Relief or displacement? How teachers are negotiating generative AI's role in their professional practice

TL;DR

The paper investigates how K–12 teachers negotiate the integration of generative AI in the context of district policies, training, and classroom practice. Using semi-structured interviews and a sociotechnical frame grounded in the Teacher Response Model, it reveals dual tensions between relief (time savings, expanded pedagogy) and displacement (threats to professional judgment, relational learning), and between mediating student AI use and building teacher expertise. The study highlights multi-layer dynamics—individual boundary-setting, peer interpretive communities, fragmented policies, and broader cultural ethics—that shape adoption and sustainability. Its contributions offer design and policy implications that center teachers, foster situated agency, and promote AI literacy as collective capacity-building to prevent inequities in genAI-enabled education.

Abstract

As generative AI (genAI) rapidly enters classrooms, accompanied by district-level policy rollouts and industry-led teacher trainings, it is important to rethink the canonical ``adopt and train'' playbook. Decades of educational technology research show that tools promising personalization and access often deepen inequities due to uneven resources, training, and institutional support. Against this backdrop, we conducted semi-structured interviews with 22 teachers from a large U.S. school district that was an early adopter of genAI. Our findings reveal the motivations driving adoption, the factors underlying resistance, and the boundaries teachers negotiate to align genAI use with their values. We further contribute by unpacking the sociotechnical dynamics -- including district policies, professional norms, and relational commitments -- that shape how teachers navigate the promises and risks of these tools.