We’re excited to announce the second round of our roundtable conversation series.

After receiving insightful suggestions from our previous series, feedback and points to ponder, we want to continue building this platform for conversation. The last theme centered on artifact scoring, which is one of the most burdensome parts of evidence generation for direct evaluation in accreditation processes.

It is also the most crucial component when considering AI-related solutions. The stakes are high, and accuracy drives everything that follows. Curricular recommendations, rescoring, and closing the loop all require accurate calibration of the rubric to begin with.

From what we heard, even selecting an artifact for a given program learning outcome can be challenging when rubric definitions are too vague or subjective to reliably identify the right artifact. This can lead to an even bigger problem: scoring the wrong artifacts.

We want to help reduce the time and burden on faculty. The solution we’ve narrowed in on is a comprehensive rubric design that consistently supports:

• Consistent artifact scoring by faculty or AI

• Rubric definitions that lead to better artifact selection

Starting next month, we’ll be running the same structure: exploring the use case of rubric design in accreditation, hosting a practical workshop on designing an agent to build rubrics, and discussing governance concerns when applying agentic AI to rubric design.

Module 1: The applied use case of rubric design in accreditation, and how agentic AI can help
Module 2: Workshop, designing an agent to build rubrics
Module 3 : Governance and guardrails for agentic AI in rubric design

Registration will be opening soon. Please stay tuned for the details! As before, we’re keeping the group small to keep it a conversation rather than a webinar.

If you were in the room for round one, thank you. This next series exists because of what you raised. If this is your first time joining, welcome in.

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Session Details:

What We Are Exploring

In Module 2, we explored what agentic AI workflows could look like in practice within Assurance of Learning.

Module 3 takes the next step: what needs to be in place for a workflow like this to be adopted responsibly inside an institution?

What We’ll Cover

The conversation is built around three questions:

• Where does governance belong? We treat review checkpoints and approval gates as design decisions rather than a compliance review at the end. We want to test that with you.

• What does your institution actually require? Data security, IRB, FERPA, procurement. We want to hear what the real gates are and who holds them.

• What would responsible adoption look like on the ground? Including how you document AI’s role in a way a peer review team would accept.

You do not need to have any of this figured out. The schools we have spoken to are at very different stages, and that range is part of what makes the conversation useful.

Facilitators

• Daisuke (Dai) Nishimura, Managing Director, Macnica

• Dolma Rawat, Research Lead, Macnica

• Jerelyn Co, AI Engineer, 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.

Register for the Session

Session Details:

What We Are Exploring

Module 1 surfaced where the AoL process feels most burdensome and where AI could genuinely help.

Module 2 moves from that discussion into practice: we’ll walk through what an agentic AoL workflow actually looks like, step by step.

What We’ll Cover

We'll walk through a practical example of an agentic workflow, using it as a starting point for discussion. Along the way, we'll examine how different components work together, where faculty expertise remains essential, and the questions institutions should consider when evaluating similar approaches.

Some of the themes include:

• Where AI adds value—and where it doesn’t

• Human oversight, trust, and workflow design

• Institutional considerations for adopting AI responsibly

• Open practitioner discussion

Facilitators

• Daisuke (Dai) Nishimura, Managing Director, Macnica

• Dolma Rawat, Research Lead, Macnica

• Jerelyn Co, AI Engineer, 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.

Register for the Session