The Turing Teacher: Identifying core attributes for AI learning in K-12 - Pelaez, Alexander; Jacobson, Amal; Trias, Kara; Winston, Elaine (Frontiers in Artificial Intelligence, 2022)
Artificial Intelligence has quickly become a major area of interest for researchers. Research in the use of artificial intelligence in education is likewise increasing. Artificial intelligence in the education domain has many uses; however, using AI specifically to enhance education and teaching, more specifically in a K-12 environment poses the most significant challenges to its use. Beyond usage and application, the quality of the education is made even more arduous due to the dynamics of teaching primary and secondary school children, whose needs are far greater than simply remembering facts. Using prior historical research on AI in education, and research in online education in the K-12 space, we explore some of the hurdles that artificial intelligence applications in K-12 teaching. In addition, we provide a paradigm for a "Turing Teacher", i.e. an AI power technology for learning, specifically targeting the K-12 space. Beyond the key attributes of autonomy, adaptability and interactivity, we find the need for a Turing Teacher to aid in the mitigation of issues such as executive functioning, socialization, group work, efficacy and motivation for K-12 students. While no Turing Teacher would be completely anonymous, we present the notion that this teacher would serve in the same manner as less experienced teachers, learn from the more experienced human teachers and thus, become indistinguishable from some teachers in the field, an extension of the traditional Turing Test.