A Defense-in-Depth Framework for AI-Resilient Asynchronous Learning

As generative AI renders traditional high-stakes take-home assessments obsolete, distance learning institutions must pivot from a reactive "detection" posture to a proactive "design" posture. To address this problem, this session introduces the Defense-in-Depth (DIDAM) Assessment Model, an original framework developed by the Air Force Global College (AFGC) Design and Development team. Grounded in the DIDAM philosophy formalized by DigitalEd (2025) and synthesizing standards from Quality Matters and the Online Learning Consortium (OLC), this model utilizes four pillars: Process-Based Scaffolding, Situated Contextualization, Targeted Oral Validation, and AI-in-the-Loop Literacy. This session will translate the DIDAM Model into a practical redesign framework for participants. First, it will define the four pillars of the model and map them to Quality Matters and OLC standards, establishing a foundation for implementation. Participants will then analyze faculty workload ROI when shifting from summative grading to a scaffolded assessment ecosystem. Attendees will leave with a repeatable Redesign Pattern used by AFGC Learning Architects to transform legacy assignments into AI-resilient learning experiences and apply these strategies within their own instructional contexts.