Towards Enhancing Learning Outcomes in Higher Education: Proposing a Conceptual Model of Personalized AI Tutoring Systems

Source abstract

Abstract The emergence of generative artificial intelligence (AI) drives the potential of enhancing learning experiences and personalizing the learning process in higher education. The large language models (LLMs) evolution of generative AI further steers the potential of developing personalized AI tutoring systems (PAITS) for achieving higher learning outcomes in higher education. PAITS can act as a personalized tutor to provide immediate support and guidance to learners’ needs, learning abilities and preferences for progressive, adaptive and active learning. However, developing a fully functioning PAITS requires to be equipped with the core features and the model/framework to structure the core features effectively that are largely unexplored. Therefore, this study aims to identify the core features of PAITS and propose a conceptual model equipped with the core features of PAITS towards enhancing learning outcomes in higher education. The study follows a qualitative research approach to point out the key features/elements of PAITS through the content analysis of state-of-the-art scholarly articles. The findings of this study can be useful for developing intelligent systems for achieving higher learning outcomes in higher education. Also, it can be considered as a reference point and guidance for further study on the subject. Both practitioners and relevant stakeholders in the context of higher education can avail benefits from the outcomes of this study.