Data - From Algorithms to Adaptation: How Generative AI is Reshaping Personalized Learning in Higher Education
Description
This systematic review followed the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement to ensure that the research process was transparent, comprehensive, and reproducible. By adhering to these standards, the review provided an accurate synthesis of current evidence regarding the benefits and challenges of using generative AI in personalized learning within higher education. Each methodological element was designed to strengthen the rigor and replicability of the study. The PICO model (Population, Intervention, Comparison, Outcome) is widely used in systematic reviews within the health sciences to frame research questions and guide the review process. However, this model can be adapted to non-clinical disciplines, such as education, by using the PICo framework (Population, Interest, Context). The modified version reflects the unique characteristics of educational research, particularly in exploring interventions, learning outcomes, and contextual influences. In this systematic review, the PICo framework has been applied to ensure the research question is specific, focused, and aligned with the review’s objectives. Below is an explanation of how each component of PICo has been defined and applied within this study: Population (P): The population component in this review refers to groups directly affected using generative AI in personalized learning. Specifically, this includes students and educators in higher education institutions, such as universities and colleges. The focus on this population ensures that the review captures both the experiences of learners and the pedagogical implications for instructors. Interest (I): The interest (or intervention) in this review is the use of generative AI in personalized learning environments. Generative AI encompasses technologies such as large language models, AI-powered tutoring systems, and adaptive learning platforms. By focusing on this specific technological innovation, the review aims to evaluate its potential to customize learning experiences, enhance student engagement, and support tailored teaching strategies. Context (Co): The context component addresses the setting or environment in which the intervention occurs. For this review, the context is higher education, which includes universities, colleges, and other post-secondary institutions. The emphasis on this context allows the review to assess how generative AI operates within formal academic settings, considering factors such as institutional resources, curriculum design, and faculty-student interactions.