Proposes a six-move framework (Prime, Probe, Point, Attach, Strengthen, Test) for learning with AI, using an 'effortless' diagnostic to avoid illusion of mastery, backed by cited evidence of design-dependent outcomes including 17% harm from unguarded AI and doubled gains from engineered tutors.
Developing evaluative judgement for a time of generative artificial intelligence.As- sessment & Evaluation in Higher Education, 49(6):893–905, 2024
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6representative citing papers
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.
A framework mapping nine prompt patterns to productive struggle and evaluative judgement for AI-supported secure coding education.
Transcript analysis of students using ChatGPT during exams revealed progressive interaction patterns that shift assessment focus from solution production to solution verification.
AI to Learn 2.0 is a deliverable-oriented framework with a seven-dimension maturity rubric and capability-evidence ladder that permits opaque AI for exploration but requires final outputs to be auditable, transferable, and supported by human-attributable evidence.
Proposes task-specific GenAI declaration structures for writing and coding tasks to move beyond binary disclosures in higher education.
citing papers explorer
-
The Effortless Trap: Productive Struggle, AI, and the Illusion of Learning
Proposes a six-move framework (Prime, Probe, Point, Attach, Strengthen, Test) for learning with AI, using an 'effortless' diagnostic to avoid illusion of mastery, backed by cited evidence of design-dependent outcomes including 17% harm from unguarded AI and doubled gains from engineered tutors.
-
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.
-
Beyond AI Delegation: A Prompt Pattern Framework for Productive Struggle and Evaluative Judgement in Secure Coding Education
A framework mapping nine prompt patterns to productive struggle and evaluative judgement for AI-supported secure coding education.
-
Reimagining Assessment in the Age of Generative AI: Lessons from Open-Book Exams with ChatGPT
Transcript analysis of students using ChatGPT during exams revealed progressive interaction patterns that shift assessment focus from solution production to solution verification.
-
Structuring Transparency: Developing Domain-Specific Generative AI Declaration Frameworks in Higher Education
Proposes task-specific GenAI declaration structures for writing and coding tasks to move beyond binary disclosures in higher education.