Co-Designing a Checklist to Assess the Risk of AI-Induced Skill Erosion in Professional Knowledge Work · Final Review · Score 30/30Co-Designing a Checklist to Assess the Risk of AI-Induced Skill Erosion in Professional Knowledge Work · Final Review · Score 30/30Co-Designing a Checklist to Assess the Risk of AI-Induced Skill Erosion in Professional Knowledge Work · Final Review · Score 30/30Co-Designing a Checklist to Assess the Risk of AI-Induced Skill Erosion in Professional Knowledge Work · Final Review · Score 30/30Co-Designing a Checklist to Assess the Risk of AI-Induced Skill Erosion in Professional Knowledge Work · Final Review · Score 30/30Co-Designing a Checklist to Assess the Risk of AI-Induced Skill Erosion in Professional Knowledge Work · Final Review · Score 30/30
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Sharp, underexplored framing (AI-induced deskilling) with a clear central empirical finding — individual habits are easy, organizational items hit structural friction — that is consistently supported across phases.
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Robust multi-cohort design (11 peers, 10 expert co-designers, 25 end-user testers spanning healthcare, law, education, HR, software, marketing) with all three versions reproduced.
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The dual-axis Scoring Guide (Individual Autonomy + Organizational Environment archetypes) and the Skill-Safe Framework Matrix give the tool a concrete diagnostic output that overlays real participant data (P1–P8).
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Extensive, well-documented stakeholder feedback (ES1–ES10) with direct quotes and explicit design implications for each transition — exemplary traceability.
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The Visual Changelog table and the explicit move from binary to frequency scales (to defeat "compliance theatre") are well-motivated and clearly tied to feedback.
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The Phase-2 "friction mapping" (green/red dots) is the quantitative backbone but is never tabulated; only illustrative items (1.1, 2.3, 3.1) are discussed, so the polarization claim is asserted rather than shown in full.
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The final V3.0 appendix is heavily corrupted by OCR/formatting (duplicated and garbled question fragments, repeated headers), seriously hurting readability of the deliverable's core artifact.
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Minor count inconsistencies (expert panel "N=10" but the text describes 4+4 guiding the transitions; end-user split 7/10/8) that should be reconciled.
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The scoring thresholds (archetype point bands, the −5 to +5 organizational scale) are presented as fixed without justification or calibration evidence.
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The instrument reframes the assigned delegation question into deskilling-risk assessment; the link back to "should this task be delegated to AI" could be made explicit rather than assumed.