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@article{196237,
        author = {Triveni Srirampur and Raghavender Srirampur},
        title = {Leveraging AI-Driven Adaptive Learning Platforms to Enhance Programming Logic in Computer Science Students},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {137-142},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196237},
        abstract = {The Indian engineering education landscape currently faces a critical "Logic Gap"—a widening disparity between a student’s syntactic proficiency and their ability to execute complex algorithmic problem-solving. This crisis is exacerbated by two primary stakeholder constraints: a student body often driven by rote-based credentialism over functional competence, and a faculty cohort restricted by traditional "chalk-and-talk" methodologies that fail to scale in high-enrollment environments. This paper proposes a systemic transition toward AI-Driven Adaptive Learning Platforms (ALPs) as a foundational pedagogical intervention. Unlike traditional assessment-centric tools, the proposed framework utilizes Bayesian Knowledge Tracing (BKT) and Item Response Theory (IRT) to create a dynamic, "Socratic" mentoring environment. By providing real-time, granular scaffolding and personalized learning pathways, the ALP addresses individual cognitive roadblocks that typically lead to student attrition and academic disinterest. Furthermore, the paper outlines a Hybrid-AI Implementation Strategy that empowers faculty through predictive learning analytics, shifting their role from routine troubleshooting to high-level architectural mentoring.},
        keywords = {AI in Education (AIEd), Adaptive Learning, Programming Pedagogy, Knowledge Tracing, EdTech Strategy.},
        month = {April},
        }