Michael Rowe

Tag: critical-thinking

4 items with this tag.

  • Processing fluency

    Processing fluency is the ease with which the mind takes in information. People read that ease as a sign that something is true, familiar, or good, which is why fluent AI-generated text is persuasive whatever its accuracy, and why markers who equate clear writing with clear thinking need to separate the two.

  • The verification trap

    Institutions everywhere now insist on the same rule — use AI, but always verify its output. Yet verifying AI output is hard, slow, and often inconclusive, even in domains where you're competent to judge it. This post pushes that advice to its limit to see what breaks, and finds that as models improve, the verification mandate quietly trains the opposite of the scepticism it intends.

  • Problem-based learning and the structural conditions for productive AI integration

    Higher education's response to AI has focused on the artefact: detecting it, restricting it, and restoring confidence in what students produce. This essay argues that the structural features of problem-based learning — problem-driven inquiry, collaborative knowledge construction, facilitation over instruction, and metacognitive reflection — are the same conditions under which AI integration becomes educationally productive rather than substitutive. The alignment is structural, not retrospective: PBL was designed around these conditions before AI existed. The argument extends further: AI shifts what category of problem PBL can engage with, expanding access to wicked problems previously beyond students' reach. Investing in PBL's structural conditions is simultaneously investing in AI readiness.

  • Why AI writing sounds convincing, and why that's noise

    AI-generated text is easy to read whether or not its content is accurate or well reasoned, and easy-to-read text feels true. Fluency was once a reasonable trace of genuine thinking, since a student who wrote clearly had usually thought clearly, but that relationship no longer holds. The AI literacy response of teaching output evaluation is a temporary fix, because as models improve, output quality converges on expert level across every artefact we care to measure. Spotting today's failure modes leaves open what you'll do when they're gone.