Exploring Higher Education Students' Experience with AI-powered Educational Tools: The Case of an Early Warning System

Abstract

AI-powered educational tools (AIEd) include early warning systems (EWS) to identify at-risk undergraduates, offering personalized assistance. Revealing students' subjective experiences with EWS could contribute to a deeper understanding of what it means to engage with AI in areas of human life, like teaching and learning. Our investigation hence explored students' subjective experiences with EWS, characterizing them according to students’ profiles, self-efficacy, prior experience, and perspective on data ethics. The results show that students, largely senior workers with strong academic self-efficacy, had limited experience with this method and minimal expectations. But, using the EWS inspired meaningful reflections. Nonetheless, a comparison between the Computer Science and Economics disciplines demonstrated stronger trust and expectation regarding the system and AI for the former. The study emphasized the importance of helping students’ additional experiences and comprehension while embracing AI systems in education to ensure the quality, relevance, and fairness of their educational experience overall.

Keywords

• Student's Experience
• Artificial intelligence
• Early Warning System
• Higher Education
• Thematic Analysis