"An Adapt-or-Die Type of Situation": Perception, Adoption, and Use of Text-To-Image-Generation AI by Game Industry Professionals
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Abstract (arXiv)
Text-to-image generation (TTIG) models, a recent addition to creative AI, can generate images based on a text description. These models have begun to rival the work of professional creatives, and sparked discussions on the future of creative work, loss of jobs, and copyright issues, amongst other important implications. To support the sustainable adoption of TTIG, we must provide rich, reliable and transparent insights into how professionals perceive, adopt and use TTIG. Crucially though, the public debate is shallow, narrow and lacking transparency, while academic work has focused on studying the use of TTIG in a general artist population, but not on the perceptions and attitudes of professionals in a specific industry. In this paper, we contribute a qualitative, exploratory interview study on TTIG in the Finnish videogame industry. Through a Template Analysis on semi-structured interviews with 14 game professionals, we reveal 12 overarching themes, structured into 49 sub-themes on professionals' perception, adoption and use of TTIG systems in games industry practice. Experiencing (yet another) change of roles and creative processes, our participants' reflections can inform discussions within the industry, be used by policymakers to inform urgently needed legislation, and support researchers in games, HCI and AI to support the sustainable, professional use of TTIG to benefit people and games as cultural artefacts.
Key facts. Cited by 11 papers in the map (data through 13 Oct 2026). Builds on 12 of its 102 references that are in the map. First posted on arXiv 24 Feb 2023.
Builds on
12 of 102 references.
References · 12
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Built on by
Citing papers per year: 2023: 1; 2024: 6; 2025: 3; 2026: 1.
11 papers.
- 2024When combinations of humans and AI are useful: A systematic review and meta-analysiscited by 105
- 2024Large Language Models and Games: A Survey and Roadmapcited by 92
- 2024The Ink Splotch Effect: A Case Study on ChatGPT as a Co-Creative Game Designercited by 12
- 2024T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Conceptscited by 12
- 2023Towards Mode Balancing of Generative Models via Diversity Weightscited by 6
- 2025SketchFlex: Facilitating Spatial-Semantic Coherence in Text-to-Image Generation with Region-Based Sketchescited by 6
- 2025GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Designcited by 5
- 2025AI Rivalry as a Craft: How Resisting and Embracing Generative AI Reshape Writing Professionscited by 3
- 2024RoBus: A Multimodal Dataset for Controllable Road Networks and Building Layouts Generationcited by 2
- 2024Unlocking Fair Use in the Generative AI Supply Chain: A Systematized Literature Reviewcited by 0
- 2026No Pixel Left Behind: Filling Gaps in Anime Colorizationcited by 0
- 2026No Pixel Left Behind: Filling Gaps in Anime Colorization1 Sep 2026
- 2025GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design21 Aug 2025
- 2025AI Rivalry as a Craft: How Resisting and Embracing Generative AI Reshape Writing Professions12 Mar 2025
- 2025SketchFlex: Facilitating Spatial-Semantic Coherence in Text-to-Image Generation with Region-Based Sketches11 Feb 2025
- 2024T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts5 Dec 2024
- 2024Unlocking Fair Use in the Generative AI Supply Chain: A Systematized Literature Review1 Aug 2024
- 2024RoBus: A Multimodal Dataset for Controllable Road Networks and Building Layouts Generation10 Jul 2024
- 2024When combinations of humans and AI are useful: A systematic review and meta-analysis9 May 2024
- 2024The Ink Splotch Effect: A Case Study on ChatGPT as a Co-Creative Game Designer4 Mar 2024
- 2024Large Language Models and Games: A Survey and Roadmap28 Feb 2024
- 2023Towards Mode Balancing of Generative Models via Diversity Weights24 Apr 2023