  Current research on Explainable AI (XAI) heavily targets on expert users
(data scientists or AI developers). However, increasing importance has been
argued for making AI more understandable to nonexperts, who are expected to
leverage AI techniques, but have limited knowledge about AI. We present a
mobile application to support nonexperts to interactively make sense of
Convolutional Neural Networks (CNN); it allows users to play with a pretrained
CNN by taking pictures of their surrounding objects. We use an up-to-date XAI
technique (Class Activation Map) to intuitively visualize the model's decision
(the most important image regions that lead to a certain result). Deployed in a
university course, this playful learning tool was found to support design
students to gain vivid understandings about the capabilities and limitations of
pretrained CNNs in real-world environments. Concrete examples of students'
playful explorations are reported to characterize their sensemaking processes
reflecting different depths of thought.
