As generative AI disrupts traditional assumptions about student writing, many instructors have responded with increased control and surveillance. This session argues for an alternative solution grounded in open education and assessment literacy. Drawing on a redesign of an ENG101 course, the presentation explores a shift from teaching essay writing as a product to using essays as representations of critical thinking within a transparent assessment framework. The session highlights a critical gap in higher education: limited faculty preparation in designing valid and reliable instruments for measuring learning. By clarifying learning constructs, aligning assignments with evidence claims, and openly sharing rubrics and criteria, this approach improves transparency, equity, and instructional coherence. The session positions open assessment as a necessary evolution of open education and a pedagogically sound response to AI.
Attendees of this session will be able to:- Explain why traditional essay-based assessments often produce invalid or unreliable inferences about student critical thinking, especially in AI-mediated learning environments.
- Identify the key components of an open assessment framework, including construct definition, evidence alignment, and transparent criteria.
- Describe how open education principles—such as transparency, shared practice, and learner agency—apply to the design of assessments, not just course materials.
- Recognize common misconceptions about measuring learning (e.g., equating product quality with cognitive processes) and how these misconceptions contribute to inequity.
- Articulate how openly shared rubrics, prompts, and assessment criteria can improve instructional coherence and support AI-resilient, equity-centered teaching practices.
