Abstract: This article examines contemporary approaches to programming as the foundation for training specialists in smart analytics and cybersecurity amid the rapid development of artificial intelligence. It identifies key scientific aspects accompanying the transformation of the developerʼs role—from classical «coding from scratch» to interacting with generative language models and automation tools. The analysis covers three main educational paradigms—traditional, adaptive, and generative-oriented—to reveal their advantages and limitations. Adaptive learning is described as a technology that employs machine learning algorithms to personalize learning paths, provide instant feedback, and automatically adjust task difficulty according to each studentʼs individual needs. Examples of adaptive platforms (CybExer, AI-Cyber Range, Knewton Alta, Georgia Tech OMS) are demonstrated, and the results of a meta-analysis are summarized, showing increased student success and engagement alongside reduced dropout rates. Special attention is given to the integration of generative artificial intelligence into curricula, which enables automatic generation of vulnerability-detection case studies. The impact of AI-driven tools (Copilot, TabNine, Diffblue) on development cycles—automatic code generation, testing, documentation, and refactoring, boosting developer productivity by 30–50 %—is also explored. A structure for an adaptive online platform is proposed, combining initial assessment, individualized plan creation, dynamic content adaptation, practical interactive projects, a feedback loop, reminder system, and final certification. A schematic diagram illustrates the interaction of system components. The study underscores the critical importance of a hybrid combination of educational methodologies—traditional, adaptive, and generative—for cultivating in students the key 21st-century competencies of analytical thinking, self-directed learning, and adaptability.