A rubric for evaluating student work when AI is part of the learning process, with a focus on intellectual ownership, critical evaluation of AI output, depth of thinking, and transparent, ethical use.
A practical checklist for designing intellectually rich assignments that make student thinking visible, clarify appropriate AI use, require judgment and reflection, and reduce opportunities for misrepresentation.
A guide to designing learning tasks for the AI era by making the learning process visible, deepening thinking, assessing judgment over output, and reconsidering what meaningful academic integrity looks like when AI is available.
Sample redesigned learning tasks for Social Studies, Biology, and English Language Arts that demonstrate ways students can use AI to challenge ideas, evaluate information, revise their thinking, and defend their decisions.
A collection of discussion prompts to help educators explore digital assessment, AI-resistant assessment design, available technology tools, formative and summative assessment, and the validity of student evidence of learning.
Sample learning tasks in Economics, Mathematics, and Cognitive Bias that show how AI can be incorporated as a tool for simulation, error analysis, critique, and deeper disciplinary thinking.
A visual framework for shifting assessment from answer production toward intellectual ownership and critical thinking through visible thinking, evaluation of AI output, human explanation, and refinement of ideas.
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