Towards a systematic educational framework for human-machine teaming

Artificial Intelligence (AI) and machine learning (ML) are having a great impact on all aspects of society. However, due to the technical competencies and mathematical understanding required for implementing solutions leveraging these technologies, access to the communities working on these technologies is limited to those having these skills. This limits the ability of domain experts to directly transfer their knowledge and contribute to the development of AI and ML systems. To address this problem, we propose the Human Education AI Teaming (HEAT) framework, in which we draw on human education to design an innovative education system to enable collaboration between humans and AI cognitive agents. The main aim of HEAT is to promote the social integration of AI by allowing domain experts to focus more on communicating a body of knowledge to the machine, and less on the computational, data, and engineering concepts associated with how the machine learns. We follow an educational theory-driven approach to derive the content knowledge and competencies required by each agent. We conclude the paper with a demonstration case study explaining how the complex autonomous guidance of a flock of sheep could leverage HEAT to make the technology accessible by empowering non-AI specialists, livestock farmers in our example.