Generative AI in Education: Personalizing Learning and Fostering Self-Assessment

Abstract

Generative Artificial Intelligence (AI) has emerged as a key driver of digital transformation in education, enabling instant personalization of learning and the generation of adaptive content tailored to learners’ abilities and needs. This article aims to explore the potential of such technologies in enhancing educational processes by fostering personalized learning and empowering students to develop self-assessment strategies. The central research problem lies in assessing the effectiveness of generative AI in improving learning outcomes and ensuring content reliability, while addressing ethical and technical challenges such as data protection and the digital divide.

The study adopts a descriptive–applied methodology through a field experiment involving 110 primary school students. Tools such as ChatGPT and Midjourney were employed to generate texts, images, and exercises, while both quantitative and qualitative methods were used to analyze students’ interactions and performance.

The article is structured around several key axes: the conceptual framework of generative AI in education, mechanisms of real-time content personalization, support for self-directed and self-assessment learning, a practical case study, and the linkage of results to motivational and cognitive comfort theories.

Findings indicate that generative AI technologies effectively enhance cognitive comfort, increase student motivation, and improve academic performance, while enabling learners to design interactive self-assessment tests using platforms such as Quizizz AI. These outcomes highlight the innovative contribution of the article, which connects real-time personalized learning with self-assessment practices, opening new perspectives for the didactics of school subjects in the age of generative AI.