice, and policy. *International Journal of Information Management, 71*, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Holmes, W., Bialik, M., & Fadel, C. (2019). *Artificial intelligence in education: Promises and implications for teaching and learning*. Center for Curriculum Redesign. https://curriculumredesign.org/our-work/artificial-intelligence-in-education
Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilizing learning analytics to support study success in higher education: A systematic review. *Computers and Education: Artificial Intelligence, 1*, 100003. https://doi.org/10.1016/j.caeai.2020.100003
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). *Intelligence unleashed: An argument for AI in education*. Pearson. https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/open-ideas/Intelligence-Unleashed-Publication.pdf
National Board for Technical Education. (2025, June 20). *NBTE, in collaboration with COL, hosts groundbreaking Trainer-in-the-*
Downloads
Published
Issue
Section
License
Copyright (c) 2026 STUDIES IN EDUCATION
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.