AI Education in a Mirror: Challenges Faced by Academic and Industry Experts
It addresses the problem of aligning AI education with real-world demands for educators and professionals, though it is incremental as it provides exploratory insights without proposing specific solutions.
This study investigated the gap between academic AI education and industry needs by interviewing 14 AI experts, identifying challenges like data quality, model scalability, and deployment constraints, with industry professionals focusing more on practical issues and academics on theoretical ones.
As Artificial Intelligence (AI) technologies continue to evolve, the gap between academic AI education and real-world industry challenges remains an important area of investigation. This study provides preliminary insights into challenges AI professionals encounter in both academia and industry, based on semi-structured interviews with 14 AI experts - eight from industry and six from academia. We identify key challenges related to data quality and availability, model scalability, practical constraints, user behavior, and explainability. While both groups experience data and model adaptation difficulties, industry professionals more frequently highlight deployment constraints, resource limitations, and external dependencies, whereas academics emphasize theoretical adaptation and standardization issues. These exploratory findings suggest that AI curricula could better integrate real-world complexities, software engineering principles, and interdisciplinary learning, while recognizing the broader educational goals of building foundational and ethical reasoning skills.