Luke Swanson, Field Day Lab, University of Wisconsin-Madison David Gagnon, Field Day Lab, University of Wisconsin-Madison Jennifer Scianna, Field Day Lab, University of Wisconsin-Madison John McCloskey, Field Day Lab, University of Wisconsin-Madison Nicholas Spevacek, Field Day Lab, University of Wisconsin-Madison Stefan Slater, Graduate School of Education, University of Pennsylvania Erik Harpstead, Human-Computer Interaction Institute, Carnegie Mellon University Abstract: The ability for an educational game designer to understand their audience's play styles and resulting experience is an essential tool for improving their game's design. As a game is subjected to large-scale player testing, the designers require inexpensive, automated methods for categorizing patterns of player-game interactions. In this paper we present a simple, reusable process using best practices for data clustering, feasible for use within a small educational game studio. We utilize the method to analyze a real-time strategy game, processing game telemetry data to determine categories of players based on their in-game actions, the feedback they received, and their progress through the game. Introduction Playtesting is a well-adopted method for iteratively testing and improving educational games. As a game moves through development phases, members of the target audience are given versions of the game to play, and in exchange generate feedback. This feedback can then be used to validate the design decisions made during the game's development, and to direct the next iterations of work.

Lakeland is using artificial intelligence to reduce the number of deadly car crashes. We've all seen that driver who speeds into the intersection trying to beat the red light. The City of Lakeland is using artificial intelligence so that traffic signals can identify reckless drivers before they cause a crash. "It will tell us as a car is approaching the intersection, the likelihood of it stopping," said Jeff Weatherford, traffic operations manager for the City of Lakeland. Lakeland's Intersection Collision Avoidance Safety Program, or iCASP works by delaying the green light of cross-traffic up to four seconds, when sensors detect a vehicle is going to run a red light.

LAKELAND, Fla. - The Florida Department of Transportation just gave Lakeland $500,000 to install artificial intelligence at 25 intersections, in the hopes of reducing the number of traffic crashes in the city. Last summer, the city kicked off a pilot AI program at four intersections. It works by installing a series of sensors at least 150 feet away from an intersection. "It is measuring the speed of the vehicle, the distance from the intersection," explained city spokesman, Kevin Cook. If the potential red-light runner looks like they are going to run the light, the computer keeps the light red for the cross-traffic for up to four seconds, which keeps them out of harm's way.