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Generative AI in the Wild: Prospects, Challenges, and Strategies

Yuan Sun, Eunchae Jang, Fenglong Ma, Ting Wang

TL;DR

This study investigates how GenAI is perceived and used in real-world creative-industry contexts through semi-structured interviews with 18 practitioners, analyzed within a Learning-Using-Assessing (LUA) framework. It identifies three core themes—prospects, challenges, and strategies—that illuminate a dynamic, cyclical co-creation process between humans and GenAI across learning, using, and assessing activities. Key contributions include demonstrating GenAI's potential to accelerate ideation and transform workflows while exposing non-functional challenges such as controllability, feedback, and regulatory concerns, and proposing user-centered design implications to enhance agency, transparency, and responsible use. The findings offer practical guidance for tool design, learning resources, and governance to foster equitable adoption and reliable, trustworthy GenAI in the wild.

Abstract

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.

Generative AI in the Wild: Prospects, Challenges, and Strategies

TL;DR

This study investigates how GenAI is perceived and used in real-world creative-industry contexts through semi-structured interviews with 18 practitioners, analyzed within a Learning-Using-Assessing (LUA) framework. It identifies three core themes—prospects, challenges, and strategies—that illuminate a dynamic, cyclical co-creation process between humans and GenAI across learning, using, and assessing activities. Key contributions include demonstrating GenAI's potential to accelerate ideation and transform workflows while exposing non-functional challenges such as controllability, feedback, and regulatory concerns, and proposing user-centered design implications to enhance agency, transparency, and responsible use. The findings offer practical guidance for tool design, learning resources, and governance to foster equitable adoption and reliable, trustworthy GenAI in the wild.

Abstract

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.
Paper Structure (64 sections, 3 figures, 2 tables)

This paper contains 64 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Human-(Gen)AI Interaction
  4. GenAI in HCI Research
  5. GenAI "in the Wild"
  6. Method
  7. Recruitment and Interviews
  8. Data Analysis
  9. A Learning-Using-Assessing Framework
  10. Findings
  11. Learning
  12. Learning -- Prospects
  13. Abundant learning resources
  14. Learning -- Challenges
  15. Scarcity of valuable resources
  16. ...and 49 more sections

Figures (3)

  • Figure 1: Co-creation between human expertise and GenAI capabilities in complex, real-world settings.
  • Figure 2: P9's 'cheat-sheet' when using Deforum Stable Diffusion for animation creation.
  • Figure 3: Sample advertising art created by P9 using Stability.AI's Stable Diffusion, which represents the complex concept of 'a boat floating on the Suzhou River pier at the Bund in Shanghai, with the boat being shaped like a wave, carrying a cup of takeaway coffee.'