Several enduring challenges persist in physical education (PE), such as inadequate demonstration, ‘eyeballing’ assessment of motor performance, and subjective feedback. These issues could potentially be addressed through the integration of technology employing artificial intelligence (AI) to enhance learning. This approach involves embedding a predefined (criterion) motor action into a human pose estimation (HPE) algorithm. Learners can observe this motor action on an iPad and utilize its kinematic characteristics to guide practice of the motor skill. The learner’s (executed) movements are then captured by the device, and the recorded motor action is analyzed by the HPE algorithm to assess movement proficiency. The algorithmic computation generates a quantitative measure of movement quality, providing learners with prompt and objective feedback. Additionally, a separate device utilized by the PE teacher compiles quantitative data from other students in the class. This collective information aids the teacher in selecting appropriate instructional strategies.