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@article{190823,
        author = {Vishakha C. Jadhav and Dr. Vaishali A. Chavan},
        title = {Integrating Cognitive Behavioral Therapy and Machine Learning for Adolescent Academic Performance Enhancement: A Comprehensive Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {4856-4870},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190823},
        abstract = {This comprehensive review examines the integration of Cognitive Behavioral Therapy (CBT) and Machine Learning (ML) approaches to develop an adolescent learning model for general academic performance enhancement across all subjects. Drawing from 265 peer-reviewed publications (2015-2025), this paper synthesizes evidence on CBT principles in educational settings, ML techniques for academic prediction and personalization, and emerging hybrid implementation models. The review reveals that while CBT interventions have demonstrated significant effectiveness in reducing academic anxiety, enhancing self-efficacy, and improving student well-being in school settings, and ML models have shown strong predictive capabilities for identifying at-risk students, the integration of these two paradigms remains nascent. Only a handful of studies have attempted to combine CBT principles with ML-driven personalization, primarily through AI-powered chatbots and digital platforms. This review identifies critical research gaps and proposes a clear pathway for developing an integrated CBT-ML adolescent learning model that combines counselor-guided interventions with student self-directed digital tools, offering actionable insights for researchers and practitioners seeking to enhance academic outcomes through evidence-based, personalized interventions.},
        keywords = {ognitive Behavioural Therapy, Machine Learning, Adolescent, Educational-Psychology, Integration, Intervention},
        month = {February},
        }