Artificial Intelligence (AI) is becoming a more integral part of daily life, yet many people
still have very little insight on it. Machine Learning makes up a large portion of AI
technologies and often makes use of black box models which make it even harder for users
to understand and trust the process. Education on such important topics is therefore
increasing in importance. Visualisation and interactivity through video games for learning
can provide a great foundation for tackling this, and some projects [1] have already looked
into creating educational games to provide users with a stronger idea of how certain AI
models work.
This FYP expands on this through the research and development of a visual game
like application to educate and present the topic of Neural Networks. The aforementioned
application involves the implementation of Neural Network that can interface with an
endless runner game, and a user interface designed with explainability in mind that allows
users to interact and experiment with the Neural Network. By making use of this, users
can collect data, train and optimise the Neural Network to have it play the endless runner
game, presenting the structure and setup of Neural Networks.
Evaluation is carried out through a user study, where participants are presented with
the game, as well as accompanying pre and post surveys. These surveys have been adapted
and adopted from previous studies [2] and aim to gauge how effective the application is in
illustrating these concepts. The results indicate that such an application is quite effective
to help people understand more complex concepts.
References/Biography
[1] L. B. Fulton, J. Lee, Q. Wang, Z. Yuan, J. Hammer, and A. Perer, “Getting playful
with explainable ai: Games with a purpose to improve human understanding of ai,”
03 2020
[2] G. Petri, C. Gresse von Wangenheim, and A. Borgatto, “Meega+: A method for the
evaluation of educational games for computing education,” 07 2018
Student: Paul Psaila
Course: B.Sc. IT (Hons.) Artificial Intelligence
Supervisor: Dr Vanessa Camilleri
Co-supervisor: Prof. Matthew Montebello