How Do I Redesign Assessment So That a Grade Still Tells Me Something Defensible About What the Student Knows or Can Do?

Begin by stating explicitly what capability the grade is supposed to represent. Are you assessing what a student can do independently, what they can accomplish appropriately with AI, their ability to evaluate and direct AI, or some combination of these? The knowledge base's Assessment Validity treats this as a validity problem: a polished artifact is no longer sufficient evidence that the student possesses the capability apparently demonstrated by that artifact. This is the assessment-side half of the same argument; the enforcement-side half is in How Can I Reduce AI Cheating in My Course?, and the general validity framing runs through What Measures and Research Methods Can an Instructor Use to Evaluate AI-Related Interventions?.

The risk of construct substitution

The article From Authentic Products to Authenticated Processes calls the resulting risk construct substitution: the assessor attributes an AI-mediated product to the student and inadvertently measures the tool's capabilities rather than the student's. Its recommended response is to make the student's reasoning, judgment, verification, iteration, and responsibility visible through such approaches as staged submissions, annotated decision rationales, process records, feedback-use statements, oral defenses, and other forms of authenticated process evidence.

Mohamed and Temimi reframe the same problem as one of imperfect information rather than morality: the student knows how the work was produced and the institution sees only the artifact plus partial traces, so the design question becomes which student response each assessment environment makes most attractive. Their response-region model compares three choices — no AI use, disclosed use, and hidden use — and shows that rules, monitoring, disclosure, and redesign operate through different channels and succeed only when the most attractive response aligns with the assessment's purpose. Two results bear directly on architecture. Deterrence runs through a detector's discrimination between hidden use and legitimate work, not its raw catch rate, so when false positives rise faster than true positives stronger monitoring can make concealment relatively more attractive. And redesign moves students toward responsible use only when the rubric actually rewards process evidence; otherwise it stays cosmetic. Their practical rule is to design for the student most tempted to conceal rather than the most conscientious one.

A strong assessment architecture

In practice, a strong assessment architecture often combines an AI-enabled authentic task with some form of independent verification. Depending on the discipline, that might include a brief oral explanation, in-class application, live demonstration, annotated portfolio, short unassisted component, or questioning about key decisions.

The Authentic Assessment concept also stresses realistic intellectual work, cognitive challenge, student agency, feedback, and social or professional authenticity — not merely making conventional assignments harder for AI to complete. Oral and live components need careful attention to anxiety, disability accommodations, linguistic differences, and assessor bias.

One popular no-surveillance architecture — per-student task variation, where each examinee gets a surface-distinct but construct-equivalent version of the same task so answers cannot usefully be shared — is capability-conditional rather than free. VARIA, a Benchmark of 600 generated variants across 60 condition cells, found frontier models clustering tightly on a joint integrity score (0.81–0.88) while non-frontier references collapse to 0.50–0.55, and that no single prompting strategy optimizes surface diversity and construct equivalence at once. Variation-at-scale cannot be assumed from prompting alone, so an institution should validate its own model–prompt pair before treating the guarantee as real.

Redesign also rarely works assignment-by-assignment. In a qualitative study of 12 academics and 17 students at a large Australian university, Nicola-Richmond et al. found both groups agreed assessment must change but stressed systemic friction — long lead times, large-cohort accreditation constraints, workload and cost — and concluded that redesign takes a village: a program-wide team combining assessment-design, generative AI, subject-matter, industry and evidence expertise, with GenAI-literacy and assurance-of-learning points placed strategically across a qualification. The task-level method that makes this tractable is the one McCorkle documents: inventory every step a student performs, ask of each step "what, specifically, am I assessing?", and derive the AI boundary from the answer — which is also how the expectations behind How Can I Reduce AI Cheating in My Course? become explicit and enforceable.

The purpose matters as much as the mechanics. El Khoury and Ma argue that reform organized only around preventing misconduct is too defensive and propose joyful assessment — safe, emotionally responsive, empowering and supportive of student Learner Agency — in which integrity is a consequence of good design rather than its starting point. Their worked example uses an instructor-built AI agent to rehearse students before judgment and to produce a rubric-aligned evidence report, but the division of labor is stated plainly: the AI organizes evidence, the instructor interprets it.