A Potential Teammate?: Understanding How Indie Game Developers Approach Generative AI’s Involvement in Their Small-Scale Creative Teamwork

Paper Title

A Potential Teammate?: Understanding How Indie Game Developers Approach Generative AI’s Involvement in Their Small-Scale Creative Teamwork

Publication Info

• Topic area: Human-AI collaboration in creative teamwork, focusing on indie game development.
• Keywords: Human-AI teaming, generative AI, indie game development, creative collaboration, independence, interdependence, teamwork dynamics, speculative design, HCI, CSCW.

Background and Problem

• Problem / challenge: Existing human-AI teaming frameworks are rooted in structured, task-oriented, and lab-based contexts, which do not address the socially nuanced, emotionally invested, and improvisational nature of real-world creative teamwork. Generative AI’s role in collaborative creative practices within teams remains underexplored.
• Significance: Understanding how generative AI can support small creative teams, like indie game developers, is critical for advancing human-AI collaboration and designing AI systems that align with the unique dynamics of creative teamwork.
• Motivation and related work: Prior research has focused on human-AI teaming in instrumental domains (e.g., military, medicine) or individual creative workflows (e.g., artists, writers). However, these studies overlook the collaborative and relational dimensions of small creative teams. Indie game development, characterized by small teams, limited resources, and artistic aspirations, provides a unique context to examine generative AI’s potential role in creative teamwork.

Solution

• Proposed approach: Investigate how indie game developers perceive generative AI as a teammate in terms of its independence and interdependence and envision its future roles in their creative teamwork.
• Novelty:
  1. Empirical investigation of generative AI’s role in socially nuanced, real-world creative teamwork.
  2. Identification of generative AI’s limitations in independence and interdependence within indie teams.
  3. Speculative insights into how generative AI could support indie teams as creative infrastructure, catalysts, and technical helpers.
  4. Proposal of new design directions for generative AI to better align with the dynamics of small creative teams.
• Procedure and key techniques:
  • Conducted 15 semi-structured interviews with indie game developers who have used generative AI in their collaborative workflows.
  • Analyzed data using reflexive thematic analysis, focusing on themes of independence and interdependence.
  • Explored participants’ speculative visions for AI’s future roles in their teamwork.

Results

• Concrete findings:
  • Generative AI lacks intrinsic artistic intent, adaptability to evolving project contexts, and the ability to engage in spontaneous, improvised creative exchanges (independence).
  • AI struggles to align with a team’s shared vision, evolving aesthetics, and creative language, and lacks emotional presence and relational engagement (interdependence).
• Advantage over baselines:
  • Highlights the unique challenges and opportunities of integrating AI into small, creativity-centric teams, extending beyond structured, task-oriented human-AI teaming paradigms.
• Experiments / evaluation:
  • Interviews with 15 indie developers using generative AI tools (e.g., ChatGPT, DALL-E 2) in their workflows.
  • Analysis focused on perceptions of AI’s collaborative potential and envisioned future roles.
• Limitations and future work:
  • Limited geographic diversity (predominantly U.S.-based participants).
  • Focused on small indie teams; larger indie studios may have different dynamics.
  • Did not include multiple members from the same team, limiting insights into collective team perspectives.
  • Future work should explore global and cross-domain perspectives and recruit multiple team members for richer insights.

Summary

This paper examines how indie game developers perceive generative AI’s role in their small, creativity-centric teams. Developers highlight AI’s current limitations in independence (e.g., lack of intrinsic creativity, adaptability) and interdependence (e.g., misalignment with team vision, lack of emotional presence). Despite these challenges, they envision AI as a supportive tool for coordination, brainstorming, and automating technical tasks. The study proposes shifting AI design from autonomous creative agents to embedded collaborative infrastructure and emphasizes the importance of cultivating AI literacy and team norms for effective integration. These findings inform the design of future AI systems that respect human authorship and enhance collaborative sense-making in small creative teams.