This article presents the development of an intrinsically integrated educational video game designed to train medical students and residents on the importance of formative feedback and how to deliver it effectively. Through this design case study, we describe the iterative methodology followed and its critical role in achieving a high-quality outcome.
The emergency management community faces challenges in preparing for and responding to high-impact, weather-related disasters. Traditional training methods, such as tabletop exercises and full-scale drills, often fall short in replicating the complexity, uncertainty, and time-sensitive decision making required in real disaster situations. This paper highlights the need for a novel multiplayer, serious computer game to help train emergency management professionals. Informed by interviews with thirteen emergency management professionals in the United States, we propose a framework for a serious disaster game. The framework for the game has players acting in different emergency management roles working together to respond to an extreme weather event.
This demo showcases a lightweight conversation template designed to help students prototype short, character-driven interactions within Answer Campus, an ongoing academic game series. By writing inside an established game world using a prebuilt Unity framework, students can sidestep common early blockers in game development: scope creep, technical uncertainty, blank-page paralysis, etc. and instead focus on tone, branching structure, and relational outcomes. This demo was piloted as a two-week narrative design sprint where students created original conversations for existing characters, drawing on a shared character bible and game assets. Post-mortems revealed that the template supported both technical and narrative growth, allowing students to explore authorship, collaboration, and emotional design.
Measuring cognitive gains in a multi-skill training game is challenging, especially with dynamic difficulty levels that must be factored into metric design. This exploratory study aims to investigate whether Item Response Theory (IRT) can be used to model the mission difficulty and scale the performance score of participants' attentional abilities to reflect their training gains in an attentional training game, Skylar's Run. Properties of EEG-based attention performance were investigated. We modeled the participants' focused and sustained attention scores during gameplay that are physiologically measured every 1/10th of a second. Using IRT, we calculated difficulty weights for 15 missions and scaled the performance scores accordingly to reflect the inherent difficulty across the missions. We also accounted for EEG data variability. Lastly, we validated the scaled performance scores by fitting regression models and found that training duration had a marginally significant positive association with the performance score of sustained attention but not focused attention. These results provide evidence of the viability of using IRT to consider variability in physiological measures of attention as well as in game difficulty in complex cognitive training games.
We investigated the effect of three virtual reality design features on users' emotional responses, including two visual features (object luminosity and background lighting) and an interaction feature (available actions). Our findings revealed the affective quality of these three design features, showing that object luminosity and actions significantly increase positive emotion, while dark background lighting induces slightly negative emotions. Object luminosity and action also had an effect on perceived presence, whereas background lighting did not. Only action increased the level of perceived cognitive load. We discuss the implications of our findings for the emotional design of VR games for learning.
