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
Year: 2026
Type: article
Abstract: 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.
Author: Samvel Asatryan
Institution: Yerevan State University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7225005077