Traditionally, game designers have driven the development process of psychotherapeutic games with psychotherapists playing a consultant role. In contrast, our study explores psychotherapists independently designing digitally gamified psychotherapy. Our workshop participants consisted of six psychotherapists who created gamified digital therapies with the design prompt of anxiety disorder. We analyze the resulting six prototypes from a game design and psychotherapy perspective. We present insights into strategies for digitally gamifying therapy and identify challenges and opportunities for the future of gamified psychotherapy, grounded in the experiences of psychotherapists designing such therapies. This study also reflects on the use of user-friendly development tools for independently curating gamified digital therapies, especially by non-technical users such as psychotherapists.

Optimizing operations on critical infrastructure networks is key to reducing the impact of disruptive events. In this paper, we explore the potential of having humans and algorithms work together to address this difficult task. For this purpose, we use a gamified experiment to build and assess this potential in the context of the debris collection problem (i.e., “gamesploring”). We developed a digital game where players can request the help of the computer while facing a multi-objective problem of assigning contractors to road segments for clearing debris in a disaster area. Through a within-subjects experimental study, we assessed how players optimized under various circumstances (e.g., initial solution vs. from scratch) compared to the computer on its own. The results are both surprising as well as insightful: they suggest that human-machine collaboration is indeed beneficial but also that more work is needed on how to appropriately guide this form of collaboration.

Many studies have already shown that games can be a useful tool to make boring or difficult tasks more engaging. However, with serious game design being a relatively nascent field, such experiences can still be hard to learn and not very motivating. In this paper, we explore the use of learning and motivation frameworks to improve player experience in the well-known citizen science game Foldit. Using Cognitive Load Theory (CLT) and Self Determination Theory (SDT), we developed six interface and mechanical changes to the tutorial levels in Foldit designed to increase engagement and retention. We tested these features with new players of Foldit and collected both behavioral data, using game metrics, and prior experience data, using self-report measures. This study offers three major contributions: (1) we document the process of operationalizing CLT and SDT as new game features, a unique methodology not used in game design previously; (2) the user interface, specifically the level selection screen, significantly impacts how players progress through the game; and (3) a player's expertise, whether from prior domain knowledge or prior gaming experience, increases their engagement. We discuss both implications of these findings as well as how these implementations can generalize to other designs.