International Journal of Science, Engineering and TechnologyAuthors: Assistant Professor Dr. V. Maria Antoniate Martin, Praveen J
Abstract: Generative Artificial Intelligence (GenAI) has rapidly transitioned from an experimental technology into a mainstream instructional resource across higher-education classrooms. Building on five recent systematic and conceptual reviews of AI in education, this paper synthesizes the reported responses, attitudes, behaviors, applications, benefits, and challenges associated with GenAI adoption in university-level teaching and learning. The review of existing literature reveals that while GenAI substantially improves personalization, engagement, and assessment efficiency, its integration is constrained by ethical concerns, over-reliance risks, unequal infrastructure access, and a persistent lack of human-centered oversight. To address these gaps, this paper proposes GenTutor-HE, a hybrid, human-in-the-loop framework that combines a generative AI tutoring engine, an adaptive knowledge-tracing module, and an explicit ethical-governance layer that filters generated content for bias, privacy risk, and factual reliability before it reaches learners. The proposed methodology follows a design-science research approach comprising requirement analysis, architectural design, prototype development, and mixed-method evaluation. A comparative analysis of existing intelligent tutoring systems, adaptive learning platforms, and generative content-based systems is presented, along with the advantages and limitations of the proposed model. The paper concludes that a balanced, ethically governed hybrid approach offers a more sustainable pathway for GenAI adoption than fully automated systems, and it outlines future enhancements including multilingual support, emotion-aware feedback, and federated-learning-based privacy preservation.
