Interactive Long-Term Study Planning for Individual Student Support, Leveraging Process Mining and AI

Abstract

This demo showcases an AI- and Process Mining-powered interactive study planning tool designed to assist students in higher education with complex personalized, datadriven feedback. The tool integrates rule-based artificial intelligence (AI) and process mining to provide real-time, context-aware guidance, enabling students to visualize study program structures and receive automated feedback on plan validity. By leveraging historical student data and curriculum rules, the tool offers personalized recommendations, empowering students to make informed, autonomous decisions about their study paths. Developed through an iterative human-centered design process, the tool addresses the complexities of long-term academic planning while balancing guidance with student autonomy. Through ongoing user testing and stakeholder engagement, the tool is continually adapted to the requirements, feedback, and insights of users, stakeholders, and researchers. The demo will focus on the tool’s interface and feedback mechanisms, highlighting its potential to support dynamic longterm study planning, especially regarding deviations from recommended study plans. With our tool, we address the complexities of study planning and support, giving students the tools to make informed decisions and offering promising and more successful study paths. In doing so, we aim to support students in managing the challenge of comprehensive long-term study planning successfully, leading to better student outcomes as preliminary results indicate.