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This work in progress paper presents and motivates the design of a novel extended reality (XR) environment for artificial intelligence (AI) education, and presents its first implementation. The learner is seated at a table and wears an XR headset that allows them to see both the real world and a visualization of a neural network. The visualization is adjustable. The learner can inspect each layer, each neuron, and each connection. The learner can also choose a different input image, or create their own image to feed to the network. The inference is computed on the headset, in real time. The neural network configuration and its weights are loaded from an onnx file, which supports a variety of architectures as well as changing the weights to illustrate the training process. 

Recently there have been calls to integrate engineering design experiences to support students’ scientific understanding. There is a need for instructional strategies in which learners are encouraged to identify and reflect on ways scientific principles can be applied to inform their designs and evaluate alternative designs. Studies show that the inclusion of contrasting cases can improve students’ conceptual understanding and reasoning. Yet, such tasks depend on how they are scaffolded. In this study, pre-service elementary teachers in a conceptual physics course analyzed contrasting solutions to a design problem. Two forms of scaffolds were embedded to facilitate case evaluation: 1) identify similarities and differences and 2) evaluate and produce an argument for a “good” design solution. We investigated the scientific ideas that the participants used as they contrasted multiple design solutions and the impact of the two approaches in students’ understanding of heat transfer. We found no significant differences in students’ conceptual understanding, but the argumentation condition had a significantly larger number of scientific ideas ‘cited’, ‘explained’ or ‘applied’ in their solutions,. The results suggest that contrasting designs with argumentation may be a promising intervention to facilitate students to use science concepts in engineering design. Future work is needed in order to investigate better scaffolds that can help students’ increase in conceptual learning.