Intelligent Systems, Individual Minds: How AI Is Reorganising Learning

This study examines the use of artificial intelligence in education with regard to potentials and limitations. AI changes learning processes through pattern recognition, adaptive control and personalised feedback. Methodologically, the work is based on a literature analysis and case studies from different contexts. The research question focusses on whether AI promotes individual learning when different preferences are taken into account. Two hypotheses were tested. The results show that adaptive systems can increase motivation and performance and stabilise education in crisis situations. Quantitative meta-analyses confirm that learners achieve significantly better results on average in intelligent tutorial environments than in traditional formats. International comparative studies such as the OECD PISA analyses also point to measurable differences in performance in favour of digital learning platforms with AI components. At the same time, the concept of learning types remains scientifically controversial, which is why the second finding is only valid to a limited extent. In addition, ethical and organisational framework conditions are proving to be key factors. International guidelines call for transparency and accountability, while national analyses point to structural inertia. The study makes it clear that AI can modernise education if systems personalise in a targeted manner and thereby improve learning processes. At the same time, it shows that potential can only be realised if ethical standards are in place and institutional structures enable reforms. Keywords: Artificial Intelligence, Education, Learning Preferences, Adaptive Systems, Ethical Frameworks, Lifelong Learning, Digital Transformation