AI Education in a Mirror: Challenges Faced by Academic and Industry Experts

2025-05-02

AI Summary

This study identifies a critical academia-industry divide in AI education: academia prioritizes theoretical adaptation and standardization, whereas industry emphasizes engineering deployment, resource coordination, and real-world constraints. Through semi-structured interviews with 14 cross-domain AI experts and thematic coding analysis, we systematically identify five key practical challenges—data quality, model scalability, deployment constraints, user behavior modeling, and model interpretability. We further conduct the first qualitative comparative analysis to clarify structural differences between academic and industrial AI practice across objectives, methodologies, and evaluation criteria. Based on these findings, we propose a reconceptualized AI education framework integrating software engineering practices, interdisciplinary problem-solving, and ethical reasoning. Our results provide empirically grounded guidance for curriculum reform, shifting AI talent development from isolated algorithmic training toward systemic, responsible AI system design and deployment capabilities.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Bridging gap between academic AI education and industry challenges
• Addressing data quality, model scalability, and practical constraints
• Integrating real-world complexities into AI curricula effectively

Innovation

• Semi-structured interviews with AI experts
• Identify challenges in data and models
• Integrate real-world complexities in curricula