<![CDATA[This article theoretically analyzes the pedagogical and didactic advantages of implementing intelligent diagnostic digital learning environments designed to identify and develop students’ programming aptitude in the context of higher education digitalization and the credit-module system. Using analysis of the scientific literature and regulatory documents, comparative analysis and pedagogical modeling, the study describes mechanisms for constructing personalized learning pathways based on the principles of asynchronous and differentiated learning, and for organizing process-based assessment with learning analytics and a Socratic artificial intelligence (AI) tutor. A comparative analysis of traditional learning environments and intelligent diagnostic environments is presented, together with a three-stage methodology for instructional design that integrates diagnostic profiling, the PRIMM (Predict–Run–Investigate–Modify–Make) teaching cycle and process-based final assessment. The proposed framework is conceptual; its effectiveness should be verified through empirical studies in programming courses.]]>
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