This study examines the impact of Large Language Model-generated narratives in a climate-change-themed Visual Novel, comparing two versions: First, the story is generated using thematic keywords in the prompts, and second, the story is generated without keywords. Fifty participants (21 female, 29 male) completed the study. Results showed that participants in the group without thematic keywords had higher levels of narrative engageability score, as measured by the Narrative Engageability Scale, than those with thematic keywords. This indicated that the ability to engage with the story was stronger in the group without keywords. However, when assessing the narrative experience using the Game User Experience Satisfaction Scale, both groups reported similar levels of satisfaction, suggesting that while the ability to engage with the narrative differed between groups, the overall narrative experience was mainly the same. These findings suggested that thematic keywords in prompts significantly impacted participants’ narrative experience of the game.

This study investigates the emotional dynamics from different cultural backgrounds using a multimodal approach that combines Facial Emotion Recognition and Heart Rate Variability (HRV) analysis. A total of 109 participants from Italy, Japan, and Korea (mean age = 24.5 years) played two casual games, namely Snake and Matching Pairs, to investigate cultural differences in emotional and physiological responses. The results revealed distinct cultural patterns in static emotional expression using generalised linear mixed models (GLMMs). The Italian cultural group showed higher levels of positive facial expressions (FE), particularly happiness; the Korean cultural group showed more frequent negative FE, while the Japanese cultural group showed restrained FE, particularly related to fear. Moreover, emotional transitions were analysed using a Markov-inspired continuousstate operator derived from probabilistic FE vectors, which characterised the temporal structure of emotional changes and uncovered systematic cross-cultural and task-dependent differences in emotional latency. These findings show that emotional transitions are shaped by cultural norms and the cognitive demands of the games. Furthermore, integrating FE and HRV features into GLMMs showed that autonomic indices predict performance and vary across game types. Overall, this study provides three key contributions. First, it indicates that FEs of emotion during gameplay differ significantly across cultures, in accordance with cultural display norms. Second, it demonstrates that emotional transitions are dynamic and influenced by game performance, with cultural background shaping these patterns. Third, it identifies cross-cultural differences in physiological responses, specifically bodily signals such as HRV. These findings enhance understanding of how games elicit and regulate emotional and physiological responses, suggesting applications beyond entertainment.

This paper presents a multi-year evaluation (2022-2024) of winning entries from the DareFightingICE Sound Design Competition, analyzing the interplay between audio aesthetics and gameplay functionality for visually impaired players and the Blind AI agent. Through user studies ($\mathrm{n} {=} {2 6}$and$\mathrm{n} {=} {2 1}$) and a comprehensive ablation study of the 2024 winning sound design, we reveal three key findings: (1) While winning entries consistently achieved high aesthetic ratings, scores plateaued despite year-on-year improvements in Blind AI performance, indicating a decoupling of these dimensions; (2) Systematic muting identified “critical” sounds whose absence severely impaired AI decision-making, whereas static background music introduced detrimental noise; (3) Drastically reducing sound design to${1 0 \%}$of sound effects degraded both AI win ratios and human aesthetic perception. These results demonstrate that practical accessible audio requires prioritizing unambiguous cues for frequent actions, minimizing sonic redundancy like non-adaptive BGM, and validating sound designs through dual AI/human evaluation. We provide actionable strategies for designers targeting future competitions and inclusive gaming.

This paper introduces Sonic Doom, an accessibility focused enhancement of the ViZDoom First-person shooter (FPS) platform, integrating an advanced sound design and two aim-assist systems: Auto Aim (automated crosshair adjustment) and Sonic Aim (audio feedback for target proximity). To tackle the issue of FPS games remaining largely inaccessible to visually impaired players (VIPs) due to the game's reliance on visual cues for navigation and combat. We evaluate our approach through a dual-method framework: (1) AI agents trained to play blindly using only an audio input, and (2) human participants in both sighted and blindfolded conditions. Results demonstrate that enhanced sound design improves navigation efficiency (e.g., faster maze completion by the AI agent) and combat accuracy (e.g., higher enemy kill rates in human trials). While Auto Aim achieved superior objective performance, subjective evaluation revealed a strong user preference for Sonic Aim, emphasizing the need to balance assistance with player autonomy. Our AI-driven evaluation framework, the first of its kind in FPS accessibility research, provides scalable, objective metrics for assessing sound designs. This work advances accessible game design by empirically validating sonification techniques in combat-intensive scenarios and establishing a methodology for future research in multi-modal game accessibility.

Biofeedback in serious games is becoming increasingly relevant to objectively assessing players’ engagement and performance. This study administered a Matching Pairs (MP) game to a group of healthy volunteers while acquiring eye-tracking data, specifically pupil dilation and blinking behavior. A dedicated algorithm has been implemented for game score assessment. A set of linear and nonlinear features were extracted from physiological signals. Statistical analysis was performed to understand whether oculometric parameters differ between the best and worst MP game trials. Moreover, correlation analysis investigated possible relationships between physiological measures and players’ performance. Results showed statistically significant smaller pupil dilation velocity, higher Index of Pupillary Activity (IPA), and shorter blink rate in the best MP trial than in the worst one. Our outcomes could highlight better cognitive resource management and greater focus in the best trial. Moreover, participants’ scores were negatively correlated with blinking rate and the time the eyes were closed during the game. It showed that more focus on specific game tasks leads to better performance, therefore limiting interruptions of the information flow due to blinking. These findings may suggest that eye parameters in serious gaming platforms could be a powerful tool for intervention programs targeting older populations or people with cognitive impairments.

In this paper, we present a Tabletop Role-Playing game (TTRPG) driven by ChatGPT. Prompts are employed to instruct ChatGPT to act as Game Masters (GMs). In crafting each prompt to integrate a distinctive role, three roles denoted as Role 1, Role 2, and Role 3, are established. Subsequently, we perform pre-game and post-game emotional assessments employing the Positive and Negative Affect Schedule (PANAS) questionnaire to scrutinize players’ emotional dynamics throughout the gaming experience. Upon analyzing the collected data, we observe that Role 1 and Role 2 affect players’ positive emotions. Notably, Role 2 exhibits the most pronounced influence on players’ positive emotions. Our findings demonstrate that a TTRPG GM powered by ChatGPT can significantly enhance players’ positive emotions. This leads us to recognize that TTRPG GM powered by ChatGPT plays a positive role in enhancing the mental well-being of specific populations.