<![CDATA[Purpose of the study: Purpose of this study is to formalize a framework for the integration of AI into higher education, predicated on an analysis of cognitive transformations, subjective strategies, and regulatory frameworks for intervention. Methodology: The methods employed in this study encompass cognitive-discursive calibration, expert rubricative assessment, NLP analysis of academic texts, hybrid modeling of textogenesis, inter-iterative comparative analysis, a dispersion questionnaire of students, an expert scale survey of educators, ontological normative modeling. Main Findings: Through inter-iterative analysis, a notable increase in argumentative complexity (+14.1%), cognitive complexity (+21.2%), and syntactic complexity (+19.4%) was observed, alongside an enhancement in coherence (+14.6%). Conversely, there was a simultaneous decrease in subjectivity (−7.5%) and a significant increase in AI-discriminant weight (+180.2%). Instances of complete generation were recorded in 16% of cases, indicative of hyperdelegation and the erosion of intentionality; the survey revealed epistemological polarization regarding the AI perception. In response to these identified factors, a stratified framework has been developed, prioritizing cognitive non-delegation, semantic traceability, and subjective accountability, which aims to ensure the stabilization of cognitive sovereignty and digital autonomy. Novelty/Originality of this study: The scientific novelty of this study lies in developing a formalized framework for integrating artificial intelligence into higher education, emphasizing cognitive traceability, subjective accountability, and normative stratification. This framework systematically aligns AI-mediated educational processes with ethical standards and pedagogical imperatives, ensuring responsible technological integration that supports transparency, learner agency, and sustainable academic governance within digital learning environments.]]>
