Proposes the CoRe-3 (FJS) competency model separating Framing, Judging, and Steering for generative AI use, with preliminary validation via simulations on an open platform showing skill dissociation and validity.
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6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Clover tool and behavioral taxonomy show tab-accept rates linked to lower attention-check scores and dwell time linked to higher scores in AI-assisted programming tasks.
A study of student pairs finds that misalignment in perceptions of partners' AI use early in collaborative programming projects is associated with lower performance, especially among lower-performing teams.
Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
A participatory workshop showed teens developing technical and socio-ethical understandings while constructing small generative LMs, offering evidence and a theory-backed framing for studying novice AI comprehension.
The paper recommends shifting CS education from implementation skills to AI-native competencies, fundamental concepts, and critical evaluation based on workshop consensus.
citing papers explorer
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Framing, Judging, Steering: An Assessable Competency Model for Teach-ing Students to Reason With Generative AI
Proposes the CoRe-3 (FJS) competency model separating Framing, Judging, and Steering for generative AI use, with preliminary validation via simulations on an open platform showing skill dissociation and validity.
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To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks
Clover tool and behavioral taxonomy show tab-accept rates linked to lower attention-check scores and dwell time linked to higher scores in AI-assisted programming tasks.
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Students' Perception Accuracy of Partners' AI Use and its Relation to Collaboration Performance
A study of student pairs finds that misalignment in perceptions of partners' AI use early in collaborative programming projects is associated with lower performance, especially among lower-performing teams.
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Relationships Between Trust, Compliance, and Performance for Novice Programmers Using AI Code Generation
Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
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Building to Understand: Examining Teens' Technical and Socio-Ethical Pieces of Understandings in the Construction of Small Generative Language Models
A participatory workshop showed teens developing technical and socio-ethical understandings while constructing small generative LMs, offering evidence and a theory-backed framing for studying novice AI comprehension.
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Reshaping Undergraduate Computer Science Education in the Generative AI Era
The paper recommends shifting CS education from implementation skills to AI-native competencies, fundamental concepts, and critical evaluation based on workshop consensus.