Augmented Cognition for Teaching: Transforming Teacher Work with Intelligent Cognitive Assistants
K-12 STEM teachers are critical to the US economy. With investments in teaching quality representing enormous economic value, the quality of education has been identified as a significant determinant of gross domestic product economic growth. However, the US teacher workforce is experiencing a crisis: teacher demand exceeds supply at every level, and attrition is extraordinarily high for new teachers. Further, while STEM teaching represents one of the areas of highest need, STEM teachers leave the profession at high rates. These developments call for innovative workforce augmentation technologies to improve K-12 STEM teachers' performance and quality of work-life. To address this critical national need, the project will investigate how intelligent cognitive assistants for teachers can transform teacher work to significantly increase teacher performance and teacher quality of work-life. The project centers on the design, development, and evaluation of the Intelligent Augmented Cognition for Teaching (I-ACT) framework for intelligent cognitive assistants for teachers. With a focus on assisting K-12 STEM teachers in technology-rich inquiry teaching that supports collaborative, problem-based STEM learning, I-ACT cognitive assistants provide teachers with (1) prospective pedagogical guidance (preparation support preceding classroom teaching), (2) concurrent pedagogical guidance (real-time support during classroom teaching), and (3) retrospective pedagogical guidance (reflection support within a community of practice following classroom teaching). The project will culminate with an experiment conducted with a fully implemented version of I-ACT in public middle schools in North Carolina and Indiana.
The project realizes its objective through two primary thrusts. First, the research team will design and develop I-ACT cognitive assistants for K-12 STEM teachers and test them in public school classrooms. Utilizing AI-based multimodal learning analytics and a social constructivist theory of pedagogy, I-ACT cognitive assistants use machine-learned models of teacher orchestration to provide guidance throughout the full teaching workflow. I-ACT cognitive assistants operate in a tight feedback loop in which collected data will drive successive iterations of machine learning to train refined teacher support models for improved I-ACT cognitive assistant functionalities. Second, the research team will investigate how I-ACT cognitive assistants improve K-12 STEM teacher performance and teacher quality of work-life. The team will conduct focus groups, case studies, semi-structured interviews, and observations of teachers using I-ACT cognitive assistants in school implementations with middle school science teachers at the project's partner schools. The team will also conduct quasi-experimental studies to determine I-ACT impact on teacher performance and quality of work-life.