PWR-193 · Human–AI Complementarity

AI-assisted reasoning

AI can improve selected reasoning tasks in a declared configuration, but it can also transmit error and add verification work.

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Revision 7T · Full Tutorial Edition · Release manifest · Methodology · Corrections

One source of teaching truth

Canonical Power learning unit · TLU-PWR-193

On answer-key logic cases, the learner compares human-only, AI-only and combined answers, verifies each decisive premise and reports harmful acceptance.

Canonical Power page
PWR-193 · AI-assisted reasoning
Full tutorial
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Practical authority
The tutorial teaches a low-risk method that may be practised inside its stated limits.
Current treatment
Full low-stakes tutorial
Research depth
deep · 9 bound sources
Risk framing
moderate to high governance
Capability self-practice
Permitted inside the tutorial's stated low-risk limits
Pathway membership
G-CUR-021

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Explanation is not permission.

This dossier explains the evidence and its limits. It is not a diagnosis, personal recommendation, assessment, clearance, performance promise or training programme. Actionable teaching appears only when the canonical Power record explicitly authorises it.

Complete bounded explanation

What this power means.

Capability to develop or express ai-assisted reasoning in a declared context without inheriting broader claims.

What the current evidence supports

AI can improve selected reasoning tasks in a declared configuration, but it can also transmit error and add verification work.

How to observe or measure it without overclaiming

Accuracy, calibration, time, verification effort and harm-weighted error versus human-only, AI-only and the better standalone baseline. This measures the declared configured system and cannot by itself establish broad transfer, unaided ability, safety or legitimacy.

Myth

AI makes a person universally smarter

Metric

Accuracy, calibration, time, verification effort and harm-weighted error versus human-only, AI-only and the better standalone baseline

Boundary

Report person, device/model/interface, task, assistance, failure state and comparator; the metric is not proof of unaided general capability.

Negative and limiting findings

  • Explanations did not reliably create complementarity and incorrect advice sometimes reduced performance below the human-only baseline.
  • No cited evidence supports perfect, universal or consequence-free performance.

Claims this evidence cannot support

  • AI makes a person universally smarter
  • The tool or robot's output proves an unaided, universal or permanent human superpower
  • A successful laboratory task proves independent real-world or clinical capability

Individual tutorial · FULL SELF GUIDED TUTORIAL

How to learn this Power now.

On answer-key logic cases, the learner compares human-only, AI-only and combined answers, verifies each decisive premise and reports harmful acceptance.

  1. Get readyUse a harmless answer-key task and exclude personal, proprietary or regulated data.
  2. Learn the method8 concrete steps teach the permitted method from start to finish.
  3. See right and wrong4 comparisons show correct or safer execution beside common wrong or riskier choices.
  4. PractiseUse 8–12 answer-key items once per week. Retest four unseen items without AI after 48 hours and change the AI error mix monthly; progress only when combined beats the better standalone baseline without more severe errors.
  5. Check progressBaseline fixture: A 12-item fictional train-scheduling puzzle has a source sheet of departure rules. A declared AI system gives six correct and six deliberately flawed explanations. Enter success checks from “Lock the task and version” and “Search for a counterexample”. If “Ask AI first and reconstruct a “human” answer later.” occurs, apply its named fix; then score Accuracy, calibration, time, verification effort and harm-weighted error versus human-only, AI-only and the better standalone baseline on the unused “Retest without AI” item.

Authority boundary: Titan teaches a concrete low-stakes method step by step while keeping evidence and transfer claims bounded. The method is Power-specific; its actionability follows this treatment.

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Related Powers: PWR-194 · PWR-195 · PWR-196 · PWR-197

Direct evidence register

Sources that support—and limit—the claim.

Citation roles are explicit. A source may support existence or trainability while simultaneously limiting transfer, certainty, generalisation or safety.

  1. Limiting / contrary
    Humans inherit artificial intelligence biases

    Lucía Vicente; Helena Matute · 2023 · Primary research

  2. Primary empirical supportLimiting / contrary
    Explainability does not mitigate the negative impact of incorrect AI advice in a personnel selection task

    Julia Cecil; Eva Lermer; Matthias F. C. Hudecek; Jan Sauer; Susanne Gaube · 2024 · Primary research

  3. Primary empirical supportLimiting / contrary
    Experimental evidence on the productivity effects of generative artificial intelligence

    Shakked Noy; Whitney Zhang · 2023 · Primary research

  4. Primary empirical supportLimiting / contrary
    Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance

    Gagan Bansal; Tongshuang Wu; Joyce Zhou; Raymond Fok; Besmira Nushi; Ece Kamar; Marco Tulio Ribeiro; Daniel S. Weld · 2021 · Primary research

  5. Primary empirical supportLimiting / contrary
    To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making

    Zana Buçinca; Maja Barbara Malaya; Krzysztof Z. Gajos · 2021 · Primary research

  6. Limiting / contrary
    The impact of AI errors in a human-in-the-loop process

    Ujué Agudo; Karlos G. Liberal; Miren Arrese; Helena Matute · 2024 · Primary research

  7. Limiting / contrary
    Effect of Uncertainty-Aware AI Models on Pharmacists' Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial

    Corey Lester; Brigid Rowell; Yifan Zheng; Zoe Co; Vincent Marshall; Jin Yong Kim; Qiyuan Chen; Raed Kontar; X. Jessie Yang · 2025 · Primary research

  8. Limiting / contraryOfficial boundary context
    Artificial Intelligence Risk Management Framework (AI RMF 1.0)

    Elham Tabassi; National Institute of Standards and Technology · 2023 · Official authority

  9. Limiting / contraryOfficial boundary context
    Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile

    C. Autio; R. Schwartz; J. Dunietz; S. Jain; M. Stanley; E. Tabassi; P. Hall; National Institute of Standards and Technology · 2024 · Official authority

Current teaching and evidence boundary

The next gate remains visible.

External confirmation is required before higher-authority use because: a clinical, high-risk or regulated configuration.

A Power-specific tutorial is available at /tutorials/pwr-193/. It contains a plain-English method, 8 ordered steps, a worked example, right-versus-wrong comparisons, specific mistakes and corrections, practice, measurement, accessibility, stopping rules and evidence. Its self guided mode remains controlling.