探讨生成式AI在计算机教育中的准确率、真实性和评估挑战
A Review of Generative AI in Computer Science Education: Challenges and Opportunities in Accuracy, Authenticity, and Assessment
- 分析生成式AI在编程教学中的应用与潜在错误
- 提出混合评估模型以平衡AI辅助与人工判断
- 适合教育研究者和课程设计者参考
本文综述了生成式AI工具（如ChatGPT、Claude）在计算机科学教育中的应用，聚焦准确性、真实性与评估三大核心问题。通过文献梳理，揭示其在提升教学效率与支持学生创作方面的优势，同时也指出人工智能幻觉、错误传播、偏见及师生内容边界模糊等风险。研究强调人工监督的必要性，建议采用人机协同的混合评估模式，构建偏见检测框架，并加强师生的AI素养。结果表明，生成式AI的成功整合需兼顾伦理、教学法与技术因素。未来可探索提升准确性、维护学术诚信及发展兼顾创意与精确性的自适应模型。
原文摘要 · Abstract (English)
This paper surveys the use of Generative AI tools, such as ChatGPT and Claude, in computer science education, focusing on key aspects of accuracy, authenticity, and assessment. Through a literature review, we highlight both the challenges and opportunities these AI tools present. While Generative AI improves efficiency and supports creative student work, it raises concerns such as AI hallucinations, error propagation, bias, and blurred lines between AI-assisted and student-authored content. Human oversight is crucial for addressing these concerns. Existing literature recommends adopting hybrid assessment models that combine AI with human evaluation, developing bias detection frameworks, and promoting AI literacy for both students and educators. Our findings suggest that the successful integration of AI requires a balanced approach, considering ethical, pedagogical, and technical factors. Future research may explore enhancing AI accuracy, preserving academic integrity, and developing adaptive models that balance creativity with precision.
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