对比学界与业界AI从业者挑战，揭示教育脱节问题。
AI Education in a Mirror: Challenges Faced by Academic and Industry Experts
- 通过14位专家访谈，分析学界与业界的AI实践差异。
- 产业侧主因部署约束与资源不足，学界侧重理论适配与标准化。
- 建议课程融合工程实践与跨学科素养，强化伦理与基础能力。
随着人工智能技术持续演进，学术界AI教育与实际产业挑战之间的差距仍是一个重要研究领域。本研究基于对14位AI专家（8位来自产业，6位来自学术界）的半结构化访谈，初步揭示了AI从业者面临的多重挑战。关键问题包括数据质量与可得性、模型可扩展性、实际应用限制、用户行为复杂性以及模型可解释性。尽管两方均面临数据与模型适配难题，产业专家更常提及部署约束、资源局限及外部依赖，而学术界则更关注理论适配与标准统一问题。这些探索性发现表明，AI课程应更好融入真实世界复杂性、软件工程原则及跨学科学习，同时重视构建基础理论与伦理推理能力的教育目标。
原文摘要 · Abstract (English)
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.
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