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This paper presents an approach of using methods of process mining andrule-based artificial intelligence to analyze and understand study paths ofstudents based on campus management system data and study program models.Process mining techniques are used to characterize successful study paths, aswell as to detect and visualize deviations from expected plans. These insightsare combined with recommendations and requirements of the corresponding studyprograms extracted from examination regulations. Here, event calculus andanswer set programming are used to provide models of the study programs whichsupport planning and conformance checking while providing feedback on possiblestudy plan violations. In its combination, process mining and rule-basedartificial intelligence are used to support study planning and monitoring byderiving rules and recommendations for guiding students to more suitable studypaths with higher success rates. Two applications will be implemented, one forstudents and one for study program designers.

A Combined Approach of Process Mining and Rule-based AI for Study Planning and Monitoring in Higher Education