Beyond Detection: Redesigning Authentic Assessment in an AI-Mediated World

The rapid uptake of generative AI (e.g., ChatGPT, DALL·E and MS Copilot) is disrupting conventional notions of authenticity in assessment across higher education. The dominant response, surveillance and AI detection, misdiagnoses the problem. In an AI-mediated world, authenticity cannot be policed into existence; it must be redesigned. Situating AI within contemporary knowledge work shaped by digitisation, collaboration and evolving ethical expectations, we reconceptualise authenticity as something constructed in contexts where AI is expected, declared and scrutinised. The emphasis shifts from what students know to how they apply knowledge, make judgement, and justify choices with AI in the loop. We offer practical design for learning moves, i.e., discipline-agnostic learning design patterns that position AI as a collaborator rather than a cheating application: tasks that require students to critique, adapt and verify AI outputs, provide explicit process transparency (prompts, iterations, rationale) and exercise assessable demonstrations of digital discernment and ethical judgement. Examples include asking business students to interrogate a chatbot-generated market analysis and inviting pre-service teachers to evaluate AI-produced lesson plans for inclusivity and pedagogical soundness. Reflective artefacts such as metacognitive commentary, process logs, and oral defences make students’ thinking visible, substantiate attribute, and reduce reliance on punitive “gotcha” approaches. Our contribution is twofold: i. a conceptual account of authenticity fit for an AI-mediated world, and ii. a set of actionable, discipline-agnostic patterns that can be tailored to local contexts. The result is an integrity stance anchored in design rather than detection, enabling assessment that remains meaningful, ethical and intellectually demanding in the presence of AI, while advancing a broader shift toward assessment paradigms that reflect real-world professionalism.