Whitepaper
Design Heuristics for Assessment WITH Learning: A Framework for AI-Integrated, Human-Centered Assessment Design
As generative AI transforms how students learn, think, and produce work, traditional assessment models face unprecedented challenges. With 71% of higher education professionals reporting student use of AI tools for brainstorming and problem-solving, the assumptions underlying assessment OF, FOR, and AS learning are no longer sufficient. This whitepaper offers education publishers and EdTech organizations a practical, research-backed framework for designing assessments that preserve pedagogical integrity while embracing AI as a collaborative learning partner.
What You'll Learn
- The Shifting Assessment Landscape: Understand how AI has fundamentally destabilized assumptions about independent cognition and why human-AI co-production demands new assessment approaches.
- Assessment WITH Learning Framework: Discover five integrated dimensions—Dialogue, Feedback, Transparency, Ethics & Agency, and Reflection—that redefine assessment as collaborative sense-making where knowledge construction and evidence creation happen concurrently.
- Five Actionable Design Heuristics: Learn how to implement Co-Presence, Explainability, Reflection Layer, Traceability, and Human-in-the-Loop principles that operationalize AI-integrated assessment while maintaining human judgment and fairness.
- Balancing Innovation with Integrity: Explore critical considerations around algorithmic bias, cognitive offloading risks, and data governance requirements (GDPR/CCPA compliance) essential for ethical AI-integrated assessment.
- Strategic Product Development Opportunities: Gain insight into how educational publishers and EdTech organizations can differentiate through embedded transparency layers, process data capture, modular assessment frameworks, and privacy-by-design architecture.
- From Theory to Practice: See concrete examples of how to embed AI disclosure tracking, version-history dashboards, and automated reflection prompts into assessment workflows without adding learner burden.