Authors :
Osaji, Obiazi Augusta; Reagan Nnabio Robinso; Orji, Temple Chigozilem
Volume/Issue :
Volume 10 - 2025, Issue 11 - November
Google Scholar :
https://tinyurl.com/2s4bhxez
Scribd :
https://tinyurl.com/3jd2yhpw
DOI :
https://doi.org/10.38124/ijisrt/25nov721
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Abstract :
Despite the increasing relevance of Artificial Intelligence (AI) in education globally, its application in enhancing
instructional delivery and practical skill acquisition in Building Technology programmes in Nigerian tertiary institutions
remains limited and underexplored. This study investigated the impact of artificial intelligence tools for enhancing
instructional delivery and skill acquisition in building technology in tertiary institutions in Rivers State. Specifically, the
study investigated the impact of Intelligent Tutoring Systems on instructional delivery in Building Technology programmes
and the impact of AI-powered Building Information Modelling on students’ practical skills development in Building
Technology programmes in tertiary institutions in Rivers State. To that end, to research questions and two hypotheses were
formulated and tested at .05 level of significance. The study adopted a descriptive survey research design, using a validated
questionnaire administered to 50 Building Technology lecturers and 30 building consultants in construction firms in Rivers
State. The reliability coefficient was determined using Cronbach’s Alpha, with a coefficient value of .78 and .81 for research
questions 1 and 2 respectively. Data collected were analyzed using mean, standard deviation, and independent samples z-
test. The findings revealed that both lecturers and students strongly agreed that Intelligent Tutoring Systems supports
personalized learning, simplifies complex concepts, and bridges theory-practice gaps, while AI-powered Building
Information Modelling enhances practical design competence, collaboration, and employability. No significant differences
were found between the responses of lecturers and students, indicating shared positive perceptions of these AI tools. It is
recommended that institutions provide adequate digital infrastructure and regular training for lecturers to ensure effective
implementation of AI in Building Technology education.
Keywords :
Artificial Intelligence, Intelligent Tutoring Systems, Building Information Modelling, Building Technology, Tertiary Institutions.
References :
- Aliyu, M. A., & Alhassan, M. A. (2024). Emerging technologies and the future of technical and vocational education in Nigeria: The role of artificial intelligence. Journal of Technical Education and Training, 16(1), 45–58. https://doi.org/10.30880/jtet.2024.16.01.005
- Al-Smadi, M., & Guetl, C. (2022). Intelligent tutoring systems and student performance: A systematic review. Education and Information Technologies, 27(2), 1345–1365.
- Azhar, S., Khalfan, M., & Maqsood, T. (2012). Building Information Modeling (BIM): Now and beyond. Australasian Journal of Construction Economics and Building, 12(4), 15–28. https://doi.org/10.5130/AJCEB.v12i4.3032
- Chen, H.-R., & Tsai, M.-J. (2023). Intelligent tutoring systems in higher education: A review of applications and effects. Computers & Education, 193, 104657.
- Musa, M. F., Abdul-Aziz, A.-R., & Dania, A. A. (2023). BIM integration in construction education: Impact and challenges. Journal of Construction Education, 45(1), 77–92.
- Nwogu, L. N., & Ibeneme, O. T. (2023). Adoption of artificial intelligence in Nigerian vocational education: Opportunities and constraints. International Journal of Vocational and Technical Education Research, 9(2), 15–26.
- Ogbuanya, T. C., & Usoro, A. A. (2023). Digital competence and technology integration readiness among TVET teachers in Nigerian technical colleges. International Journal of Technology and Design Education, 33(2), 567–584. https://doi.org/10.1007/s10798-022-09728-2
- Oke, A. E., & Aghimien, D. O. (2021). Integrating BIM into higher education curricula: A Nigerian perspective. International Journal of Construction Education and Research, 17(2), 123–139.
- Okonkwo, I. R., & Obi, A. I. (2021). E-learning and intelligent systems for TVET in Nigeria: An exploratory study. Journal of Technical Education and Training, 13(3), 1–11.
- Okoye, K. R. E., Chikwendu, N. N., & Anierobi, E. I. (2024). Innovative pedagogies for technical education in Nigeria: The place of artificial intelligence and virtual reality. Journal of Technical Education, 56(1), 101–118.
- Olatunji, O. A. (2020). Barriers to BIM adoption in developing countries: Nigeria’s experience. Journal of Construction Project Management and Innovation, 10(1), 2352–2368.
- Russell, S., & Norvig, P. (2022). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
- Udofia, U. I., Akpan, J. P., & Etukudo, E. I. (2022). Strategies for bridging skill gaps in building technology through innovative instructional approaches. Nigerian Journal of Industrial and Technical Education, 9(1), 92–102.
- UNESCO. (2022). Artificial intelligence in education: Opportunities and challenges for sustainable development. United Nations Educational, Scientific and Cultural Organization.
15. VanLehn, K. (2019). Intelligent tutoring systems: An overview. In F. Fischer, C. E. Hmelo-Silver, S. R. Goldman, & P. Reimann (Eds.), International Handbook of the Learning Sciences (pp. 259–271). Routledge.
Despite the increasing relevance of Artificial Intelligence (AI) in education globally, its application in enhancing
instructional delivery and practical skill acquisition in Building Technology programmes in Nigerian tertiary institutions
remains limited and underexplored. This study investigated the impact of artificial intelligence tools for enhancing
instructional delivery and skill acquisition in building technology in tertiary institutions in Rivers State. Specifically, the
study investigated the impact of Intelligent Tutoring Systems on instructional delivery in Building Technology programmes
and the impact of AI-powered Building Information Modelling on students’ practical skills development in Building
Technology programmes in tertiary institutions in Rivers State. To that end, to research questions and two hypotheses were
formulated and tested at .05 level of significance. The study adopted a descriptive survey research design, using a validated
questionnaire administered to 50 Building Technology lecturers and 30 building consultants in construction firms in Rivers
State. The reliability coefficient was determined using Cronbach’s Alpha, with a coefficient value of .78 and .81 for research
questions 1 and 2 respectively. Data collected were analyzed using mean, standard deviation, and independent samples z-
test. The findings revealed that both lecturers and students strongly agreed that Intelligent Tutoring Systems supports
personalized learning, simplifies complex concepts, and bridges theory-practice gaps, while AI-powered Building
Information Modelling enhances practical design competence, collaboration, and employability. No significant differences
were found between the responses of lecturers and students, indicating shared positive perceptions of these AI tools. It is
recommended that institutions provide adequate digital infrastructure and regular training for lecturers to ensure effective
implementation of AI in Building Technology education.
Keywords :
Artificial Intelligence, Intelligent Tutoring Systems, Building Information Modelling, Building Technology, Tertiary Institutions.