# Upskilling with Generative AI: Practices and Challenges for Freelance Knowledge Workers

arXiv: https://arxiv.org/abs/2604.27231 · HTML: https://mlsanity.com/paper/2604.27231/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 139 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 27 of 27)

- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models](https://mlsanity.com/paper/2102.02503/) (2021)
- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023)
- [How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries](https://mlsanity.com/paper/2310.06778/) (2023)
- [Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chat](https://mlsanity.com/paper/2401.17163/) (2024)
- [Ironies of Generative AI: Understanding and mitigating productivity loss in human-AI interactions](https://mlsanity.com/paper/2402.11364/) (2024)
- [Quantifying the Invisible Labor in Crowd Work](https://mlsanity.com/paper/2110.00169/) (2021)
- [Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation](https://mlsanity.com/paper/2502.18357/) (2025)
- [Governance of Generative AI in Creative Work: Consent, Credit, Compensation, and Beyond](https://mlsanity.com/paper/2501.11457/) (2025)
- [DBox: Scaffolding Algorithmic Programming Learning through Learner-LLM Co-Decomposition](https://mlsanity.com/paper/2502.19133/) (2025)
- [Generative AI in Knowledge Work: Design Implications for Data Navigation and Decision-Making](https://mlsanity.com/paper/2503.18419/) (2025)
- [The Impact of Generative AI Coding Assistants on Developers Who Are Visually Impaired](https://mlsanity.com/paper/2503.16491/) (2025)
- [Generative AI Uses and Risks for Knowledge Workers in a Science Organization](https://mlsanity.com/paper/2501.16577/) (2025)
- [Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators](https://mlsanity.com/paper/2502.09799/) (2025)
- [LearnMate: Enhancing Online Education with LLM-Powered Personalized Learning Plans and Support](https://mlsanity.com/paper/2503.13340/) (2025)
- [Beyond the Personal Assistant: How Expectations for Enterprise AI in Teamwork Diverged as Generative AI Took Shape, 2023-2025](https://mlsanity.com/paper/2509.10956/) (2025)
- [Rideshare Transparency: Translating Gig Worker Insights on AI Platform Design to Policy](https://mlsanity.com/paper/2406.10768/) (2024)
- [Knowledge Workers' Perspectives on AI Training for Responsible AI Use](https://mlsanity.com/paper/2503.06002/) (2025)
- [Crowd Coach: Peer Coaching for Crowd Workers' Skill Growth](https://mlsanity.com/paper/1811.05364/) (2018)
- [Cooperation in the Gig Economy: Insights from Upwork Freelancers](https://mlsanity.com/paper/2301.08808/) (2023)
- [Co-Designing Collaborative Generative AI Tools for Freelancers](https://mlsanity.com/paper/2602.05299/) (2026)
- [Navigating Multi-Stakeholder Incentives and Preferences: Co-Designing Alternatives for the Future of Gig Worker Well-Being](https://mlsanity.com/paper/2302.13436/) (2023)
- [Designing Sousveillance Tools for Gig Workers](https://mlsanity.com/paper/2403.09986/) (2024)
- [Gig2Gether: Data-sharing to Empower, Unify and Demystify Gig Work](https://mlsanity.com/paper/2502.04482/) (2025)
- [A Paradigm for Creative Ownership](https://mlsanity.com/paper/2505.15971/) (2025)
- [Occupational Diversity and Stratification in Platform Work: A Longitudinal Study of Online Freelancers](https://mlsanity.com/paper/2604.03517/) (2026)

## Abstract (arXiv)

Freelance workers must continually acquire new skills to remain competitive in online labor markets, yet they lack the organizational training, mentorship, and infrastructure available to traditional employees. Generative AI-powered tools like ChatGPT are reshaping market skill demands while also offering new forms of on-demand learning support to meet those demands. Despite growing interest in AI-powered learning tools, little is known about how freelancers actually use these tools to learn, the challenges they encounter, and how generative AI for learning interacts with precarity and competition in platform-based work. We present a mixed-methods study combining a survey and semi-structured interviews with freelance knowledge workers. Grounded in self-directed learning theory, we examine how freelancers integrate generative AI tools into their learning practices. Our findings show that freelancers increasingly rely on generative AI to structure learning and support exploratory skill acquisition, but do not treat it as their primary learning resource due to inconsistency, lack of contextual relevance, and verification overhead. We identify a shift from learning as growth to learning as survival, where upskilling is oriented toward immediate market viability rather than long-term development. We also surface a structural challenge we term invisible competencies, in which workers acquire skills through generative AI tools but lack credible ways to signal or validate these skills in competitive freelance markets. Based on these insights, we offer design recommendations for generative AI-powered learning tools for freelancers.