Generative AI can support students in producing essays, code, analyses, and multimodal work with varying levels of independent effort. This can make it more difficult to determine what student work reflects about their own understanding, reasoning, and capabilities. Given the limitations of current AI-detection tools, the challenge is not simply stronger surveillance, but assessment design that helps preserve meaningful evidence of student learning when generative AI is used.
The AI-Resilient Assessment Design Framework is a structured, evidence-informed model for designing assessments that keep students’ understanding, reasoning, and capabilities visible, whether or not generative AI is used. It supports assessment validity while also helping students develop AI-ready skills.
determines what GenAI is permitted to do in producing the assessed work. It specifies not only how much AI use is allowed but also what the tool may do, at which stage of the task, and under what conditions.
generates evidence that the student possesses the competence the finished work implies. This evidence may come from visibility into the student’s process and interactions, dialogic or performative demonstrations of understanding, and transfer, multimodal, or triangulated evidence of retained capability.
establishes the responsibilities the student retains for AI-assisted work through transparency and provenance, validation and ethical responsibility, and ownership, reproducibility, and defensibility of the final work.
| Level | Authorization | Verification | Accountability |
|---|---|---|---|
| Level 1No AI | Generative AI is excluded from the assessed task. Students are not allowed to use AI. | Standard supervision or in-class conditions. No further evidence is required beyond the assessed performance. | Ordinary academic-integrity expectations apply. |
| Level 2Ideation & planning | AI supports brainstorming, structuring, and organization. Analysis and drafting remain the student’s own work. | A planning note, source map, or brief oral discussion of central decisions. | A short AI-use statement identifying the tools used and the purposes served. |
| Level 3Editing & revision | AI supports language, style, and revision. Core disciplinary decisions remain the student’s. | Version comparison or revision rationale, and/or a brief oral spot-check explaining key revisions and disciplinary decisions. | A record of accepted and rejected AI edits; independent fact-checking and source verification. |
| Level 4AI completion, human evaluation | AI generates substantial elements. The student selects, evaluates, revises, and integrates the outputs. | Demonstrate competence through an oral explanation, parallel assessment task (assessment twin), or transfer task, supported by evidence of key decisions made during the work. | Disclose how AI was used, check the accuracy and ethical appropriateness of its output, and/or accept responsibility for the final work. |
| Level 5Full AI | AI creates most of the content. The student guides it, checks everything, and explains the result. | A real-time walkthrough in which the student explains, adapts, or justifies the work while demonstrating retained capability. | A clear record of how AI was used, and the student defends the work. |
AI-Resilient Assessment Design Framework. Dilinika, J. M. S. (2026). Authorization levels adapted from Perkins et al. (2024).
Licensed CC BY 4.0 — free to share and adapt with credit. · Questions or feedback: shalanijayamanne@gmail.com
For the full framework in detail and examples, read the full guide →please visit ai-resilient-assessment-framework.org