Abstract:

Artificial intelligence tools can support workplace learning, but they can also encourage superficial understanding. When learners or practitioners accept fluent output too quickly, without examining its assumptions, limits, or alternatives, the result may seem convincing yet remain weakly grounded in experience and judgment.

This paper examines the risk in workplace settings where knowledge must be explained, reviewed, and reused by others. It argues that faster text production does not automatically lead to better learning and may even weaken it when reflection and peer review are reduced.

To address this problem, the paper proposes using quality gates in knowledge externalization. These include contextual anchoring, source traceability, explicit conditions of use, consideration of alternative explanations, and recorded peer validation. Together, these features make externally captured knowledge easier to question, discuss, and reuse in learning processes.

The paper argues that workplace learning benefits most from digital support when it strengthens reflection, explanation, and professional judgment rather than replacing them.

Keywords:

AI-assisted learning, Workplace learning, Quality gates, Tacit knowledge, Knowledge externalization, Peer review, Counter-hypothesis, Provenance, Human oversight, Reviewable learning, Reflective practice, Learning design.