iCDEEr™ (Integrated Core–Micro-Core, Digital Capability, Entrepreneurship, Ethics and Reform Curriculum): An AI-Enabled Higher Education Ecosystem for Curriculum, Competency, Learning, and Academic Intelligence
DOI:
https://doi.org/10.59944/postaxial.v4i3.1969Keywords:
Higher Education, AI In Education, Curriculum Intelligence, Competency, Learning Analytics, Educational Ecosystem, Responsible AIAbstract
Higher education requires increasingly integrated use of educational evidence for curriculum improvement, competency development, learning and assessment, student support, and academic quality. The iCDEEr™ framework proposes a human-governed AI-enabled higher education ecosystem that connects these functions through a curriculum-and-competency core, learning and assessment evidence, institutional intelligence, AI-supported recommendations, human academic decision-making, implementation, outcomes, and continuous quality improvement. This article develops the framework as a conceptual research architecture rather than an empirical effectiveness model. Using structured conceptual synthesis of the iCDEEr™ Collaborative Research Master Framework and selected literature on AI in education, learning analytics, educational data, and responsible AI, the article identifies eight provisional intelligence domains: curriculum, competency, research, quality, career, academic scheduling, student support, and governance intelligence. The education-focused interpretation places curriculum, competency, learning, assessment, student support, and quality improvement at the centre while retaining the wider institutional domains as supporting functions. The article also specifies a Core–Micro-Core architecture, a human-governed AI decision cycle, potential educational outcomes, and a staged validation pathway from expert review and instrument development to pilot, multi-university, implementation, and longitudinal studies. The framework does not claim causal effects or empirical effectiveness. Its contribution is a structured research language for investigating how educational evidence and AI-supported intelligence can inform higher education decisions while preserving human authority, transparency, privacy, and contextual interpretation.
References
Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17, 42. https://doi.org/10.1186/s41239-020-00218-x
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, 4. https://doi.org/10.1186/s41239-023-00436-z
Baker, R. S. J. d., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice (pp. 61–75). Springer.
Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59, 64–71. https://doi.org/10.1007/s11528-014-0822-x
Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68, 1961–1990. https://doi.org/10.1007/s11423-020-09788-z
Jiang, X., & Abdullah, Z. (2026). AI-enabled governance in higher education: A systematic review of applications, outcomes, and emerging implications. Frontiers in Education, 11, 1856440. https://doi.org/10.3389/feduc.2026.1856440
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.
Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: Guidance for policy-makers. UNESCO. https://doi.org/10.54675/PCSP7350
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO.
Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450. https://doi.org/10.1111/bjet.12152
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252–254. https://doi.org/10.1145/2330601.2330661
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366
U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. U.S. Department of Education.
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027
West, D., Huijser, H., & Heath, D. (2016). Putting an ethical lens on learning analytics. Educational Technology Research and Development, 64, 903–922. https://doi.org/10.1007/s11423-016-9464-3
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1793994
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, 39. https://doi.org/10.1186/s41239-019-0171-0
Alenezi, A. F. (2026). Reframing artificial intelligence and learning analytics in emerging higher education: Towards a human-centred and contextually responsive future. Frontiers in Education, 11, 1856437. https://doi.org/10.3389/feduc.2026.1856437
Razami, H. H., Singh, A. D., Sampedro, G. A. R., Kovilpillai, J. J. S., Raza, F. A., Anwar, R., Hamdan, A., & Konno, F. (2026). A systematic review of generative AI in higher education learning analytics and decision support. Discover Artificial Intelligence. https://doi.org/10.1007/s44163-026-01888-8
Purwanto, H., Isnanto, R. R., Soesanto, Q. M. B., Nursikawagus, A., & Ferdiansyah, F. R. (2026). Beyond dashboards: A systematic literature review of learning analytics, business intelligence, and generative AI for decision-making in universities. Journal of Computing Theories and Applications. https://doi.org/10.62411/jcta.15963
