The Role of Generative AI (ChatGPT) in Computer Science Education

Abstract

Generative Artificial Intelligence (AI) has rapidly transformed the educational landscape by providing intelligent, interactive, and personalized learning support. Among the various AI-powered tools, ChatGPT has emerged as one of the most widely adopted applications in higher education, particularly in computer science education. This paper explores the role of ChatGPT in improving programming skills, conceptual understanding, problem-solving abilities, and academic productivity among computer science students. The study also examines the benefits and challenges associated with integrating ChatGPT into teaching and learning environments. While ChatGPT enhances personalized learning, instant feedback, and coding assistance, concerns regarding academic integrity, overdependence, misinformation, and reduced critical thinking remain significant. The paper concludes that ChatGPT should serve as a complementary educational tool rather than a replacement for educators. Proper institutional policies, AI literacy, and ethical guidelines are essential to maximize its educational benefits while minimizing associated risks.

Introduction

This paper examines the impact of ChatGPT and generative AI on computer science education. AI has evolved into a powerful educational tool that provides instant explanations, debugging support, code generation, and personalized learning assistance. It helps students overcome programming challenges more efficiently and enables educators to automate tasks such as creating assignments, quizzes, and teaching materials. However, excessive dependence on AI can reduce students' critical thinking, debugging skills, and independent problem-solving abilities. The paper also highlights concerns about inaccurate AI-generated responses and emphasizes the need for ethical guidelines for AI use in education.

The study reviews previous research, showing that while traditional Intelligent Tutoring Systems and Automated Assessment Tools provide limited feedback, Large Language Models offer interactive explanations and personalized support. Researchers recognize both the benefits of improved accessibility and the risks of weakening students' foundational programming skills.

To evaluate ChatGPT's educational impact, the authors propose a Cognitive Interaction Framework, which models learning based on student prompts, AI responses, and a cognitive retention coefficient that decreases when students rely on copying AI-generated code. Three evaluation metrics are used: debugging speed, conceptual understanding, and originality of student work.

The experiment involved 120 students in a Data Structures and Algorithms course, divided into a control group without AI access and an experimental group using ChatGPT with prompt-engineering training. Data were collected through a monitored cloud-based IDE and offline conceptual quizzes.

Results showed that students using ChatGPT completed debugging tasks about 42% faster and achieved slightly higher conceptual quiz scores. However, they performed 6.7% worse in closed-book coding exams, indicating overreliance on AI. Telemetry data also revealed that frequent copy-paste behavior was linked to poor knowledge retention. Overall, the study concludes that ChatGPT is an effective learning aid when used responsibly but should complement—not replace—independent thinking and problem-solving in computer science education.

Conclusion

Generative AI platforms, with ChatGPT at the forefront, have fundamentally redefined the baseline toolkit for modern computer science departments. By providing tailorable, immediate educational scaffolding, these tools offer students a responsive environment to refine software engineering skills, unpack complex computational logic, and diagnose compile-time errors interactively. Concurrently, faculty can harness these models as effective organizational levers to streamline course administration and maximize instructional scalability. Nonetheless, successfully anchoring generative tools within a computer science curriculum demands a proactive confrontation with clear pedagogical and ethical risks. Unconditioned reliance threatens to produce graduates with degraded critical thinking skills and fragile independent engineering capabilities. Simultaneously, the low barrier to automated plagiarism requires universities to accelerate the deployment of modernized academic integrity frameworks. Ultimately, ChatGPT must be managed as a specialized instructional assistant rather than an instructor substitute; human faculty remain entirely indispensable for authentic mentorship, evaluating complex logical synthesis, and teaching students how to safely navigate ambiguous, real-world software problems. Moving forward, institutions should lean into a hybrid learning model supported by structured AI literacy initiatives, ensuring technology amplifies, rather than replaces, deep intellectual development.

References

[1] J. Doe and R. Smith, \"The Dawn of Generative AI in Higher Education,\" IEEE Transactions on Education, vol. 67, no. 2, pp. 145-152, 2024. [2] A. Jones, \"Pedagogical Shifts in Computer Science: Coping with Large Language Models,\" in Proceedings of the IEEE International Conference on Software Engineering Education, 2025, pp. 89-98. [3] M. Brown, \"Automated Assessment vs. Conversational AI: A Comparative Study,\" Journal of Educational Technology Systems, vol. 53, no. 4, pp. 412-429, 2025.

Copyright

Copyright © 2026 Chethan TR, Deepika T N, Soumya , Sneha . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.