Self-annotation is the gold standard for collecting affective state labels in affective computing. Existing methods typically rely on full annotation, requiring users to continuously label affective states across entire sessions. While this process yields fine-grained data, it is time-consuming, cognitively demanding, and prone to fatigue and errors. To address these issues, we present PREFAB, a low-budget retrospective self-annotation method that targets affective inflection regions rather than full annotation. Grounded in the peak-end rule and ordinal representations of emotion, PREFAB employs a preference learning model to detect relative affective changes, directing annotators to label only selected segments while interpolating the remainder of the stimulus. We further introduce a preview mechanism that provides brief contextual cues to assist annotation. We evaluate PREFAB through a technical performance study and a 25-participant user study. Results show that PREFAB outperforms baselines in modeling affective inflections while mitigating workload (and conditionally mitigating temporal burden). Importantly, PREFAB improves annotator confidence without degrading annotation quality.

There is a global shortage of therapeutic, educational, and social support for neurodivergent children and youth, with substantial downstream consequences for individual well-being and societal costs in adulthood. While early and sustained support is known to be both humane and economically effective, existing service models struggle to scale. Therapeutically Assisted Role-Playing Games (TARPGs) have recently emerged as a promising, group-based intervention that combines guided play, narrative structure, and social skill rehearsal to support neurodivergent development. Case studies consistently report positive outcomes, including increased engagement, social connection, emotional regulation, and self-efficacy. However, current research remains dominated by small-scale and non-experimental case studies, and practical barriers persist for wider adoption, especially for carers without formal therapeutic training. Therapists are often not available, and carers are the next, and more numerous, line of support for neurodivergent children. This paper introduces AURA (AI-Assisted Understanding of Role-play), a novel system designed to support therapists and carers running TARPG sessions. AURA addresses a key practical challenge in TARPG facilitation: documenting complex, multi-player narrative sessions without disrupting live interaction. By analysing audio and/or video recordings of sessions, AURA generates structured post-session summaries, and key event timeline, enabling facilitators to focus on participants rather than note-taking. AURA aims to lower barriers to entry, extend TARPG capacity beyond scarce clinical settings, and support more scalable, evidence-informed interventions for neurodivergent children and youth.

Video games are one of the richest and most popular forms of human-computer interaction and, hence, their role is critical for our understanding of human behaviour and affect at a large scale. As artificial intelligence (AI) tools are gradually adopted by the game industry a series of ethical concerns arise. Such concerns, however, have so far not been extensively discussed in a video game context. Motivated by the lack of a comprehensive review on the ethics of AI as applied to games, we survey the current state of the art in this area and discuss ethical considerations of these systems from the holistic perspective of theaffective loop. Through the components of this loop, we study the ethical challenges that AI faces in video game development.Elicitationhighlights the ethical boundaries of artificially induced emotions;sensingshowcases the trade-off between privacy and safe gaming spaces; anddetection, as utilised during in-gameadaptation, poses challenges to transparency and ownership. This paper calls for an open dialogue and action for the games of today and the virtual spaces of the future. By setting an appropriate framework we aim to protect users and to guide developers towards safer and better experiences for their customers.

Games are designed to elicit strong emotions during game play, especially when players are competing against each other. Artificial Intelligence applied to predict a player’s emotions has mainly been tested on single-player experiences in low-stakes settings and short-term interactions. How do players experience and manifest affect in high-stakes competitions, and which modalities can capture this? This paper reports a first experiment in this line of research, using a competition of the video game Hearthstone where both competing players’ game play and facial expressions were recorded over the course of the entire match which could span up to 41 minutes. Using two experts’ annotations of tension using a continuous video affect annotation tool, we attempt to predict tension from the webcam footage of the players alone. Treating both the input and the tension output in a relative fashion, our best models reach 66.3% average accuracy (up to 79.2% at the best fold) in the challenging leave-one-participant out cross-validation task. This initial experiment shows a way forward for affect annotation in games “in the wild” in high-stakes, real-world competitive settings.

