Less time on documentation.
More time improving learning.

Assessment for accreditation follows a familiar pattern across systems like AACSB and ABET: reviewing student work, applying evaluation criteria, and documenting evidence of learning outcomes.

This work sits at the intersection of faculty workload, institutional accountability, and continuous improvement — yet remains one of the least well-supported workflows in higher education.

We’re starting by looking at a few of the most challenging parts:

• Making expectations clear and measurable before student work is collected, so evidence gaps surface early.
• Helping faculty apply the same criteria consistently, with rationale and evidence behind scoring decisions.
• Seeing student performance sooner, with traceable evidence to identify where attention may be needed.

We’re testing one principle: AI supports the work, and faculty own every decision.

Aolia is an initiative of Macnica, a global technology solutions company headquartered in Japan.

Aolia grew from earlier research into how competencies are reflected in courses and student work. That research raised a practical question: how can schools understand what students have learned beyond what course grades alone can show? Exploring that question led us to accreditation and the assessment work behind it.

We are developing Aolia through ongoing conversations with faculty, assessment directors, and accreditation leaders. Their experience helps shape the workflows, priorities, and safeguards we build. We share what we learn along the way, including the questions that are still open.