Bridging Technology Quality and Pedagogical Quality: A Two-Level Quality Assurance Stack for Human–AI Collaboration in Digital Learning

The rapid integration of artificial intelligence into digital learning has surfaced a consequential challenge: quality assurance frameworks are evolving far more slowly than the technologies they are meant to govern. This experience paper reports on two complementary inquiries examining quality assurance in AI-enabled digital learning at two distinct but interdependent levels. The first, conducted through a multimodal engagement with the Quality Matters AI Working Group culminating in a design sprint at the QM Board Retreat in November 2025, examined quality assurance at the system level, exploring how organizations might govern, guide, and improve AI adoption in ways that preserve educational integrity. The second, conducted in partnership with D2L between November and December 2025, examined quality assurance at the artifact level, investigating where current evaluation frameworks fall short when applied to AI-generated educational content.

Together, these bodies of evidence converge on a shared insight: quality assurance in AI-enabled environments cannot be reduced to checklists, automated scores, or static rubrics. Artifact-level evaluation without system-level governance produces inconsistent, acontextual judgments; system-level governance without artifact-level evidence produces policy untethered from practice. Drawing on human-centered AI (Shneiderman, 2022), TPACK-informed perspectives on educator knowledge (Gunder & Ford, 2025; Koehler & Mishra, 2009), peer-review approaches to online course quality (Legon & Garrett, 2018), and Hau's (2025) concept of Relational Quotient, the paper argues that quality assurance is an ecosystem problem whose relational dimensions form the load-bearing structure on which technical and pedagogical quality both depend.