Visual guidance plays a crucial role in level design, while prior work has largely relied on qualitative observations. This study presents a novel quantitative framework for evaluating visual guidance during level transitions in 3D role-playing games. By integrating analyses of depth maps derived from raycast grids with high-resolution RGB image sequences from our primary dataset in Dark Souls III, we quantify metrics such as luminance dynamics, chromatic complexity, and spatial depth distribution. This bimodal analysis separates geometric factors (from depth) and perceptual factors (from color), thereby clarifying how specific visual cues consistent with design principles such as spatial funneling and chromatic contrast-influence player navigation and immersion. Our initial empirical findings, derived from strictly quantitative and numerical analyses, suggest that the synergy between geometric constraints and perceptual cues provides an effective framework for both validating design principles and identifying navigation pitfalls in level transitions. These results not only provide a formal, data-driven understanding of level design but also offer actionable insights for creating more intuitive virtual environments and establishing evaluation criteria for future procedural level generation.
The use of personality to predict user preferences or select a job candidate are practical examples of how personality can be used in a variety of fields. But to assess someone's personality is not easy. The most commonly used techniques are personality questionnaires, but they are subject to social desirability bias and false responses, limiting the accuracy of personality assessment. Implicit techniques have also been used, but they require great amounts of user interactions with the system, and most of them only focus on broader personality dimensions. To overcome those limitations, we developed a short-duration mobile serious game, Coin Catcher, as a concept proof to implicitly measure the granular personality trait morality in less than 5 min. The game uses concepts related to normative morality, proposing dilemmas that the player must solve in order to progress in the game. Experiments with real users were conducted ($n=96$), showing morality can be implicitly determined by a simple game in a short period of time, without the bias associated with personality questionnaires. Additionally, correlations with other personality traits, such as altruism, cooperation, modesty, sympathy, and anger were also found, showing the game has the potential to measure several granular personality traits that better characterize a person.
This paper examines the impact of AI-generated versus human-written dialogue in a murder-mystery game on player experience. We manipulated both the game dialogue type (AI-generated vs. human-written dialogue) and the game type description (AI-labelled vs human-written-labelled) to assess effects on overall player experience (PX), immersion, and perceived autonomy. Results show that player expectations, modulated by the game type description, had a greater impact on overall PX than the actual game type. Players who thought that they played a game with a human-written narrative reported more positive overall PX, higher immersion and perceived autonomy regardless of which version they actually played. However, interacting with the AI-generated dialogue version led to higher perceived autonomy, suggesting that AI-driven dialogue can enhance perceived freedom without necessarily improving the overall PX. These findings show that player expectations, in response to game descriptions, play a central role in the experiences of players. This work contributes to understanding the influence of narratives and the extent to which AI features increase player engagement.
This paper presents a gamified Virtual Reality (VR) application integrated with a second-screen Web interface to support exposure therapy for children with sensory food aversion. The system allows therapists to configure therapeutic sessions, monitor user actions in real time, and adapt the virtual environment according to the patient's responses. The Web interface records session data, including behavioral performance and, when available, biosignals such as EEG and ECG, building a historical profile for each patient. The solution was developed through a co-design process with therapists to ensure usability and alignment with therapeutic practice. Preliminary results show the system's potential to improve engagement, support therapist intervention, and enable future developments, such as the integration of real-time neurofeedback to adapt the experience based on physiological data.
Modern racing games often take place on race tracks with huge surroundings, including forests, fields, cities, or mountains. While not being important to the racing itself, the surroundings must be present, dense, and detailed to enhance the gameplay feel, and must be performance-optimized for a smooth gaming experience. Creating these surroundings manually is a very laborious task, even for a skilled level designer. Also, manual design makes it prohibitively expensive to modify the track layout later. This makes the surroundings an ideal candidate for procedural content generation, which is nowadays extensively used in many areas of video game level design. In this paper, we present a method for controllable track surroundings generation. It generates a one square kilometer map in a matter of minutes, orders of magnitude faster than the manual process. It features several different biomes, various types of assets and vegetation, and can be further extended using custom assets. The generated race tracks and their surroundings are easily adjustable, making the level design iterative and easier to perfect.