This article surveys the current state-of-the-art in affective computing (AC) principles, methods, and tools as applied to games. We review this emerging field, namely affective game computing, through the lens of the four core phases of the affective loop: game affect elicitation, game affect sensing, game affect detection, and game affect adaptation. In addition, we provide a taxonomy of terms, methods, and approaches used across the four phases of the affective game loop and situate the field within this taxonomy. We continue with a comprehensive review of available affect data collection methods with regard to gaming interfaces, sensors, annotation protocols, and available corpora. This article concludes with a discussion on the current limitations of affective game computing and our vision for the most promising future research directions in the field.

How can we model affect in a general fashion, across dissimilar tasks, and to which degree are such general representations of affect even possible? To address such questions and enable research towardsgeneralaffective computing, this paper introduces The Arousal video Game AnnotatIoN (AGAIN) dataset. AGAIN is a large-scale affective corpus that features over 1,100 in-game videos (with corresponding gameplay data) from nine different games, which are annotated for arousal from 124 participants in a first-person continuous fashion. Even though AGAIN is created for the purpose of investigating the generality of affective computing across dissimilar tasks, affect modelling can be studied within each of its 9 specific interactive games. To the best of our knowledge AGAIN is the largest—over 37 hours of annotated video and game logs—and most diverse publicly available affective dataset based on games as interactive affect elicitors.

A key challenge of affective computing research is discovering ways to reliably transfer affect models that are built in the laboratory to real world settings, namely in the wild. The existing gap between in vitro and in vivo affect applications is mainly caused by limitations related to affect sensing including intrusiveness, hardware malfunctions, availability of sensors, but also privacy and security. As a response to these limitations in this paper we are inspired by recent advances in machine learning and introduce the concept of privileged information for operating affect models in the wild. The presence of privileged information enables affect models to be trained across multiple modalities available in a lab setting and ignore modalities that are not available in the wild with no significant drop in their modeling performance. The proposed privileged information framework is tested in a game arousal corpus that contains physiological signals in the form of heart rate and electrodermal activity, game telemetry, and pixels of footage from two dissimilar games that are annotated with arousal traces. By training our arousal models using all modalities (in vitro) and using solely pixels for testing the models (in vivo), we reach levels of accuracy obtained from models that fuse all modalities both for training and testing. The findings of this paper make a decisive step towards realizing affect interaction in the wild.

Assessing the believability of agents, characters and simulated actors is a core challenge for human computer interaction. While numerous approaches are suggested in the literature, they are all limited to discrete and low-granularity representations of believable behavior. In this paper we view believability, for the first time, as a time-continuous phenomenon and we explore the suitability of two different affect annotation schemes for its assessment. In particular, we study the degree to which we can predict character believability in a continuous fashion through a two-player game study. The game features various opponent behaviors that are assessed for their believability by 89 participants that played the game and then annotated their recorded playthrough. Random forest models are then trained to predict believability based on ad-hoc designed in-game features. Results suggest that a discrete annotation method leads to a more robust assessment of the ground truth and subsequently better modelling performance. Our best models are able to predict a change in perceived believability with a 72.5% accuracy on average (up to 90% in the best cases) in a time-continuous manner.

To which degree can abstract gameplay metrics capture the player experience in a general fashion within a game genre? In this comprehensive study we address this question across three different videogame genres: racing, shooter, and platformer games. Using high-level gameplay features that feed preference learning models we are able to predict arousal accurately across different games of the same genre in a large-scale dataset of over 1, 000 arousal-annotated play sessions. Our genre models predict changes in arousal with up to 74% accuracy on average across all genres and 86% in the best cases. We also examine the feature importance during the modelling process and find that time-related features largely contribute to the performance of both game and genre models. The prominence of these game-agnostic features show the importance of the temporal dynamics of the play experience in modelling, but also highlight some of the challenges for the future of general affect modelling in games and beyond.

