Generative AI in the Wild: Prospects, Challenges, and Strategies
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Abstract (arXiv)
Propelled by their remarkable capabilities to generate novel and engaging content, Generative Artificial Intelligence (GenAI) technologies are disrupting traditional workflows in many industries. While prior research has examined GenAI from a techno-centric perspective, there is still a lack of understanding about how users perceive and utilize GenAI in real-world scenarios. To bridge this gap, we conducted semi-structured interviews with (N=18) GenAI users in creative industries, investigating the human-GenAI co-creation process within a holistic LUA (Learning, Using and Assessing) framework. Our study uncovered an intriguingly complex landscape: Prospects-GenAI greatly fosters the co-creation between human expertise and GenAI capabilities, profoundly transforming creative workflows; Challenges-Meanwhile, users face substantial uncertainties and complexities arising from resource availability, tool usability, and regulatory compliance; Strategies-In response, users actively devise various strategies to overcome many of such challenges. Our study reveals key implications for the design of future GenAI tools.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Apr 2024.
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Citing papers per year: 2024: 1; 2025: 6; 2026: 2.
9 papers.
- 2025Generative AI Uses and Risks for Knowledge Workers in a Science Organizationcited by 5
- 2025Co-designing Large Language Model Tools for Project-Based Learning with K12 Educatorscited by 5
- 2025GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Designcited by 5
- 2025ACAI for SBOs: AI Co-creation for Advertising and Inspiration for Small Business Ownerscited by 2
- 2024Students' Perceived Roles, Opportunities, and Challenges of a Generative AI-powered Teachable Agent: A Case of Middle School Math Classcited by 1
- 2025Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learningcited by 1
- 2025Implicature in Interaction: Understanding Implicature Improves Alignment in Human-LLM Interactioncited by 0
- 2026Towards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarioscited by 0
- 2026An investigation of AI integration in sound designer workflows and experiencescited by 0
- 2026An investigation of AI integration in sound designer workflows and experiences26 May 2026
- 2026Towards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarios8 May 2026
- 2025Implicature in Interaction: Understanding Implicature Improves Alignment in Human-LLM Interaction29 Oct 2025
- 2025GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design21 Aug 2025
- 2025Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning3 Jun 2025
- 2025ACAI for SBOs: AI Co-creation for Advertising and Inspiration for Small Business Owners9 Mar 2025
- 2025Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators13 Feb 2025
- 2025Generative AI Uses and Risks for Knowledge Workers in a Science Organization27 Jan 2025
- 2024Students' Perceived Roles, Opportunities, and Challenges of a Generative AI-powered Teachable Agent: A Case of Middle School Math Class26 Aug 2024