Generative arts have been an active area of research for decades, yet it is the recent developments in machine learning that have made it into an exploding field of opportunities, controversies and tensions challenging our ideas about art, the role of the artist and human creativity in general. These artistic developments and debates, however, are not new and have been predated by centuries of art movements in which the concepts of automatism and control originate. This article provides a survey of the past and contemporary visual artists whose practices have been connected through the concept of automatism, before and after the appearance of the computer and datasets. It is intended for practitioners who already work in this area and want to get a deeper understanding of this rapidly growing field, its historic roots and connections to art movements of the past, as well as for those who are new to this field to potentially serve as a starting point for this innovative line of research. It sets the stage for the authors’ own art practice-based research into the potential of Artificial Intelligence (AI) to be integrated into their own artistic practices, specifically those of making comics and visual narratives.

Much research in human–artificial intelligence (AI) interaction treats cooperation as a design problem of alignment, asking how system behavior can be coordinated with human intentions, task goals or expectations in order to support effective collaborative action. In this debate contribution, we shift the analytic focus from alignment to sequential organisation and ask how cooperation is interactionally accomplished when one source of action does not demonstrably orient to a shared sense of reality, yet its outputs are nevertheless incorporated into an unfolding sequence of action. Drawing on a sequential comic-making practice involving a generative AI system, we analyse three examples to examine how creative work can progress through misrecognition, breakdown and material effects, not necessarily requiring consensus or control. We argue that such misalignment is not a failure to be repaired but can function as a recurring interactional condition in human–AI cooperation, sustained through the sequential uptake, reinterpretation and transformation of AI-generated outputs. Based on this analysis, we suggest that human–AI interaction and design research would benefit from more explicitly interactional accounts of cooperation that treat misalignment as a productive condition rather than something to be minimised. We position artistic creative practice as a methodologically revealing site for examining how agency, authorship and coordination are practically accomplished in cooperative work with AI and we discuss implications for human-centered design.

How can generative AI (GenAI) be integrated in educational practices? This paper describes an approach combining problem-based learning with the creation of Interactive Digital Narratives (IDNs) using GenAI, applied during a training school for which the concrete task was to represent Malta’s complex Neolithic history. After introducing the topic, the concept of IDN, and GenAI tools, the educational journey continued by interweaving contextual information such as site visits with iterative design steps facilitating trainees to create their own IDN projects. This approach was applied during a five-day course, resulting in four IDNs incorporating the specific perspectives of the respective teams. The paper describes the pedagogical approach as well as the different ways the trainee teams found to address the limitations and challenges of current GenAI tools.

Believability is a key concept in NPC design, yet assessing it remains a methodological challenge. Existing approaches often rely on qualitative and subjective data, which can be influenced by interpretation and researcher bias. This issue arose in the case study connected to this paper examining the believability of characters in the Dragon Age games. To address this, the study combined qualitative insights with quantifiable results, using comparative tools to enhance reliability. This mixed-method approach led to the development of the Digital Character Believability Comparator (DCBC). DCBC allowed the case study to present clear and coherent results with few participants that provided a strong basis for discussion.

This paper investigates what we can learn about a game’s design by analyzing retellings applying Eladhari’s framework [4]. Mystics Ablaze, a fighting game, is evaluated in its capacity of instigating retellings using a novel method including qualitative interviews for data collection and deductive thematic analysis. The results of the analysis show that the participants were engaged with the narrative layer of the game, both through retellings and other kinds of interpretations. The novel methodology is promising in its ability to evaluate retellings.

This study explores how the integration of Large Language Models (LLMs) in story-driven video games may impact the social interactions and storytelling with non-playing characters (NPCs). We conducted a case study where participants experienced interacting with LLM-controlled NPCs in the roleplaying game Skyrim. Using the community based game modification Mantella, an alternative to the traditional scripted dialogue trees used for NPC that is substituted with LLM-powered conversations. The case study was conducted in three phases; first with a structured interview, second by an experiment where the participants interacted with LLM-powered NPCs, and finally by an in-depth semi-structured interview concerning the participants’ experiences. The data was thematically analyzed. Our findings indicate that LLM-powered NPCs enhance immersion and agency, as well as offering a sense of recurring novelty due to the unpredictable nature of LLMs. However, implementing a technology which allows for unrestricted dialogue and storytelling in a game not designed for it, poses challenges. For example, nascent technological issues with the integration of the LLM in the game mechanics may have a negative impact on players’ in terms of immersion and the believability of the NPCs. In addition, we examine the potential for LLM-powered NPCs to enrich game worlds in alignment with an appropriate game design. Based on the results, we suggest the suitable approaches for the usage of LLM-powered NPCs in future game development in regards to story-driven games and other applications where narration is key.

This paper presents a further exploration of a mixed initiative comic-making method that combines hand-drawn imagery with AI-generated visuals in a turn-taking, interactive manner. Initially developed within a personal artistic practice, the method was later tested in broader settings to understand how it might function as a public, co-creative method. As a first step, a pre-study was conducted at a public AI art exhibition, where casual participants were invited to engage with the method by producing short visual narratives.

Based on insights from this initial engagement, a structured workshop was carried out where participants created comics with the same method by alternating between analogue sketching and AI-assisted generation using a publicly available image generation tool appropriated for the task by the authors. Participants provided written reflections on their experience. The resulting comics and responses were collected and clustered to explore emerging patterns in narrative form and participant perception. This paper documents both phases – the pre-study and the workshop – and outlines the steps of the method and presents the comics created with it. Drawing from participant reflections, the paper highlights themes that arose in relation to the process. In particular, we examine how the authors’ method operates outside their studios in situated contexts and in relation to casual creative practices.

Where do generative AI (GenAI) tools stand in 2025 when it comes to the design of Interactive Digital Narratives (IDNs)? In 2024, we introduced a first effort in benchmarking GenAI tools for IDN design. This paper updates the findings for the current generation of previously evaluated tools (ChatGPT, Claude, Gemini, Copilot), and also adds several open source entries such as DeepSeek, Llama, Mistral, and Qwen. We introduce two key additions to the GenAI4IDN benchmarking framework: methods to benchmark iterative prompting and the effects of contextualized prompting. Furthermore, we introduce and apply a method for AI-assisted evaluation, facilitating rapid evaluation of large amounts of IDN design artifacts with a level of quality similar to human raters.

Extended reality (XR) enables both new opportunities but also introduces new barriers for inclusion in society. Furthermore, XR is less researched than web, desktop and mobile applications. This position paper presents the concept of an XR platform for inclusion, with the purpose to make people on the autism spectrum and with other disabilities, more independent of help from others in everyday life situations. Based on previous research, our position is that, through current and future XR technologies combined with civic and artificial intelligence, it is possible to create individually personalised support for this purpose, grounded in practice to ensure validation.

Generative Artificial Intelligence (GenAI) holds much potential for artistic practice in game design, craft, and fine arts. At the time of writing, 2024, the advancements of GenAI are rapid in regards to Large Language Models (LLMs) and Text to Image (TtI) systems. Artists and game designers are keen to explore new the tools, systems, and their updates as they are released, and the pace is quick. The members of the work group have multiple roles as scientists, game designers, and art- and craft practitioners, and converged around exploring GenAI for arts, crafts, and game development.