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Artificial Intelligence and Machine Learning continue to increase in popularity. As a result, several new approaches to machine learning education have emerged in recent years. Many existing interactive techniques utilize text, image, and video data to engage students with machine learning. However, the use of physiological sensors for machine learning education activities is significantly unexplored. This paper presents findings from a study exploring students’ experiences learning basic machine learning concepts while using physiological sensors to control an interactive game. In particular, the sensors measured electrical activity generated from students’ arm muscles. Activities featuring physiological sensors produced similar outcomes when compared to exercises that leveraged image data. While students’ machine learning self-efficacy increased in both conditions, students seemed more curious about machine learning after working with the physiological sensor. These results suggest that PhysioML may provide learning support similar to traditional ML education approaches while engaging students with novel interactive physiological sensors. We discuss these findings and reflect on ways physiological sensors may be used to augment traditional data types during classroom activities focused on machine learning.