Session Details:
Date & Time: Wednesday, October 21 · 10:00–11:00 AM PT
Who It’s For: Deans, AoL directors, assessment leaders, and faculty at AACSB-accredited business schools.
What We Are Exploring
Rubrics define how student work is evaluated against program-level learning outcomes. But unclear or overlapping criteria, and gaps in the available evidence, may surface only during scoring, when it is too late to revise the rubric or collect different evidence.
In this Module 1, we’ll examine an applied rubric-design use case and discuss where agentic AI could support the process while faculty retain responsibility for the final rubric.
What We’ll Cover
We’ll walk through a practical example of rubric design and refinement, using it as a starting point for discussion. Together, we’ll consider the decisions involved, the challenges that tend to emerge, and where AI support may or may not be useful.
Together, we’ll consider questions such as:
How can program-level learning outcomes be translated into clear, measurable criteria?
What helps faculty interpret and apply rubric criteria consistently?
Where could agentic AI support rubric design, and which decisions should remain with faculty?
Facilitators
Daisuke (Dai) Nishimura, Product Manager, Macnica
Dolma Rawat, Research Lead, Macnica
About the Roundtable Discussion
Our roundtables are practitioner-led discussions built to explore emerging questions around AI and accreditation. They’re intended as a space for shared learning, thoughtful discussion, and exchanging perspectives across institutions.
Interested in Participating?
If this topic resonates with you and you’d like to take part in this roundtable or future ones, we’d love to hear from you.
Register using the button below. If you have any questions, reply to this post or get in touch—we’re always looking to bring new perspectives into the discussion.