Cho, Kim & Kim (2026) — Accepted at ICML 2026 (position paper).
This ICML 2026 position paper argues that adopting AI in organizational practice does not automatically yield productivity gains — human and environmental factors critically moderate the relationship. Drawing on the partial equilibrium model of Gries and Naudé (2022), it identifies five key moderators that can attenuate or negate productivity benefits.
Five Moderating Factors
1. Human resource composition — the mix of skills, roles, and experience in the workforce 2. Baseline capability of individuals — pre-existing competence before AI introduction 3. Learning curve of practitioners — how quickly users adapt to AI tools 4. Incentives for fair use — motivations for appropriate and ethical AI usage 5. Flexibility of objectives — organizational ability to adjust goals with AI integrationRelevance to AI in Education
While framed broadly, the implications for teachers and educational institutions are direct. The learning curve factor is particularly salient: if practitioners cannot adapt quickly, AI tools may reduce rather than increase productivity — a finding that echoes the cognitive offloading speedup illusion where users overestimate AI's time-saving benefits.The incentives factor connects to concerns in academic-integrity — if students and teachers lack incentives for fair AI use, productivity gains may be captured by surface-level efficiency while deeper learning suffers. The framework complements GenAI assessment governance by highlighting that technical capability alone is insufficient without attention to the human systems within which AI is deployed.
For higher-ed institutions investing in AI infrastructure, this paper serves as a cautionary reminder that deployment without attention to training, incentives, and organizational readiness may fall short of promised gains — a finding consistent with research on AI fatigue among students.
Related Pages
- ai-assisted-writing-research-teams — 6 of 8 papers in May 28 scan
- persistent-ai-agents-academic-research — empirical evidence that AI productivity requires active human involvement
- cognitive-offloading-speedup-illusion — Speedup illusion and miscalibration
- teacher-role — Teacher roles with AI
- academic-integrity — Academic integrity and AI
- genai-assessment-governance — GenAI assessment governance
- ai-fatigue-academic-contexts — AI fatigue dimensions
- generative-ai — Generative AI in education