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.
Enhancing teaching through constructive alignment
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
The authors report that an AI-assisted harness enabled weekly closed-book tests to replace lectures in one small upper-level course while preserving student accountability, based on survey data from 18 students and project git history.
A framework mapping nine prompt patterns to productive struggle and evaluative judgement for AI-supported secure coding education.
A methodology for undergraduate mathematics education relocates evidential trust from polished written work to live human explanation and cumulative observation to address AI assistance.
citing papers explorer
-
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.