ACCELERATE: Assessment Principles for Best Practice (2025) updates the foundational American Association for Higher Education (AAHE) principles with contemporary emphasis on equity, collaboration, and transparency. This pilot study documents the development of an AI chatbot providing programmatic assessment guidance grounded in ACCELERATE principles, examining which principles translate effectively to AI-mediated coaching versus those requiring direct human involvement. Using retrospective analysis of seven authentic challenges from Year 1 programmatic assessment evaluations at one institution, we examined AI-mediated coaching responses for evidence of effective principle translation and structural tensions that emerge when human-centered values encounter algorithmic operationalization. Preliminary findings suggest that six principles may translate effectively to AI support, while four (Collaborative/Co-Creative, Energized by Expertise, Responsiveness-Oriented, and Enduring/Evolving) carry structural tensions that appear to resist technological resolution. We propose a framework that positions AI as a tool for technical work, while humans remain guarantors of relational work in principled assessment practice.
Stavros Hadjisolomou, Rita W. El-Haddad· Intersection: A Journal at t...· 0 citations
Autonomous artificial intelligence (AI) agents can now log into a learning management system, read course materials, and complete unproctored, asynchronous assessed work end-to-end with no student involvement. We document that capability and trace its consequences for assessment validity. Three demonstrations on a live undergraduate course supply the evidence: two quiz completions, one in approximately 12 minutes, one in under 5, and a third in which the agent fabricated credible personal reflection for a discussion board. The wider public record includes at least 15 documented agent runs across three platforms and seven tools. We apply Kane’s argument-based validity framework: agent completion removes the attribution on which every inference in Kane’s chain depends. Everything downstream, from course grades to the evidence chains behind program review and accreditation, rests on support that is no longer there. The failure concerns validity rather than integrity: an institution can punish misconduct and still lack grounds for the scores it reports. Collective accreditor guidance addresses institutional uses of AI in evaluation and does not yet reach the agentic case. Audience data from the underlying conference session show attendees already recognizing both the vulnerability and the gap in institutional guidance. Polled attendees most often named online quizzes as agent-completable, with discussion-based work close behind. Majorities in both listings were working without settled written guidance. The response defended here is design rather than detection: four principles for verified human presence, low-effort changes faculty can adopt now, and the assurance levers assessment professionals already operate.
Stavros P. Hadjisolomou, R. El-Haddad· Intersection: A Journal at t...· 0 citations
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