Navigating Generative AI Integration: A Framework for Educational Transformation

Abstract

The integration of Generative Artificial Intelligence into higher education is a transformative inflection point and requires comprehensive institutional re-conceptualization across pedagogy, communication, assessment, and governance. Large Language Models have shifted from being on the periphery of all tasks to being central, in which the processes through which knowledge is accessed, synthesized, and demonstrated fundamentally reframe everything. Educational institutions must both harness the affordances of AI to drive personalization and efficiency without sacrificing academic integrity and related essential cognitive skills development. Intelligent Tutoring Systems facilitate adaptive learning pathways, where standardized models are replaced with responsive frameworks that address the individual needs of all students through continuous content adjustment and predictive analytics. The automation of faculty workload frees up instructional time for mentoring and innovative pedagogy, yet it also introduces new literacy requirements centered on algorithmic communication competencies. The rise of prompt engineering as a foundational skill refashions writing from the making of content to the directing of content and critical curation. Traditional methodologies for assessment are collapsing in the face of AI's generative capabilities, demanding a migration toward authentic evaluation that centers on real-world applications, oral examination formats, and process-based learning verification. Governance frameworks that were built for predictable systems are being shown as inadequate for managing autonomous, opaque, computational systems (with emergent properties). An effective institutional response requires risk-stratified categorization systems, algorithmic transparency protocols, and human-in-the-loop accountability to ensure that technological development bolsters, rather than cripple, education's mission.