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The popularity of applications involving physiological sensing (e.g., brain and muscle activity) and robotics has continued to grow in recent years. However, empirical studies evaluating ways to expose K-12 students to physiological computing are limited. To address this gap, we present PhysioBots, an educational tool designed to introduce K-12 students to physiological computing and robotics. We evaluated PhysioBots with 27 high school students between the ages of 15 and 17 to compare the use of physiological (e.g., self-induced changes in brain or muscle activity) and conventional control (e.g., keyboard) of a robot during a STEM education activity. Our preliminary results suggest that PhysioBots may improve students’ self-efficacy and programming confidence. Observations from open-ended survey questions also indicate that PhysioBots may support students in exploring ways to gamify emotional state manipulation. We discuss these findings and offer insights for future STEM education work involving physiological sensing and robotics. 

In 2019, Meta purchased CTRLLabs, a neural interface start-up, for more than $500 million. A recent report by Morgan Stanley analysts valued the total addressable market of brain-computer interfaces (BCIs) at around $400 billion in the U.S. alone. Headlines discussing the exploration of novel neural interface technologies by companies such as Blackrock Neurotech, Synchron, and Neuralink have become increasingly common. Although the media often piques our curiosity about this technology, few people truly comprehend its underlying mechanics. Over the past decade, I have dedicated my career to addressing this gap. This article shares experiences and insights obtained from introducing students to physiological computing through the Neuroblock software. 

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.