Human Tutoring Augmented by Artificial Intelligence (AI): A Tutoring Analytics and Performance Support Model to Improve the Work and Professional Growth of Future Tutors

University Of Colorado At Boulder, Boulder CO

Investigators

Abstract

This research project addresses the urgent national need to dramatically enlarge the tutoring workforce, which is primarily comprised of paraprofessionals with limited formal training as educators. Human tutoring has been repeatedly shown to produce large impacts on student learning, and is now poised to become a core structural part of daily schooling. Tutors can play an important role in addressing widening achievement and opportunity gaps as well as in attending to students’ increased social-emotional needs. As the tutoring workforce expands, new technologies and professional learning models will be critical to preserve and enhance tutoring's learning benefits for students, including students from historically marginalized groups and low socio-economic backgrounds. This project will develop an AI-augmented professional support model for on-the-job training that centers personalized, data-driven feedback in order to enhance tutors’ practices as well as their professional growth. This project will contribute vital new knowledge towards building capacity for tutors to provide high quality instruction to all students and for tutoring to serve as a pathway into education professions, potentially helping to address the dire national shortage of qualified teachers. This project brings together an interdisciplinary team of researchers from the University of Colorado Boulder working with practitioner partners from Saga Education. The former group has expertise in deep learning for speech and language processing, human-computer interaction, mathematics education, teacher learning and coaching, educational policy, and behavioral science. Complementing these perspectives, the latter group offers extensive experience in developing, organizing, and supporting mathematics tutoring programs in Title I public schools. The specific work context for this research will be an existing in-school, mathematics tutoring program provided by Saga Education. The project will develop and study an innovative tutoring analytics system, along with a toolkit that supports the learning of effective pedagogical skills as tutors engage in their everyday work. The system will enhance tutors’ pedagogical and relational skills by providing near-real-time feedback on the discourse strategies they use when working with students. More specifically, deep learning models will be used to automatically analyze tutor-student dialogues and recognize important discursive events. Multimodal feedback interfaces will provide tutors with personalized information related to each tutoring session. This multilayered approach will also include resources that support tutors’ and supervising coaches’ use of the feedback for reflective noticing and goal-setting during professional learning sessions. Research will investigate the extent to which this model changes the nature of work (tutoring interactions and practices) and leads to the professionalization of workers (tutor efficacy and work engagement). This project has been funded by the Future of Work at the Human-Technology Frontier cross-directorate program to promote deeper basic understanding of the interdependent human-technology partnership in work contexts by advancing the design of intelligent work technologies that operate in harmony with human workers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.