Beyond the Hype: Learning Recognition in the Age of AI

Introduction
Across projects as diverse as INFUSE, national credit transfer initiatives, and cross-border recognition pilots, the same tension emerges: Should recognition systems be designed to optimize institutional scale and efficiency, or to nurture the learner’s experience, agency, and trust?

Today, that tension is amplified by the hype surrounding artificial intelligence. As institutions race to adopt AI-driven processes, the design pendulum is swinging hard toward automation — often without the learner’s voice in the room.

The AI Adoption Cycle and Its Influence
We are in the “peak hype” phase of AI adoption, where:

• Leaders are told that if a solution isn’t AI-first, it’s already behind.
• Vendors pitch automation as the fastest route to transformation.
• Funders see AI as a lever for measurable scale.

While AI can deliver consistency and speed, the adoption cycle skews priorities. Efficiency is easy to measure; trust, belonging, and engagement are not. That imbalance encourages decisions that remove human touchpoints in favor of algorithmic processing.

The Recurring Shift in Recognition Projects
In many initiatives, early visioning sessions start with a focus on transparency, guided exploration, and empowering the learner to chart their own path. But as workshops progress — especially when student voices are absent — the emphasis shifts. The design lens narrows to back-office automation, reducing interaction to the minimum necessary for processing.

This shift may produce faster turnaround times and reduced staff load. But it risks leaving learners feeling processed rather than recognized, and informed rather than inspired.

Why It Matters for the Ecosystem
Learning recognition is not just about determining equivalency; it’s about affirming value. When recognition systems become purely transactional, they erode trust, diminish the learner’s sense of belonging, and weaken the relationship between the individual and the institution.

AI can simulate guidance and predict outcomes, but it cannot feel the stakes of a learning journey. It cannot replicate the moment of human affirmation that tells a learner, “Your experience matters here.”

A Familiar Story
In The Wonderful Wizard of Oz, the spectacle of the Wizard’s power was more about perception than reality. Today’s AI-driven recognition systems risk the same trap: sophisticated on the surface, mechanical underneath. When learners see behind the curtain, the perception gap can be jarring — and trust, once lost, is hard to restore.

The Question That Matters
At the core of every recognition design is a choice:

Do we treat learning as a transaction to optimize, or as a relationship to nurture?

This is not a rhetorical question. It has direct consequences for governance, design, and the long-term health of our ecosystem.

A Call to Balance
Efficiency and empathy are not mutually exclusive. Automation can support scale and consistency, but it should be in service of — not in place of — human connection. The learner’s voice must be embedded from the outset, ensuring that recognition systems serve both the head and the heart.

The GDN, DSU, ATAIN, JETX, INFUSE, PCCAT, ANZ, CTDL, LER, EduCTX, OECD/Academic, X5GON, Cedefop / UNESCO and many other initiatives that we – and I have participated in – have an opportunity to lead by example: building systems that see learners not as records to be processed, but as participants in a shared journey where every credit is both a data point and a milestone in a personal story.