Advancing Higher Education through Artificial Intelligence (AI): A Framework for Teaching, Assessment, and Research Integration

Olasunkanmi Opeoluwa Adeoye; Akindapo Abass Alimi; Olugbenga Solomon Agboola; Akinyinka Tosin Akindele; Oladiran Tayo Arulogun; Ganiyu Ojo Adigun

doi:10.37284/eajes.8.2.2946

Olasunkanmi Opeoluwa Adeoye Ladoke Akintola University of Technology
Akindapo Abass Alimi Ladoke Akintola University of Technology
Olugbenga Solomon Agboola Ladoke Akintola University of Technology
Akinyinka Tosin Akindele Ladoke Akintola University of Technology
Oladiran Tayo Arulogun Miva Open University
Ganiyu Ojo Adigun Ladoke Akintola University of Technology

DOI: https://doi.org/10.37284/eajes.8.2.2946

Keywords: AI, AI Framework, Higher Educational Institution, AIED, LLM

Share Article:

Abstract

The benefits of using Artificial Intelligence (AI) in higher education cannot be overstated. Several studies have demonstrated its importance in improving explicit teaching and learning, argumentation and decision-making, assessment and curriculum design, and other research efforts. However, insufficient comprehensive frameworks for guiding effective usage result in abuse, bias, fragmented efforts, and inconsistent outcomes. The study population comprises approximately 6,000 people, including academic staff and students from the Ladoke Akintola University of Technology (LAUTECH) Open and Distance Learning Centre in Ogbomoso. The sample size of 362 was determined using Cochran’s formula. However, only 333 participants completed the questionnaire. The study employed convenience sampling and informed consent for respondents, analysing closed-ended data using descriptive analysis and open-ended data using thematic analysis to identify patterns and subthemes. The results revealed mixed opinions. Respondents emphasised the advantages of AI in terms of contextualised, tailored teaching content, efficient assessment, and research support. On the other hand, academic staff expressed concerns about overreliance on technology, a lack of human oversight, insufficient infrastructure, and data privacy issues. These findings underscore the need for transparent, balanced, and accountable AI integration that mitigates unethical use while preserving essential human elements. As a result, a framework was created to guide the ethical and practical use of AI, ensuring higher educational quality and innovation while preserving academic integrity and inclusivity.

Downloads

Download data is not yet available.

References

Allen, L. K., & Kendeou, P. (2023). ED-AI Lit: An Interdisciplinary Framework for AI Literacy in Education. Policy Insights from the Behavioral and Brain Sciences, 11(1), 3–10. https://doi.org/10.1177/23727322231220339

Anas, M. R. A., & Belle, W. (2023). AI as a Partner in Learning: A Novel Student-in-the-Loop Framework for Enhanced Student Engagement and Outcomes in Higher Education. Proceedings of the 24th Annual Conference on Information Technology Education, 171–172. https://doi.org/10.1145/3585059.3611405

Black, N. B., George, S., Eguchi, A., Dempsey, J. C., Langran, E., Fraga, L., Brunvand, S., Howard, N., Diego, S., & Word, I. (2024). A Framework for Approaching AI Education in Educator Preparation Programs. AAAI Conference on Artificial Intelligence, 38, 23069–23077. https://doi.org/10.1609/aaai.v38i21.30351

Carmichael, T., & Hadžikadić, M. (2019). The Fundamentals of Complex Adaptive Systems. In T. Carmichael, A. J. Collins, & M. Hadžikadić (Eds.), Complex Adaptive Systems (pp. 1–16). Springer International Publishing. https://doi.org/10.1007/978-3-030-20309-2_1

Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00408-3

Chang, Q., Pan, X., Manikandan, N., & Ramesh, S. (2022). Artificial Intelligence Technologies for Teaching and Learning in Higher Education. International Journal of Reliability, Quality and Safety Engineering, 29(05), 2240006. https://doi.org/10.1142/S021853932240006X

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. 20(1). https://doi.org/10.1186/s41239-023-00392-8

Doraid Dalalah & Osama M.A. Dalalah. (2023). The false positives and false negatives of generative AI detection tools in education and academic research: The case of ChatGPT. 21(2), 100822–100822. https://doi.org/10.1016/j.ijme.2023.100822

