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SERE PAPER 03
SYSTEMS · EVIDENCE · RECONSTRUCT · EXPLORATION

The Bridge

From knowing to doing
Can AI reduce the distance between what a person knows and what they are able to do?

The model

KNOWLEDGE → SUPPORT → CAPACITY → ACTION → OUTPUT → LEARNING

The problem

A useful answer is not the same thing as increased capability. More activity is not automatically more progress. And support can either strengthen human agency or create dependence.

The SERE test

SERE asks what the person was trying to do, what prevented or delayed action, what AI actually changed, what happened afterwards, what evidence shows that the action mattered, what remained human, and whether the capability survives when the AI is removed.

What current research suggests

Recent research is exploring human-AI fit, shared agency, durable learning and the conditions under which AI supports rather than replaces human judgement. The evidence is developing and includes small or context-specific studies, so SERE does not treat it as universal proof.

Provisional finding

AI may reduce some forms of friction between knowing and doing. Whether it creates meaningful human capability depends on the person, task, quality of support, surrounding system, outcome evidence and whether learning or dependence follows.

The institutional position

SERE is not pro-AI, anti-AI, blindly optimistic or automatically fearful. SERE sits where reality can be examined. The evidence may show that a system should continue, change, stabilise or stop. The method is independent of the conclusion.

Sources

SERE Literature · Research edition · Public evidence, SERE reconstruction, analysis and stated uncertainty.