A Human Capability Test for Learning Outcomes in the AI Era

Synthesis: This paper argues that generative AI has broken the traditional link between submitted work and student capability, so universities can no longer assume that the quality of submitted work reliably demonstrates what a student can do. The author proposes a human capability test built around three questions: what must a student demonstrate independently, what can be strengthened through AI augmentation, and what must the student verify, defend, and take responsibility for. An engineering material-balance problem illustrates how the test operates at the level of an individual learning outcome. The framework shifts the assessment question from whether AI was used to what capability remains the learner's own.

The Problem

Permission policies for GenAI increasingly recognize its implications for university learning, but they do not provide evidence of what students have actually learned. This breaks the traditional assumption that submitted work quality demonstrates student capability.

The Human Capability Test

The proposed test reframes Assessment around three questions: what must a student demonstrate independently; what can be strengthened through AI augmentation; and what must the student verify, defend, and take responsibility for. It is intentionally non-prescriptive and adaptable across disciplines. An engineering material-balance problem illustrates how the test operates at the level of an individual learning outcome.

Implications

By shifting the Assessment question from whether AI was used to what capability remains the learner's own, the framework offers a principled way to preserve academic integrity and authentic evidence of learning in the AI era. It gives instructors a practical, outcome-level procedure for deciding what to assess independently versus with AI augmentation.

Connected Concepts

• Assessment
• Academic Integrity
• Authentic Assessment
• Generative AI
• Higher Ed

Connected Articles

• Beyond Detection Authentic Assessment AI 2025 — Beyond Detection: Authentic Assessment and AI
• Coauthorship Integrity Reconceptualising Assessment Validity For The Age Of Gene — Reassessing Authorship and Integrity for the Age of GenAI
• Kofinas Generative AI Authentic Assessment Integrity 2025 — GenAI, Authentic Assessment and Integrity
• Taylor Lacroix Purpose Before Policy Academic Integrity 2026 — Purpose Before Policy in Academic Integrity
• Lodge Adaptive Capabilities GenAI Future 2026 — Adaptive Capabilities for a GenAI Future

Citation

Saleh, N. B. (2026). A Human Capability Test for Learning Outcomes in the AI Era. EdArXiv preprint.