THE USE OF GENERATIVE ARTIFICIAL INTELLIGENCE BASED ON THE CIDI-PROMPTING MODEL TO ENHANCE THE LEVEL OF COGNITIVE COMPETENCIES AND CREATIVE THINKING OF UNIVERSITY STUDENTS

The integration of Generative Artificial Intelligence (GenAI) into higher education has outpaced the development of pedagogical models capable of directing it towards higher-order learning. Unstructured, zero-shot interaction with Large Language Models tends to yield fluent but uncritical output, while conventional assessment instruments are not designed to capture the quality of a learner's interaction with an AI system. This article addresses that gap by presenting the design of a Personalized Multi-Agent System (MAS) built upon the CIDI (Clarify, Ideate, Develop, Implement) pedagogical framework, together with the assessment architecture required to evaluate it. Following a design-based research methodology, four components are specified. First, the CIDI framework is articulated as a four-stage model of structured human-AI interaction, grounded in Distributed Cognition Theory and Cognitive Load Theory. Second, a dual-agent architecture is specified in full: an Ideation Agent configured for divergent generation and a Critical Evaluator Agent configured for convergent methodological critique, each defined by its system prompt and temperature configuration, and both integrated into an institutional Learning Management System. Third, a complete instructional design is presented, implementing the framework as a four-stage research-planning task within a research methodology curriculum, and introducing the Iterative Refinement Cycle (IRC) as an observable indicator of dialogic engagement recoverable from LMS logs. Fourth, two purpose-built assessment instruments are introduced: a 100-point Cognitive Competence Score rubric addressing problem formulation, methodological rigour, logical structuring and academic literacy; and an analytic rubric operationalizing Torrance's four dimensions of divergent thinking. The article concludes with a validation protocol specifying the design, reliability indices and analytic procedures required for subsequent empirical testing. The contribution is methodological: it supplies a reproducible architecture and a transparent instrumentation set for research on structured human-AI interaction in higher education.