Keywords
Generative AI, Social Learning Theory, Focus Group, Grounded Theory Method, Software Developers
Abstract
Generative AI code assistants from GitHub and ChatGPT are transforming software development, yet their impact on developers' on-the-job learning remains underexplored. While early educational studies show these tools boost short-term performance, they may undermine long-term learning skills. However, evidence is limited to student populations and small tasks, leaving critical gaps about professional developers' learning trajectories. This research-in-progress employs Constructivist Grounded Theory with scenario-based focus groups to investigate how GenAI assistants reshape social learning processes in software development teams. We examine how AI tools affect the stages of observational learning from Bandura's social learning theory. By focusing on developers' interactions with peers and seniors across various workplace scenarios, this study seeks to understand whether AI augments, substitutes, or transforms traditional learning mechanisms, and how these effects vary by experience level. Findings will inform organizational policies, mentoring programs, and educational curricula to preserve learning while capturing productivity gains.
Recommended Citation
Isik, Oyku; Khan, Ajaan Rahman; and Li, Dongtan, "Faster, But Not Smarter? How AI Code Assistants Reshape Learning for Software Developers" (2025). ICIS 2025 Proceedings. 2.
https://aisel.aisnet.org/icis2025/paperathon/paperathon/2
Faster, But Not Smarter? How AI Code Assistants Reshape Learning for Software Developers
Generative AI code assistants from GitHub and ChatGPT are transforming software development, yet their impact on developers' on-the-job learning remains underexplored. While early educational studies show these tools boost short-term performance, they may undermine long-term learning skills. However, evidence is limited to student populations and small tasks, leaving critical gaps about professional developers' learning trajectories. This research-in-progress employs Constructivist Grounded Theory with scenario-based focus groups to investigate how GenAI assistants reshape social learning processes in software development teams. We examine how AI tools affect the stages of observational learning from Bandura's social learning theory. By focusing on developers' interactions with peers and seniors across various workplace scenarios, this study seeks to understand whether AI augments, substitutes, or transforms traditional learning mechanisms, and how these effects vary by experience level. Findings will inform organizational policies, mentoring programs, and educational curricula to preserve learning while capturing productivity gains.
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