A Review of Generative AI in Computer Science Education: Challenges and Opportunities in Accuracy, Authenticity, and Assessment

TL;DR

This survey reviews the integration of Generative AI in computer science education, highlighting challenges like accuracy and bias, and opportunities such as improved efficiency and creative support, emphasizing the need for balanced, ethical approaches.

Contribution

It provides a comprehensive overview of current challenges and opportunities of Generative AI in education, proposing hybrid assessment models and emphasizing the importance of AI literacy.

Findings

Generative AI enhances educational efficiency and creativity.

Concerns include hallucinations, bias, and academic integrity.

Hybrid assessment models and AI literacy are recommended.

Abstract

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.