mlsanityData to 7 Oct 2026

How AI Impacts Skill Formation

arXiv 2601.20245 ↗First posted 28 Jan 2026cs.CY cs.AI cs.HCcited by 20

Not on the map. Why?

Abstract (arXiv)

AI assistance produces significant productivity gains across professional domains, particularly for novice workers. Yet how this assistance affects the development of skills required to effectively supervise AI remains unclear. Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition in the process. We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of AI. We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average. Participants who fully delegated coding tasks showed some productivity improvements, but at the cost of learning the library. We identify six distinct AI interaction patterns, three of which involve cognitive engagement and preserve learning outcomes even when participants receive AI assistance. Our findings suggest that AI-enhanced productivity is not a shortcut to competence and AI assistance should be carefully adopted into workflows to preserve skill formation -- particularly in safety-critical domains.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 14 of its 44 references that are in the map. First posted on arXiv 28 Jan 2026.

Builds on

14 of 44 references.

References · 14

  1. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 264
  2. 2022Measuring Progress on Scalable Oversight for Large Language Modelscited by 212
  3. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  4. 2024On scalable oversight with weak LLMs judging strong LLMscited by 94
  5. 2025Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivitycited by 93
  6. 2025Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversationscited by 88
  7. 2024Clio: Privacy-Preserving Insights into Real-World AI Usecited by 66
  8. 2024AI Hallucinations: A Misnomer Worth Clarifyingcited by 34
  9. 2024Rocks Coding, Not Development--A Human-Centric, Experimental Evaluation of LLM-Supported SE Taskscited by 13
  10. 2023Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomescited by 9
  11. 2023Dissenting Explanations: Leveraging Disagreement to Reduce Model Overreliancecited by 5
  12. 2025Societal Impacts Research Requires Benchmarks for Creative Composition Taskscited by 3
2 more
  1. 2020Experiential Learning Approach for Software Engineering Courses at Higher Education Levelcited by 1
  2. 2024Code Interviews: Design and Evaluation of a More Authentic Assessment for Introductory Programming Assignmentscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 20.

20 papers.

Sort:

By area:

  1. 2026Dive into Claude Code: The Design Space of Today's and Future AI Agent Systemscited by 33
  2. 2026AI Assistance Reduces Persistence and Hurts Independent Performancecited by 7
  3. 2026The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloadingcited by 3
  4. 2026Human-AI Productivity Paradoxes: Modeling the Interplay of Skill, Effort, and AI Assistancecited by 2
  5. 2026The Impact of AI Usage and Informativeness on Skill Development in Logical Reasoningcited by 1
  6. 2026Offloading Score: Measuring AI Reliance Through Counterfactual Workflowscited by 1
  7. 2026Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecastingcited by 1
  8. 2026(Im)Paired Programming: Coding Agents Improve Productivity but Harm Understandingcited by 1
  9. 2026Can AI Lower the Barrier to Cybersecurity? A Human-Centered Mixed-Methods Study of Novice CTF Learningcited by 0
  10. 2026Intellectual Stewardship: Re-adapting Human Minds for Creative Knowledge Work in the Age of AIcited by 0
  11. 2026Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native Softwarecited by 0
  12. 2026Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Developmentcited by 0
8 more
  1. 2026Psychological Competence as a Missing Dimension in AI Evaluationcited by 0
  2. 2026Epistemic Transfer in AI-Assisted Verification: A Framework and Evaluation Protocolcited by 0
  3. 2026Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systemscited by 0
  4. 2026Process-Constituted Intelligence: A Shared Criterion for Humans and Machinescited by 0
  5. 2026Assessment of Non-Institutional AI Tool Usage Among Clinicianscited by 0
  6. 2026Designing Against Deskilling: Metacognitive Feedback Reduces Cognitive Offloading to LLM Assistantscited by 0
  7. 2026Comprehension Audits to Mitigate Risks from Automated AI Researchcited by 0
  8. 2026Code Understanding is a Bottleneck for Coding Agentscited by 0