Abstract
This chapter provides an overview of research on AI applications in higher education using a systematic review approach. There were 146 articles included for further analysis, based on explicit inclusion and exclusion criteria. The findings show that Computer Science and STEM make up the majority of disciplines involved in AI education literature and that quantitative methods were the most frequently used in empirical studies. Four areas of AI education applications in academic support services and institutional and administrative services were revealed, including profiling and prediction, assessment and evaluation, adaptive systems and personalisation, and intelligent tutoring systems. This chapter reflects on the challenges and risks of AI education, the lack of association between theoretical pedagogical perspectives, and the need for additional exploration of pedagogical, ethical, social, cultural, and economic dimensions of AI education.
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