How can we bridge the space between teaching complex theory and making an assessment accessible? This session reframes Generative AI as a living OER for high-quality STEM assessment. Moving beyond basic automation, we explore how AI acts as a technical partner in designing project-based learning.
Using a group project on relativistic effects for a trip to Alpha Centauri as a case study, the presenter will demonstrate how AI audits student logic for time dilation and length contraction. Participants will then engage in AI-assisted assessment building to create their own rubrics aligned with Quality Matters and WCAG 2.1 AA standards, including generating ARIA labels for digital interactives. Attendees will leave with a scalable framework for coordinating consistent, accessible, and inclusive digital materials across STEM departments.
Attendees of this session will be able to:- Define and apply the AI-as-OER framework to transition from static teaching materials to dynamic, adaptable STEM assessments that align with specific Course Learning Outcomes (CLOs).
- Construct a multi-stage project rubric for complex scenarios, such as relativistic time dilation in deep-space travel, that ensures transparent grading and high student engagement.
- Perform an accessibility and quality audit on AI-generated content to ensure all digital assessment materials meet WCAG 2.1 AA standards and Quality Matters benchmarks.
