How GenAI Transforms Computer Engineering Education: The Case of the Middle East and North Africa

Authors

  • Mostafa Mohamad Zayed University, College of Interdisciplinary Studies
  • Dababrata Chowdhury Canterbury Christ Church University
  • Christopher Russell Canterbury Christ Church University
  • Ali Balikel İstanbul Kent Üniversitesi
  • Peter Kawalek Loughborough Business School

DOI:

https://doi.org/10.16920/jeet/2025/v39is1/25129

Keywords:

GenAI, Multiple Perspectives Theory, Educational Ecosystem, Computer Engineering, MENA region

Abstract

This research investigates the factors influencing the adoption of GenAI in computer engineering (CE) education within the Middle East and North Africa (MENA) region. The gap in the adoption of GenAI seems to be less pronounced between northern and southern countries compared to other digital technologies. Nevertheless, the academic community has not fully explored the institutional, cultural, and geopolitical factors affecting GenAI adoption. Utilising Harold Linstone's multiple perspectives theory (1981-2019), this study analyses the technical, organisational, and personal viewpoints of 50 CE educators working in higher education institutions in the MENA area. Through an abductive thematic analysis of the interviews and sentiment analysis, the research provides a comprehensive understanding of the GenAIpowered Educational Ecosystem for CE (AEECE) by detailing the essential processes, applications, outcomes, and interactions among the key stakeholders in this ecosystem, including educators, students, university administration, regulators, industry partners, and employers. From a technical standpoint, our findings indicate that GenAI is transforming CE in the region, particularly concerning coding and programming competencies for project management, content creation, and cybersecurity analytics. Other technical insights included innovative practical applications, comprehension of AI's opaque aspects, and limitations in resources. From an organisational viewpoint, we identified that aligning AI investments with the national vision is crucial, as is the need for curriculum reform and addressing resource disparities among various geopolitical contexts. On a personal level, it was noted that educators stepping back from their role as learning facilitators and an excessive reliance on GenAI can undermine critical thinking skills, highlighting the importance of ethical usage and implementation of GenAI applications.

Downloads

Download data is not yet available.

Downloads

Published

2025-09-10

How to Cite

Mohamad, M., Chowdhury, D., Russell, C., Balikel, A., & Kawalek, P. (2025). How GenAI Transforms Computer Engineering Education: The Case of the Middle East and North Africa. Journal of Engineering Education Transformations, 39(is1), 3–19. https://doi.org/10.16920/jeet/2025/v39is1/25129

Issue

Volume 39, Special Issue 1, September 2025

Section

Research Article
Crossref
Scopus
Google Scholar
Europe PMC

References

Acharya, D. B., Kuppan, K., & Divya, B. (2025). Agentic AI: Autonomous Intelligence for Complex Goals–A Comprehensive Survey. IEEE Access.

Awasthy, R., Flint, S., Sankarnarayana, R., & Jones, R. L. (2020). A framework to improve university–industry collaboration. Journal of Industry-University Collaboration, 2(1), 49-62.

Bansal, T. (13th Oct 2023). Does OpenAI’s Non-Profit Ownership Structure Actually Matter?. Forbes, Leadership Strategies. Available at: Does OpenAI’s Non-Profit Ownership Structure Actually Matter? Accessed on 30th April 2025.

Boddington, P. (2023). AI ethics. Singapur: Springer International Publishing, 48.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.

Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A. E., & Rodríguez, M. E. (2021). Artificial intelligence and reflections from educational landscape: A review of AI studies in half a century. Sustainability, 13(2), 800.

Camacho-Zuñiga, C., Rodea-Sánchez, M. A., López, O. O., & Zavala, G. (2024, May). Generative AI Guidelines by/for Engineering Undergraduates. In 2024 IEEE Global Engineering Education Conference (EDUCON) (pp. 1-8). IEEE.

Carter, L., Liu, D., & Cantrell, C. (2020). Exploring the intersection of the digital divide and artificial intelligence: A hermeneutic literature review. AIS Transactions on Human-Computer Interaction, 12(4), 253-275.

Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage Publications.

Drigas, A., Pappas, M., & Lytras, M. (2016). Emerging technologies for ict based education for dyscalculia: Implications for computer engineering education. International journal of engineering education, 32(4), 1604-1610.

Elish, M. C. (2019). Moral crumple zones: Cautionary tales in human-robot interaction (pre-print). Engaging Science, Technology, and Society (pre-print).

Finnie-Ansley, J., Denny, P., Becker, B. A., Luxton-Reilly, A., & Prather, J. (2022, February). The robots are coming: Exploring the implications of openai codex on introductory programming. In Proceedings of the 24th Australasian computing education conference (pp. 1019).

George, A. S. (2023). Preparing students for an AI-driven world: Rethinking curriculum and pedagogy in the age of artificial intelligence. Partners Universal Innovative Research Publication, 1(2), 112-136.

Giannakos, M. N., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernández‐Leo, D., Järvelä, S., Mavrikis, M., & Rienties, B. (2024). The promise and challenges of generative AI in education. Behaviour & Information Technology, 1–27. https://doi.org/10.1080/0144929x.2024.2394886

Gnanaprakasam, J., & Lourdusamy, R. (2024). The role of AI in automating grading: Enhancing feedback and efficiency. In Artificial Intelligence and EducationShaping the Future of Learning. IntechOpen.

