Technosocial Change Agency: We saw in our prior work that learners needed more opportunity to experiment with mechanics of training data in AI to understand its workings and ethical impacts. This motivated the creation of the Data Detectives educational game.
Game Description: Transformational games are an under-explored, engaging way to help learners understand complex and serious topics. We designed the game with learning science-backed design principles, as well as multiplayer aspects to enable benefits from peer-to-peer and intergenerational learning.
Challenge Addressed: Understanding mechanics of AI and connections to ethics can be difficult for learners to grasp.
Goal: We aim for this game to help fill this learning need, leveraging and supporting playful, social learning.
Context: Based at Carnegie Mellon University supported by a NSF-funded AISL research grant and ongoing
Key contributions: Designed game, engaged in rapid prototyping, carried out playtests, published a poster and lightning talk related to teh game
Team members: Prof. Amy Ogan, Prof. Jessica Hammer, Jennifer Kim, Shixian Xie, Ellia Yang
For more information, please reach out to me or take a look at some of our publications from this project on my research page.
Players embody two roles the 'Training Data Master' and the 'Algorithm,' where the training data is sorted with a secret rule that the algorithm must guess
Design principles are backed by scientific findings to support social learning and make learning the mechanical aspects of AI less intimidating