CodeExemplar: Example-Based Scaffolding for Introductory Programming in the GenAI Era
This addresses the problem of balancing assistance and learning integrity for introductory programming students in the generative AI era, though it appears incremental as it builds on existing scaffolding and analogical transfer concepts.
The paper tackles the tension in introductory programming between students needing timely help and the risk of copying from generative AI by proposing example-based scaffolding, where AI provides context-different examples to support analogical transfer, with initial feedback from a classroom pilot and instructor interviews.
Generative AI (GenAI) can generate working code with minimal effort, creating a tension in introductory programming: students need timely help, yet direct solutions invite copying and can short-circuit reasoning. To address this, we propose example-based scaffolding, where GenAI provides scaffold examples that match a target task's underlying reasoning pattern but differ in contexts to support analogical transfer while reducing copying. We contribute a two-dimensional taxonomy, design guidelines, and CodeExemplar, a prototype integrated with auto-graded tasks, with initial formative feedback from a classroom pilot and instructor interviews.