Akademik Veri Yönetim Sistemi
Generative AI in Education: Theories, Applications, and Ethical Frontiers, Springer Nature, ss.143-157, 2026
This chapter explores how artificial intelligence (AI)-driven learning analytics (LA) might help how students learn. With the increased use of digital tools in education, massive volumes of student data are produced, necessitating effective analysis to improve learning processes and results. LA provides a systematic way to measuring, collecting, and analyzing data, allowing for insights into student performance, engagement, and targeted interventions. The incorporation of AI, particularly generative AI (GenAI), expands the capabilities of LA by offering predictive and prescriptive models that provide real-time feedback, and identify at-risk learners. However, using AI with LA is not without its challenges. Ethical considerations for data privacy, transparency, and regulatory compliance remain critical. This chapter emphasizes the need for strong frameworks and regulations, such as the European Union’s AI Act and UK guidelines, to ensure responsible AI use in education. It also discusses the practical uses of AI-powered LA with four applications, such as early alert systems and personalized career guidance, while emphasizing the significance of clear legal frameworks. It continues by advocating for a balanced strategy that prioritizes ethical issues in order to assure both educational impact and student well-being.