Drach, I., Petroye, O., Borodiyenko, O., Reheilo, I., Bazeliuk, O., Bazeliuk, N., & Slobodianiuk, O. (2023). The Use of Artificial Intelligence in Higher Education. International Scientific Journal of Universities and Leadership, 15, 66–82. https://doi.org/10.31874/2520-6702-2023-15-66-82

Goosen, W., & Mugumo, P. V. (2024). A framework for the application of AI in higher education in association with APPETD and Swiss Institute for Management and Innovation. In J. Jaiswal (Ed.), International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024) (Vol. 13188, pp. 57–70). https://doi.org/10.1117/12.3030740

Hong, Y., Nguyen, A., Dang, B., & Nguyen, B.-P. T. (2022). Data Ethics Framework for Artificial Intelligence in Education (AIED). 2022 International Conference on Advanced Learning Technologies (ICALT), 297–301. https://doi.org/10.1109/ICALT55010.2022.00095

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, Stepha Krusche, Gitta Kutyniok, Tilman Michaeli, Claudia Nerdel, Jürgen Pfeffer, Oleksandra Poquet, Michael Sailer, Albrecht Schmidt, Tina Seidel, … Gjergji Kasneci. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. 103, 102274–102274. https://doi.org/10.1016/j.lindif.2023.102274

Kshetri, N. (2020). Artificial Intelligence in Developing Countries. IT Professional, 22(4), 63– 68. https://doi.org/10.1109/MITP.2019.2951851

Lukianets, H., & Lukianets, T. (2023). Promises And Perils Of Ai Use On The Tertiary Educational Level. Grail of Science, 25, 306–311. https://doi.org/10.36074/grail-of-science.17.03.2023.053

Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology–Organisation–Environment (TOE) Framework. Buildings, 12(2), 90. https://doi.org/10.3390/buildings12020090

Neely, K. (2015). Complex adaptive systems as a valid framework for understanding community level development. Development in Practice, 25(6), 785–797. https://doi.org/10.1080/09614524.2015.1060949

Olari, V., & Romeike, R. (2021). Addressing AI and Data Literacy in Teacher Education: A Review of Existing Educational Frameworks. Workshop in Primary and Secondary Computing Education, 1–2. https://doi.org/10.1145/3481312.3481351

OpenAI. (2019). Better Language Models and Their Implications. https://web.archive.org/web/20201219132206/https://openai.com/blog/better-language-models/

Poornesh, M. (2024). Through a Teacher’s Lens: Combating Bias in AI-Powered Education for a Just Future. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 97(4), 119– 124. https://doi.org/10.1080/00098655.2024.2393153

Sallu, S., Novdin Manoktong Sianturi, Bambang Purwoko, Yudhi Herliansyah, & Melda Agnes Manuhutu. (2023). Learning in Higher Education Based on Artificial Intelligence (AI) with Case-Based Reasoning (CBR). Journal of Namibian Studies : History Politics Culture, 34, 1049–1064. https://doi.org/10.59670/jns.v34i.1191

Samaradiwakara, G. D. M. N., & Gunawardena, C. G. (2014). Comparison of Existing Technology Acceptance Theories and Models to Suggest A Well-Improved Theory/Model. International Technical Sciences Journal, 1(1), 21–37.

Stokel-Walker, C. (2022). AI bot ChatGPT writes smart essays—Should academics worry? Nature. https://doi.org/10.1038/d41586-022-04397-7

Thongprasit, J., & Wannapiroon, P. (2022). Framework of Artificial Intelligence Learning Platform for Education. International Education Studies, 15(1), 76–76. https://doi.org/10.5539/ies.v15n1p76

Thorp, H. H. (2023). ChatGPT is fun, but not an author. 379(6630), 313–313. https://doi.org/10.1126/science.adg7879

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. 10(1). https://doi.org/10.1186/s40561-023-00237-x

Ueland, J. S., Hinds, T. L., & Floyd, N. D. (2021). Equity at the edge of chaos: Applying complex adaptive systems theory to higher education. New Directions for Institutional Research, 2021(189–192), 121–138. https://doi.org/10.1002/ir.20356

Viberg, O., Kizilcec, R. F., Wise, A. F., Jivet, I., & Nixon, N. (2024). Advancing equity and inclusion in educational practices with AI-powered educational decision support systems ( AI ‐ EDSS ). British Journal of Educational Technology, 55(5), 1974–1981. https://doi.org/10.1111/bjet.13507

Zhai, X. (2022). ChatGPT User Experience: Implications for Education. https://doi.org/10.2139/ssrn.4312418