Artificial Intelligence and Large Language Models in Higher Education: Results of A Systematic Review

(1. Centre FPR Digital Competence and STEAM, University College of Teacher Education Burgenland, Eisenstadt, Austria;

2. Virtual University College, University College of Teacher Education Burgenland, Eisenstadt, Austria)

The analysis shows that AI and LLMs offer significant advantages for higher education, such as the creation of individualized learning paths and the automation of assessment processes. At the same time, the study identifies significant challenges, including privacy issues, algorithmic biases, and the lack of explainability of complex models. Ethical implications such as fairness and equity are also critically examined. To address these challenges, concrete recommendations for educational institutions are developed. These include the development of proprietary, internally hosted AI systems for better data control, the promotion of interdisciplinary collaboration, comprehensive training programs for educators, and the establishment of governance structures and ethical guidelines.

The findings emphasize the need for an integrated perspective that considers both technical and ethical aspects in order to harness the full potential of AI in education in a responsible way. This work provides practical guidelines for the effective and ethical implementation of AI technologies in higher education, thereby contributing to improving the quality of education while ensuring data protection and fairness.

Key words: artificial intelligence, AI in education, systematic literature review, AI applications in higher education, explainable AI

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