Reimagining Success and Failure: Equitable Assessment Practices in an Age of Artificial Intelligence

Synthesis: Dollinger and Nieminen (2026) present a conceptual paper arguing that GenAI's disruption of Assessment is an opportunity — not a threat — to fundamentally reimagine how student success and failure are defined in higher education. Rejecting the containment approaches (surveillance, invigilation, AI-detection) that dominate current institutional responses, they contend that GenAI exposes what was already true: that Assessment practices have long been broken, sorting and ranking students in ways that systematically disadvantage those from equity-deserving backgrounds. The paper proposes reimagining success along two dimensions — a shift from individualistic to distributed understandings of knowledge, and a move from predetermined standards toward agentic assessment that positions students as active participants in defining success — with profound implications for Authentic Assessment and grading.

Key Findings

• Assessment was already broken; GenAI just made it undeniable: Decades of Assessment research showing inequity remain unaddressed or even exacerbated. Universities have long relied on assessments that sort, rank, and grade students, measuring reproduction of knowledge under artificial constraints rather than how knowledge is actually created or used. GenAI challenges individual authorship so fundamentally that maintaining current practices is no longer possible without explicit justification.
• Rejecting both dominant equity approaches: The paper critiques (1) the accommodations model, which places the burden to adapt on individual "at risk" students, and (2) structural critiques that reframe assessment as disabling students but still leave intact the zero-sum logic that "for one student to succeed, someone else needs to fail" — what Nieminen (2024) calls the paradox of inclusive assessment.
• From individualistic to distributed success/failure: The myth that valid knowledge resides within a single individual privileges students who excel in isolated, high-pressure performance (e.g., closed-book, time-constrained exams, which show differential outcomes by gender, socio-economic status, race, and disability) while disadvantaging collaborative, resource-rich learners. GenAI makes the inherently social, distributed nature of knowing impossible to ignore, reframing group work and collaborative process documentation as evidence of "integrated knowledge networks."
• From predetermined to agentic success/failure: Standardisation rooted in industrial-era values fixes learning outcomes before students arrive, positioning them as performers rather than agents. Agentic assessment structurally enables students to participate in defining what counts as success and failure — negotiating formats, criteria, and timelines — and validates diverse ways of knowing within academic structures.
• Reframing around detection of learning, not detection of cheating: Rather than asking "what can't GenAI do," educators should ask what students should learn through assessment: navigational choices, reflections, and judgements (i.e., "detecting learning rather than detecting cheating," per Ellis & Lodge 2024).
• Grading reform (the "elephant in the room"): Grades are the most powerful cultural marker of success/failure. The paper advocates competency-based or pass/fail frameworks (which can be as rigorous as rubrics), process-documented portfolios, "persuasive portfolios," navigational-capability scoring, and student self-grading — noting that we might "grade our grading systems rather than our students."

Implications for AI in Education

For educators and institutions, the paper reframes GenAI from an academic-integrity crisis into a catalyst for equitable Assessment reform. Its central practical shift is from surveillance and detection (AI-detection tools, invigilated high-stakes exams) toward process-oriented, agentic, and collaborative assessment that values diverse ways of knowing. This connects directly to Authentic Assessment — assessing navigation of complex, resource-rich environments rather than static individual output — and to Agency, positioning students as partners and drivers of their learning rather than subjects of external judgment. For distributed and collaborative assessment, it points to reimagining group work so teams document how they synthesise perspectives and co-create ideas, and to competency-based/portfolio models of human-AI collaboration. The paper is explicit that institutional constraints (grading policies, accreditation, external ranking systems) are real, recommending localised experimentation within individual courses as an evidence-generating entry point, while calling for future empirical research on the equity outcomes of grading reform.

Connected Concepts

• Assessment
• Equity In AI Education
• Generative AI
• Higher Ed
• Authentic Assessment
• Agency

Connected Articles

• Beyond Detection Authentic Assessment AI 2025 — Redesigning authentic assessment rather than detecting AI misuse
• Fenton Oral Exams AI Authentic Assessment 2025 — Oral exams as an authentic assessment strategy amid AI
• Nicola Richmond Programwide Assessment GenAI 2025 — Program-wide collaborative redesign of assessment in the GenAI era
• Prompt Privilege Equitable AI Access 2026 — Equitable access and outcome gaps in AI-mediated tasks

Citation

Dollinger, M., & Nieminen, J. H. (2026). Reimagining Success and Failure: Equitable Assessment Practices in an Age of Artificial Intelligence. Journal of University Teaching and Learning Practice, 23(1). (CC BY-ND 4.0.)