Is it possible to predict moment-to-moment gameplay engagement based solely on game telemetry? Can we reveal engaging moments of gameplay by observing the way the viewers of the game behave? To address these questions in this paper, we reframe the way gameplay engagement is defined and we view it, instead, through the eyes of a game’s live audience. We build prediction models for viewers’ engagement based on data collected from the popular battle royale game PlayerUnknown’s Battlegrounds as obtained from the Twitch streaming service. In particular, we collect viewers’ chat logs and in-game telemetry data from several hundred matches of five popular streamers (containing over 100,000 game events) and machine learn the mapping between gameplay and viewer chat frequency during play, using small neural network architectures. Our key findings showcase that engagement models trained solely on 40 gameplay features can reach accuracies of up to 80% on average and 84% at best. Our models are scalable and generalisable as they perform equally well within- and across-streamers, as well as across streamer play styles.

There is growing evidence suggesting that subjective values such as emotions are intrinsically relative and that an ordinal approach is beneficial to their annotation and analysis. Ordinal data processing yields more reliable, valid and general predictive models, and preference learning algorithms have shown a strong advantage in deriving computational models from such data. To enable the extensive use of ordinal data processing and preference learning, this paper introduces the Python Preference Learning Toolbox. The toolbox is open source, features popular preference learning algorithms and methods, and is designed to be accessible to a wide audience of researchers and practitioners. The toolbox is evaluated with regards to both the accuracy of its predictive models across two affective datasets and its usability via a user study. Our key findings suggest that the implemented algorithms yield accurate models of affect while its graphical user interface is suitable for both novice and experienced users.

How could we gather affect annotations in a rapid, unobtrusive, and accessible fashion? How could we still make sure that these annotations are reliable enough for data-hungry affect modelling methods? This paper addresses these questions by introducing PAGAN, an accessible, general-purpose, online platform for crowdsourcing affect labels in videos. The design of PAGAN overcomes the accessibility limitations of existing annotation tools, which often require advanced technical skills or even the on-site involvement of the researcher. Such limitations often yield affective corpora that are restricted in size, scope and use, as the applicability of modern data-demanding machine learning methods is rather limited. The description of PAGAN is accompanied by an exploratory study which compares the reliability of three continuous annotation tools currently supported by the platform. Our key results reveal higher inter-rater agreement when annotation traces are processed in a relative manner and collected via unbounded labelling.

Is it possible to predict the motivation of players just by observing their gameplay data? Even if so, how should we measure motivation in the first place? To address the above questions, on the one end, we collect a large dataset of gameplay data from players of the popular game Tom Clancy's The Division. On the other end, we ask them to report their levels of competence, autonomy, relatedness and presence using the Ubisoft Perceived Experience Questionnaire. After processing the survey responses in an ordinal fashion we employ preference learning methods based on support vector machines to infer the mapping between gameplay and the reported four motivation factors. Our key findings suggest that gameplay features are strong predictors of player motivation as the best obtained models reach accuracies of near certainty, from 92% up to 94% on unseen players.

Procedural Content Generation of game content has been vastly improved over the last years and is more and more adopted also in the game industry. It relies mostly on evolutionary and related optimization methods but usually only treats a single of the many available facets as visuals, levels, audio, etc. The problem of how to combine several facets of generation is largely unsolved, but nevertheless very important. One of its subproblems is that we currently do not know in advance how users will react to machine-generated combinations. Based on a simple maze game with exchangeable visuals and audio styles we test how users receive ‘usual’ and ‘unusual’ facet compositions by means of rank trace based annotations of their own play-throughs. By means of machine learning techniques, we establish a model in order to learn and predict user reactions. Understanding the effects of facet composition on the user is fundamental if we want to rise evolutionary generation of content to the next level.