Modern programming tools use generative AI (GenAI) to suggest code to the user as they type, interrupting their problem-solving behavior and undermining the development of their programming critical thinking skills. In this paper, we present a scaffolded programming exercise designed to support student differentiation between good and bad GenAI code suggestions based on negative expertise–that identifying why an answer is wrong is part of developing conceptual knowledge. We compare a version of the tool that showed one suggestion (correct or not), to a version that showed three suggestions (one of which was correct). We present results on performance and error rates as well as qualitative findings centered on Pintrich and DeGroot’s theory of self-regulation. Students reported that the single suggestion version better aligned with industry tools and presented a lower cognitive load. Students also reported that the multiple suggestion version caused them to slow down and think critically about the line under consideration, the overall purpose of the code, and the benefits of planning.
J. Prather, Stephen MacNeil, Andrew Luxton-Reilly et al.· Proceedings of the 2026 ACM...· 0 citations
Program decomposition is a core skill in computer science that overlaps with program comprehension, refactoring, and design. Decomposition takes many forms, with one common task at the introductory level being the identification and extraction of meaningful abstraction into separate functions. However, there is limited empirical evidence about how the algorithmic structuring of code affects the difficulty of producing a meaningful decomposition. In this paper, we empirically study how different algorithmic implementations of the same underlying task affect students' ability to reason about good abstractions through method extraction. Grounded in a recent framework on code structuring, we design three functionally equivalent versions of the same task that differ only in how two latent functional patterns relate to each other: sequentially, hierarchically, or interleaved. Using a large-scale controlled study with 994 introductory programming students, we examine students' effort in decomposing the programs, the approaches they adopt, and their perceptions of whether the resulting decomposition improves readability and understanding. Our results show that interleaved functional composition is more difficult to decompose, providing initial empirical support for the framework's hypothesized ordering of composition pattern difficulty. Students also perceive their decompositions of the interleaved version to be less easy to read and understand. We also find that students describe many different approaches of decomposing, and we discuss several implications for teaching and future research.
G. Haldeman, Claus Brabrand, Paul Denny· Annual Conference on Innovat...· 0 citations
This paper presents the development and initial validation of an instrument to measure self-efficacy while using GenAI to learn programming, and finds strong support for the validity of the existing Steinhorst instrument in a new context, specifically an introductory programming course that fully integrates GenAI.
J. Prather, Lauren E. Margulieux, Yekaterina Kharitonova et al.· Proceedings of the 2026 ACM...· 0 citations