Assessment of the barriers to AI integration in teacher education programme through Deiph method Nigerian Universities lecturers’ experience
Egbai, Julius Michael1
1Department of Educational Foundations, University of Calabar, Calabar, Nigeria
Eke, Ogbu Eke2
2Ijeoma Ubochi, Dept. of Curriculum & Instruction, Alvan Ikoku University of Education, Owerri, Imo State, Nigeria
Abstract
The study adopted survey design to examine Michael Okpara University OF Agriculture Umudike and Alvan Ikoku University of Education Owerri teacher educators’ experience on the barriers to AI integration in teacher education programme in Nigerian universities through Deiph method. Researchers’ made experience response questionnaire titled” Teacher-educators Questionnaire on the barriers to Ai integration in Teacher education programme in Nigerian Universities using Deiph method” (TQBAIEEP). TQBAIEEP was used for data collection. It had reliability coefficient of 0.8 5 determined using Cronbach Alpha. The data collected was analyzed using mean and standard deviation in answering research questions. The findings showed that teacher educators’ experiences indicate that barriers to AI integration in teacher education in Nigerian universities exist at the institutional, technological, and socio-cultural factors levels. Recommendations were made which span institutional, technological, and socio-cultural dimensions, emphasizing cross-cutting strategies and capacity building. The Deiph method provides a structured framework for diagnosing, evaluating, identifying, and prioritizing the barriers.
Keywords: Assessment, barriers, artificial intelligence, teacher education, Deiph
Impact Statement
The research paper presents a comprehensive assessment of the barriers to the integration of Artificial Intelligence (AI) in teacher education programs within Nigerian universities. The key findings are:
Institutional barriers: The study identifies budget constraints, lack of leadership support, resistance from faculty, and inadequate institutional policies as significant impediments to AI integration.
Technological challenges: Limited access to technology, inadequate infrastructure, digital divide, and insufficient technical expertise among educators are cited as major technological barriers.
Socio-cultural factors: Cultural norms, socio-economic influences, student diversity, and low awareness about AI among educators are recognized as influential socio-cultural barriers.
The impact of this research is significant as it provides a structured and systematic framework for diagnosing, evaluating, identifying, and prioritizing the barriers to AI integration in teacher education programs. This insight is crucial for informing policy decisions, resource allocation, and the development of targeted interventions to overcome the challenges. The findings can guide universities and policymakers in Nigeria and similar contexts to address the multifaceted barriers and facilitate the successful integration of AI in teacher education.
About The Author
EGBAI, Julius Michael Ph.D hails from Akpet Central in Biase Local Government Area of Cross River State of Nigeria. He attended all levels of educational institutions culminating to his attainment of the Doctor of Philosophy in Educational Measurement, Assessment, statistics and Evaluation from the prestigious University of Calabar, Calabar in 2016, having bagged an M.Ed in Educational Measurement and Evaluation both in the same department of Educational Foundations in 2012. He joined the services of the University in 2017 as lecturer ll. He is currently a senior lecturer, lecturing Educational Test and Measurement, Research Method in Education as well as Basic statistics in Education to both degree and post graduate students in the faculties of education in the University of Calabar, Calabar.
Dr Egbai has many academic researched articles published in reputable journals in both local and foreign Journals. As part of his community services, he has served as a Legislative Leader of Biase Local Government Council having earlier served as a Supervisory Councillor for Education in the same Council.
Dr. Ogbu Eke is a seasoned education professional with over 15 years of experience in curriculum development, instructional design, and teacher training. He is a senior lecturer in the Department of Curriculum Studies and Educational Technology, Alvan Ikoku Federal University of Education, Owerri, Imo State Nigeria. He holds a Ph.D. in Curriculum Studies and Instruction and has made significant contributions to the field through his extensive research and publications.
As an active member of prestigious academic organizations such as the Curriculum Organization of Nigeria (CON), World Council for Curriculum and Instruction (WCCI), and the Teacher Registration Council of Nigeria, Dr. Eke is deeply engaged in shaping educational policies and practices. His areas of expertise include integrating technology in language instruction, addressing climate change and sustainable development in the curriculum, and promoting learner autonomy and collaborative learning strategies.
With a strong focus on practical, evidence-based solutions, Dr. Eke has demonstrated his ability to translate research into impactful classroom practices. He is passionate about empowering teachers and developing innovative instructional approaches that foster student success and community engagement. His work has been recognized with several awards, including the Young African Leaders Journal of Development Certificate of Outstanding Contributions in Research and the Students’ Union Government AlvanIkoku Federal College of Education Award of Outstanding Lecturer of the Year.
Dr. Eke’s commitment to educational excellence and his collaborative leadership style make him a valuable asset in any academic or educational institution.
References
Adeleke, Y. O., & Ogunnaike, O. O. (2019). Challenges and opportunities of Blended learning in Nigerian Higher Education. International Journal of Information and Education Technology, 9(10), 768–774.
Anderson, R., & Smith, J. (2018). Artificial intelligence and assessment: A future perspective. Educational Assessment, 55(1), 78–94.
Hasson, F., & Keeney, S. (2011). Enhancing rigour in the Delph technique research. Technological Forecasting and Social Change, 78(9), 1008–1015.
Johnson, L., Becker, S. A., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2018). NMC/CoSN horizon report, 2018 K-12 edition. The New Press Media Consortium.
Smith, A., & Johnson, B. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Educational Technology, 59(6), 5–9.
United Nations Educational, Scientific and Cultural Organization. (2018). Harnessing artificial intelligence for global education. UNESCO.
Wang, L., Chao, E., & Lee, A. Y. (2021). AI in education: A comprehensive review. Educational Technology Research and Development, 69(3), 951–976.
Zhao, Y., & Alvarez-Torres, M. J. (2020). Barriers to the integration of artificial intelligence in education: A sociocultural perspective. Journal of Educational Technology and Society, 23(3), 22–35.
Avurakoghene, O. P., & Oredein, A. O. (2023). Educational leadership and artificial intelligence for sustainable development. Shodh Sari-An International Multidisciplinary Journal, 02(03), 211–223. https://doi.org/10.59231/sari7600
Kumar, S. (2023). Artificial Intelligence Learning and Creativity. Eduphoria, 01(01), 13–14. https://doi.org/10.59231/eduphoria/230402
Kumar, S., & Simran. (2024). Equity in K-12 STEAM education. Eduphoria, 02(03), 49–55. https://doi.org/10.59231/eduphoria/230412