Citation:

Rahamath Mohamed Razikh Ulla, 2026. "AI-Driven Behavioural Interventions Integrating Cognitive Frameworks with Intelligent Systems", ESP Journal of Engineering & Technology Advancements  6(1): 175-185.

Abstract:

Artificial intelligence (AI) is becoming critical in improving behavioral interventions in health, sustainability, and digital well-being. This review proposes a theory-based framework combining the Theory of Planned Behavior (TPB) with recommendation-system architecture and principles of EAST behavioral design (Easy, Attractive, Social, Timely). TPB offers interpretable cognitive predictors—attitudes, subjective norms, and perceived behavioral control whereas collaborative filtering enables personalization at scale, and EAST limits intervention delivery to action-oriented behavioral optimization. Empirical studies by just-in-time adaptive interventions (JITAIs), conversational agents and energy-feedback systems indicate that they have strong short-term effects but continue to face persistent challenges related to durability, interpretability, and fairness. The intelligent recommendation architecture suggested is TPB-compliant and overcomes prediction-oriented models in favor of cognitively anchored adaptive controllable behavioral systems. The proposed future research directions are longitudinal construct modelling, fairness-conscious personalization and cross-domain scalability. It represents a promising step toward towards responsible and scalable behavior change technologies by combining cognitive theory with intelligent recommendation systems.

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Keywords:

Artificial Intelligence, Collaborative Filtering, Digital Health, EAST Framework, Ethical AI, Explainable AI, Just-in-Time Adaptive Interventions, Recommendation Systems, Sustainable Behavior, Theory of Planned Behavior.