Tech-Enabled Teacher Supports: University of Michigan
PERIOD:
TO
Culturally and pedagogically competent mathematics teachers are our nation’s most irreplaceable asset for developing powerful mathematical minds. However, secondary mathematics teachers who serve Black, Brown, and poor youth have historically struggled to turn key long-standing theories on culturally relevant-sustaining pedagogies into actionable classroom practices. In response, the Cultivate Belonging Collaborative (CBC, Matthews et al., 2023) is a professional learning opportunity that provides time and space for mathematics teachers to center their instructional improvement goals through deep self-reflection and meticulous observational analysis of their classroom practices with skilled and justice-oriented coaching support.
The framework underlying the CBC, Belonging-Centered Instruction (BCI), underscores seven dimensions of teacher practice (1. Safety to be wrong, 2. Decentering Teacher Authority, 3. Mathematics to Know Myself and My World, 4. High Standards & Rigorous Support, 5. Social & Emotional Bridging, 6. Communal Orientation, 7. Empathetic Awareness and Support) that cultivate agentic and humanizing mathematics classrooms and have been shown to predict students’ achievement, engagement, and social-emotional outcomes for Black and Brown students in mathematics (Matthews et al., 2021).
While Generative AI is not a silver bullet for alleviating entrenched racial and socioeconomic inequities within American schooling, it can be skillfully used to elevate human potential and culturally competent growth for secondary mathematics teachers and the teacher educators who support them. The current project calls for an AI-enhanced CBC coaching model that acknowledges the need for a delicate balance between human intuition-agency and technological effectiveness-efficiency. Advances in Natural Language Processing (NLP) methods have made it possible to automatically i) harness vast amounts of communication data produced in technology-mediated learning environments, and ii) quantify aspects of human cognition, affective and social processes in human-to-human and human-to-agent conversations that iii) would otherwise be impossible for human analysts to process efficiently. Thus, we aim to use GenAI to a) reduce the labor-intensive human facilitated aspects of the CBC coaching model, and b) increase teachers’ self-directed learning outside of human-facilitated coaching sessions.
Primary Investigator(s)
