Developed for my final year MComp Project module, EduGov aimed to use incidental learning to teach users about government structures and politics.
Players play as a procedurally generated government and must react to events and news about their country as they play. They are also presented with opportunities to proposes changes their country's government.
The world map and country borders are generated using multiple cellular automata. Constituencies, politicians, legislative bodies, and more are all procedurally generated and unique for each country.

Early in development the project suffered from bloated features and over-scoping. This led to a number of features which had initially been planned being cut from the final artefact. This issue was compounded by to unforeseen complications due to the Covid-19 pandemic.
If revisited, EduGov would benefit from it's events being updated and expanded upon with additional events. This would help make the game more engaging and provide more gameplay variety.
A more ambitious feature which got cut was the full simulation of all non-player controlled countries. This would make the generated world more dynamic and would be appropriate for a version of EduGov aimed at teaching elements of international relations.

The game's design was informed by an academic literature review conducted as part of the MComp project. One of the key findings of the literature review was the need to make the game low-fidelity so that it did not impose significant resource burdens on civic engagement or education initiatives which may makes use of the project's findings.

Players are able to make numerous changes to their government including electoral system, legislative system, term duration, representative system, and others.