Personalized learning through AI: Pedagogical approaches and critical insights

In this analysis, we review artificial intelligence (AI)-supported personalized learning (PL) systems, with an emphasis on pedagogical approaches and implementation challenges. We searched the Web of Science and Scopus databases. After the preliminary review, we examined 30 publications in detail. ChatGPT and machine learning technologies are among the most often utilized tools; studies show that general education and language learning account for the majority of AI applications in the field of education. Supported by particular learning approaches stressing student characteristics and expectations, the results show that automated feedback systems and adaptive content distribution define AI’s educational responsibilities mostly. The study notes major difficulties in three areas: technical constraints and data privacy concerns; educational and pragmatic barriers. Although curriculum integration and teacher preparation are considered major concerns, pedagogical challenges come first above technology integration. The results also underline the need for thorough professional development activities for teachers and AI tools for especially targeted instruction. The study shows that the efficient application of AI-enabled PL requires a comprehensive strategy addressing technological, pedagogical, and ethical issues all at once. These results help to describe the current state of AI in education and provide ideas for future developments as well as techniques for its use.