Teaching Real-time Object Detection with an Emphasis on Engagement and Inclusiveness

As the use of artificial intelligence surges across various sectors, institutions of higher education are witnessing a growing interest in related courses. Students from diverse professional and personal backgrounds are increasingly drawn to artificial intelligence in general and to machine learning in particular. The teaching of artificial intelligence and machine learning is gradually expanding beyond disciplines such as computer science and engineering where such material has traditionally been taught. Educators now face the challenge of creating and delivering content that is not only practical but also relevant and engaging to all participants in the educational process. Our study introduces an innovative approach to teaching computer vision at a business school through customizing a real-time object detection application that is accessible to non-computer science majors such as students specializing in entrepreneurship or information systems among other fields. This hands-on method involves students in every phase, from collecting images to building, deploying, and testing models in real time, ensuring active engagement and ownership of their learning by focusing on application customization rather than coding. We present our proposed approach, discuss insights gained from using it in the classroom, and explore its potential adaptation by educators considering it for their own teaching.