This paper analyzes the integration of artificial intelligence (AI) technologies into digital and pedagogical diagnostics within a cyberpedagogical framework. Using theoretical analysis and comparative methods, the study examines AI-driven adaptive testing, learning analytics, performance monitoring, predictive modeling, and automated feedback systems. The findings indicate that AI enhances personalized learning, improves the accuracy of assessment, and optimizes teachers’ instructional practices. However, issues related to data security, ethical concerns, and teachers’ digital readiness remain significant challenges. The article concludes that effective AI integration requires a comprehensive, ethically grounded approach that preserves the central role of the human factor in education.

cyberpedagogy, artificial intelligence, digital diagnostics, pedagogical diagnostics, adaptive testing, learning effectiveness, learning analytics.

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