The emergence of generative AI invites us to re-evaluate not just what we teach, but how we assess student learning. This theme focuses on the transition from “AI-detecting” to “AI-integrating” or “AI-resilient” course design. The main considerations here are academic integrity and learning objectives: how can we ensure students are still meeting core learning goals while acknowledging the tools they have at their disposal? By using frameworks like Bloom’s Taxonomy to identify tasks that require uniquely human cognitive effort, or by adopting “authentic” assessment models that prioritize process over final product, you can create a curriculum that remains rigorous and relevant in the AI era.
The following resources offer practical frameworks, “copy-paste” prompt templates, and discipline-specific strategies to help you navigate the impact of generative AI. These tools are designed to prioritize student thinking and maintain academic rigor while reducing overreliance on automation.
This comprehensive guide organizes assignment redesign into a three-tiered strategy: securing the foundation with AI-resistant tactics, prioritizing the learning journey through process-based grading, and partnering with technology for innovative, AI-integrated projects.
This resource focuses on five core principles to encourage authentic student ownership of learning. It provides actionable strategies for making student thinking visible through scaffolded milestones, personal anchors, and reflective checkpoints.
This instructor guide features six adaptable activities designed to transform AI from a shortcut into a collaborator for critical thinking. Built on the “Co-Intelligence Spiral,” these exercises help students debate complex topics, audit research sources, and refine their reasoning.
A narrative guide and “copy-paste” toolkit designed to help instructors build assignments that treat AI as a supportive scaffold. It includes ready-to-use assignment starters for reflections, case studies, and fact-checking tasks.
This practical template helps instructors use AI to design high-engagement class sessions. It features customizable “prompt starters” that align with pedagogical best practices to ensure AI-assisted prep remains ethical and effective.
Specifically tailored for quantitative disciplines, this guide identifies where AI excels and struggles in math-heavy contexts. It offers strategies like explanation-embedded problems and error-detection tasks to ensure assessments reward critical reasoning.
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