Large language models can generate plausible solutions to programming assignments, making it tempting to detect their use by matching student code against a reference bank of generated solutions. Yet similar code can also arise when an assignment admits only a few natural implementations, which leaves open what a match...
Runlong Ye, Jing Fan, Angela Zavaleta Bernuy et al.· 0 citations
Accurate references are foundational to scholarly work, enabling verification, attribution, and systematic review. However, the rapid adoption of large language models has introduced a serious integrity concern: plausible-looking but fabricated citations. Although hallucinated references are widely discussed, their vis...
Paul Denny, Gweneth Barbre, Musa Blake et al.· 0 citations
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.
J. Prather, Stephen MacNeil, Andrew Luxton-Reilly et al.· International Computing Educ...· 0 citations
This qualitative analysis revealed how students' perceived the roles of voice and text input in shaping their problem-solving process, as well as the reported drawbacks and advantages of each modality.
K. Riegel, Yan Cathy Hua, Paul Denny et al.· Annual Conference on Innovat...· 0 citations
This paper empirically study how different algorithmic implementations of the same underlying task affect students' ability to reason about good abstractions through method extraction, and shows that interleaved functional composition is more difficult to decompose.
G. Haldeman, Claus Brabrand, Paul Denny· Annual Conference on Innovat...· 0 citations
This work analyzed 23 publicly available syllabi and course materials of upper-division, credit-bearing courses that meet specific criteria, including explicitly addressing Generative AI in software engineering, and characterized courses'learning objectives, assessments, topics, and documented AI tools.
Francis Geng, Anshul Shah, Miannuan Chen et al.· 1 citation
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.· International Computing Educ...· 0 citations
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