Artificial intelligence is rapidly transforming higher education, affecting teaching practices, learning processes, and the competencies expected of graduates. This paper examines the methodological and practical challenges involved in evaluating AI’s educational impact, highlighting the limitations of traditional assessment approaches in a rapidly evolving technological context. It argues that changes in learning outcomes, widespread AI adoption, and the absence of stable evaluation frameworks hinder meaningful comparisons of educational effectiveness. In response, the paper proposes the systematic development of foundational AI literacy as a shared institutional objective, enabling universities to foster critical, ethical, and informed AI use while supporting more consistent assessment across disciplines.
Riferimenti bibliografici
Bertolini M. (2026). AI literacy and new learning strategies. In: W. Leal Filho, T. Wall, H. Müller, P. Yang & A. Klimczuk (Eds.). Artificial Intelligence in Sustainability Teaching and Research. World Sustainability Series. Cham: Springer.
Bertolini M., Castellini M., Moretto M., & Vergalli S. (2024). Smart grids in the renewable energy community era: New markets and the role of information. In: Sustainable Development of Renewable Energy: Latest Advances in Production, Storage, and Integration (pp. 401-412). Amsterdam: Elsevier. Doi: 10.1016/B978-0-443-13613-9.00006-4.
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. Doi: 10.1186/s41239-023-00436-z.
Jin Y., Yan L., Echeverria V., Gašević D., & Martinez-Maldonado R. (2025). Generative AI in higher education: A global perspective of institutional adoption policies and guidelines. Computers and Education: Artificial Intelligence, 8, 100348. Doi: 10.1016/j.caeai.2024.100348.
OECD (2026). Policies supporting responsible and systematic GenAI adoption in higher education. OECD Education Spotlights, No. 23. Doi: 10.1787/c4e5621f-en.
Ogunleye B., Zakariyyah K. I., Ajao O., Olayinka O., & Sharma H. (2024). A systematic review of generative AI for teaching and learning practice. Education Sciences, 14(6), 636. Doi: 10.3390/educsci14060636.
Sailer M., Maier R., Berger S., Kastorff T., & Stegmann K. (2024). Learning activities in technology-enhanced learning: A systematic review of meta-analyses and second-order meta-analysis in higher education. Learning and Individual Differences, 112, 102446. Doi: 10.1016/j.lindif.2024.102446.
Shadish W. R., Cook T. D., & Campbell D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.
Skulmowski A. (2023). The cognitive architecture of digital externalization. Educational Psychology Review, 35, 101. Doi: 10.1007/s10648-023-09818-1.
UNESCO (2023). Guidance for generative AI in education and research. Paris: UNESCO.
Xu W., & Ouyang F. (2022). A systematic review of AI role in the educational system based on a proposed conceptual framework. Education and Information Technologies, 27(3): 4195-4223. Doi: 10.1007/s10639-021-10774-y.
