ISCAP Proceedings: Abstract Presentation
Navigating Generative AI in Introductory Programming: Balancing Guidance, Performance, and Integrity
Rui Sundrup
University of Louisville
Abstract
Generative AI (GenAI) has quickly become an indispensable tool across educational settings, raising both opportunities and concerns for programming instruction (Giannakos et al., 2025). While AI-powered tools can accelerate learning, scaffold problem-solving, and reduce frustration for novices, they also present risks of overreliance, reduced conceptual understanding, and academic integrity violations (Mittal et al., 2024; Zhai et al., 2024). For instructors of introductory programming courses, the central challenge is not whether students will use GenAI, but how its use should be guided, restricted, and integrated to maximize learning while maintaining fairness.
This study explores the role of GenAI integration in beginner-level programming classes by comparing multiple instructional contexts with varying degrees of AI use and guidance. Specifically, we investigate (1) how different levels of AI allowance (from strict restrictions to open use) affect student performance, (2) the importance of instructor guidance in framing productive and ethical use of AI, and (3) how students perceive AI’s role in their learning.
Initial analyses suggest nuanced patterns: while more open AI use may boost short-term assignment performance, it also appears linked to increased risks of academic dishonesty and weaker evidence of long-term problem-solving retention. In contrast, courses where instructors framed AI as a supportive but limited tool showed promising trends toward deeper understanding and more constructive student attitudes. Student feedback further highlights both enthusiasm for AI’s convenience and concern over potential overreliance.
These preliminary results point to the need for intentional design in AI integration. For instructors, this work provides early evidence and practical considerations for developing course policies that leverage the benefits of GenAI while mitigating risks. More broadly, the findings aim to contribute to ongoing conversations about how higher education can adapt responsibly in an era of rapid technological change.
References:
Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., ... & Rienties, B. (2025). The promise and challenges of generative AI in education. Behaviour & Information Technology, 44(11), 2518-2544.
Mittal, U., Sai, S., & Chamola, V. (2024). A comprehensive review on generative AI for education. IEEE Access.
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28.