Published Paper Details:

The rapid expansion of interdisciplinary undergraduate programs has increased the complexity of academic decision-making among higher education aspirants. Although centralized admission platforms simplify institutional admission procedures, they often lack intelligent academic guidance systems capable of assisting students in selecting suitable academic programs aligned with their abilities, interests, and career goals. The Degree Online Services Telangana (DOST) platform provides a unified admission environment for undergraduate programs in Telangana State. However, the present admission process mainly focuses on administrative automation and does not support personalized course recommendation. This study presents a hybrid artificial intelligence-based recommendation model for undergraduate course selection within the DOST ecosystem. The proposed model combines collaborative filtering, latent factor learning, and sentiment-aware analysis to generate personalized course recommendations. Academic performance indicators, contextual attributes, peer influence patterns, and qualitative feedback representations are integrated to estimate recommendation suitability scores. Mathematical formulations for similarity computation, latent interaction learning, recommendation fusion, and optimization are incorporated to establish a theoretically grounded recommendation architecture. A preliminary validation study based on survey observations highlights the necessity of intelligent recommendation support mechanisms within centralized admission environments. The proposed model provides a theoretically grounded approach for integrating academic performance, contextual attributes, and sentiment-aware learning into undergraduate course recommendation. The study contributes a domain-specific recommendation architecture for the DOST ecosystem and offers a foundation for subsequent empirical evaluation and large-scale deployment.