Women are largely underrepresented in IT, girls’ interest in STEM and IT fields tends to drop throughout secondary education. Educational games are a great tool to change the perception of certain topics, as well as changing the behavior of the players. Thus, this report describes the development of a game to make the field of IT more appealing to girls between the ages of 10 and 14. After collecting requirements with the client and doing a literature study a design is proposed. The final product is a two-player 2D Role-Playing-Game with puzzle elements, specifically designed to be played in a classroom environment. The game takes place in a data center and will show the players the societal importance of data centers as well as the diversity of the work in data centers. The gameplay consists of exploring a data center, talking with both male and female employees in various roles, helping them with their work through minigames, and solving a mystery. The game was designed to specifically cater to girls and to break stereotypes regarding women in IT.

How people behave in social interactions is influenced by a multitude of factors. A large part of human communication is embedded within non-verbal communication. This type of communication is sent throughout social signals, that are embodied within low-level social cues (e.g. gaze, posture, gestures). In order for intelligent systems to seamlessly interact with humans, they need to possess some form of social intelligence. That includes expressing and recognising social signals. The field of social cue forecasting intends to predict low-level behavioral cues within social interactions, allowing systems to adapt their behavior according to the forecasted behavior of interlocutors, or synthesize human behavior on the basis of the prediction. Within social science theory, it has been established that these behavioral cues are dependent on social context, as well as individual idiosyncrasies. Under earlier work within human behavior synthesis, the latter has been mostly used, and referred to as ’style’. This work attempts to broaden the traditional view of style and proposes a model for incorporating both group, and individual-level style using a hierarchical latent variable model. To adapt to unseen groups, we incorporate this hierarchical latent structure into a meta-learning model. Introducing the hierarchical neural processes and social processes models. After testing these models on a real-world dataset containing triadic interactions, it turns out that most models fail due to posterior collapse. This prevents them from learning a useful latent representation containing semantic information with respect to forecasting future sequences of social cues. To combat this, a constant weight was assigned to a part of the loss term. However, as the issue still persists, it leaves us unable to prove whether our proposed method improves upon the baseline approach. Therefore, future work on posterior collapse in neural processes models is needed.