Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2302.12601

"An Adapt-or-Die Type of Situation": Perception, Adoption, and Use of Text-To-Image-Generation AI by Game Industry Professionals

Veera Vimpari·Annakaisa Kultima·Perttu Hämäläinen·Christian Guckelsberger
View original source ↗Read source document ↗Export metadata ↓

Abstract

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.

Explore related subjects

cs.HCcs.AI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Veera Vimpari, Annakaisa Kultima, Perttu Hämäläinen, Christian Guckelsberger. 2023-09-05. "An Adapt-or-Die Type of Situation": Perception, Adoption, and Use of Text-To-Image-Generation AI by Game Industry Professionals. https://doi.org/10.1145/3611025

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials

Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. Using a real-world dataset, we demonstrated the effectiveness of TrialCompass in providing insights into designing eligibility criteria for septic shock and sepsis-associated acute kidney injury. We also discussed the research prospects of applying visual analytics to clinical trials.

cs.HC↗
arXiv2026

PRIMMDebug: Teaching Secondary School Students a Reflective Approach to Debugging

Debugging is a challenging and infuriating experience for many secondary school students learning their first text-based programming language. One frequent problem is the lack of reflection in students' debugging strategies, which makes error resolution unlikely and teacher reliance common. Tools that encourage more reflective and teacher-independent debugging may foster more success with fixing errors, but are lacking. This paper presents PRIMMDebug, an approach for teaching the debugging process to secondary school students. PRIMMDebug consists of an online tool that takes students through the steps of a pedagogical process based on PRIMM, a framework for teaching programming. The tool encourages written articulation throughout the debugging process and limits students' ability to run and edit code at certain stages. A classroom study with PRIMMDebug found a general reluctance among students to engage with the reflection it promotes, despite teachers appreciating this emphasis on reflection. We end by suggesting three considerations for future pedagogical debugging research and tooling: balance structure and flexibility, teach shorter debugging heuristics, and use tooling early on in students' programming journey.

cs.HC↗
arXiv2026

Mirror Skin: In Situ Visualization of Robot Touch Intent on Robotic Skin

Effective communication of robot touch intent is essential for safe and predictable physical human-robot interaction. While intent communication has been widely studied, existing approaches lack the spatial specificity and semantic depth necessary to efficiently convey robot touch intent. We present Mirror Skin, a cephalopod inspired concept that mirrors in-situ visual representations of a human's body parts onto the corresponding robot's touch region to communicate who shall initiate touch, where it will occur, and when it is imminent. We informed the design of Mirror Skin through a structured design exploration with experts and demonstrate the real-world feasibility of Mirror Skin with a proof-of-concept prototype. User studies in VR and with the physical prototype showed that Mirror Skin significantly improves accuracy and response times for interpreting touch intent and improves the user experience during physical human-robot interactions.

cs.HC↗