Standard · QLM category creation

Adaptive Assessment Evidence Standard for the AI Era

The QLM evidence standard defines how learning becomes evidence when final answers can be generated.

Research brief

The evidence sequence

QLM defines the core sequence as prediction, action, observation, explanation, revision, transfer, and profile update. Each step is a chance to observe reasoning rather than only the final answer.

  • Prediction: what the learner expects before acting.
  • Action: what the learner chooses inside a task or simulation.
  • Observation: what the learner notices after feedback or consequence.
  • Explanation: how the learner justifies the decision.
  • Revision: how the learner changes strategy after evidence.
  • Transfer: whether the learner can apply the idea in a new context.
  • Profile: how the evidence updates a living skills record.

Evidence levels

Not every signal deserves the same confidence. QLM separates activity evidence, reasoning evidence, performance evidence, transfer evidence, and reviewed evidence so institutions can avoid overclaiming.

Review and governance

The standard is designed for educators, faculty, managers, and researchers to inspect. High-stakes use should include review rules, limitations, privacy protections, and fairness checks.

Published by Quantum Learning Machines · 2026-06-02

Next step

Turn the category into a pilot.

Use this path when you want a pilot, research partnership, or product walkthrough.

Explore the standards library