Graham, R. (2018). The global state of the art in engineering education. Massachusetts Institute of Technology (MIT) Report, Massachusetts, USA.

Gutiérrez, J. L. M. (2024). On actor-network theory and algorithms: ChatGPT and the new power relationships in the age of AI. AI and Ethics, 4(4), 1071-1084.

Han, Y., & Moghaddam, M. (2021). Analysis of sentiment expressions for user-centered design. Expert Systems with Applications, 171, 114604.

Linstone, H. A. (1999). Decision Making for Technology Executives: Using Multiple Perspectives to Improved Performance. Artech House.

Linstone, H. A. (2019). The Management of Technology: Old and New Perspectives. In Natural Resources And Development In Arid Regions (pp. 265-292). Routledge.

Linstone, H. A., & Mitroff, I. I. (1994). The challenge of the 21st century: Managing technology and ourselves in a shrinking world. Suny Press.

Lythreatis, S., Singh, S. K., & El-Kassar, A. N. (2022). The digital divide: A review and future research agenda. Technological Forecasting and Social Change, 175, 121359.

Mellqvist, N., & Mozelius, P. (2024). Exploring Student Perspectives on Generative AI in Requirements Engineering Education. Proceedings of the International Conference on AI Research., 4(1), 473– 481. https://doi.org/10.34190/icair.4.1.3136

Mills, J. E., & Treagust, D. F. (2003). Engineering education: Is problem-based or project-based learning the answer? Australasian Journal of Engineering Education, 3(2), 2-16.

Ministry of State for Artificial Intelligence (MoSAI). AI Ethics Principles & Guidelines. Available at: MOCAI-AIEthicsEN-1.pdf [Accessed in 20th of March 2025].

MIT-Solan-Middel East. (2023) The Middle East Region is Preparing for a Future Tech Ecosystem. Available at: https://www.mitsloanme.com/article/the-middle-eastregionis-preparing-for-a-future-tech-ecosystem/ [Accessed in 4th of May 2025].

Mohamad, M. (2015). Multiple Perspective of Mobile Money System Development: Action Case of e-Masary Wallet (Doctoral dissertation, University of Manchester).

Mohamad, M. (2025). Technological Innovation Ecosystem for Sustainable Development: Multi-cases from Federal Universities in UAE. In Universities and the SDGs: Empowering Change in the MENA Region.

Mohamad, M., & Eldarmi, A. (2020). Activity theory as a lens for Corporate Managerial Innovation: A Proposed Systematic Framework for Military Managerial Innovation. Proceedings of the British Academy of Management. Available at: https://virtual.oxfordabstracts.com/event/1520/submission/671

Moor, J. H. (2020). What is computer ethics?. In The ethics of information technologies (pp. 15-24). Routledge.

Pardos, Z. A., & Dadu, A. (2020). Sentiment analysis of MOOC discussion forums: What it can tell us about the student experience. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 442-451).

Phillips, F., & Linstone, H. (2016). Key ideas from a 25-year collaboration at technological forecasting & social change. Technological Forecasting and Social Change, 105, 158-166.

Pochtovyuk, A., Buriak, I., Buriak, M., & Zakharova, O. (2020, September). High Engineering Education as a Component of the System of Human Capital Development. In 2020 IEEE Problems of Automated Electrodrive. Theory and Practice (PAEP) (pp. 1-5). IEEE.

Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22.

Sahoo, K., Samal, A. K., Pramanik, J., & Pani, S. K. (2019). Exploratory data analysis using Python. International Journal of Innovative Technology and Exploring Engineering, 8(12), 4727-4735.

Sailun, Z., & Alhajj, R. (2019). Sentiment analysis in education: A review. Knowledge and Information Systems, 60(1), 1-31.

Selwyn, N. (2021). Education and technology: Key issues and debates. Bloomsbury Publishing.

Siemens, G., & Baker, R. S. D. (2012, April). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254).

Somasundaram, M., Latha, P., & Pandian, S. S. (2020). Curriculum design using artificial intelligence (AI) back propagation method. Procedia Computer Science, 172, 134-138.

Sottilare, R., & VanLehn, K. (2020). Intelligent tutoring systems swot analysis. Design Recommendations for Intelligent Tutoring Systems, 27.

Umanets, V., Shakhina, I., & Rozputnia, B. (2024). Training future computer science teachers to use artificial intelligence technologies in the educational process. Modern Information Technologies and Innovation Methodologies of Education in Professional Training Methodology Theory Experience Problems, (72), 162-169.

Vassilakopoulou, P., & Hustad, E. (2023). Bridging digital divides: A literature review and research agenda for information systems research. Information Systems Frontiers, 25(3), 955-969.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39.

Zhang, Z., & Chang, Y. (2024). Leveraging Generative AI to Enhance Engineering Education At Both Low-Level And High-Level Study. In 2024 Fall ASEE Mid-Atlantic Section Conference.

Most read articles by the same author(s)