Abstract

Business program assessment is formalized through Assurance of Learning (AoL) processes that emphasize systematic measurement, analysis, and continuous improvement of student learning outcomes (AACSB International, 2026; Borschback & Mescon, 2021). However, these processes remain labor-intensive, episodic, and limited in their ability to generate timely, actionable insights (Kehal, 2020). Shifting long-standing assessment processes from a fully human-driven activity to hybrid, sociotechnical processes, this research advances a co-assessment perspective in which faculty and AI collaborate to assess student learning. We argue that AI’s capacity for rapid, scalable, and consistent assessment of student learning directly addresses persistent challenges in program assessment, such as delayed feedback cycles, faculty time constraints, and uneven rubric application. At the same time, faculty retain a critical role in assessing higher-order thinking, interpreting nuance, and contextualizing performance within disciplinary and programmatic goals. Building on this premise, we expand sociotechnical research perspectives by introducing a human-AI co-assessment model. In this model, generative AI performs initial assessment, providing feedback on the level of performance the artifact demonstrates on rubric criteria. Then, faculty engage in higher-order judgment. Importantly, discrepancies between AI and human evaluations are treated as diagnostic signals that reveal ambiguity in learning outcomes, gaps in rubric design, or variation in student reasoning. This reconceptualization extends existing AoL processes by embedding AI within the continuous improvement cycle central to accreditation standards (AACSB International, 2026). We discuss how the human-AI co-assessment model can be implemented at the program level to support AoL, curriculum improvement, and accreditation reporting. This TREO invites participants to reenvision assessment as a collaborative intelligence system and offers a theoretically grounded framework for integrating human and artificial evaluators in information systems program assessment.

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