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

2025-10-21

AI Summary

Generative AI is rapidly entering classrooms, yet conventional “adoption-first, training-later” approaches overlook institutional inequities and teacher agency, exacerbating risks to educational equity. This study adopts a sociotechnical perspective, analyzing semi-structured interviews with 22 educators through qualitative thematic analysis to uncover how teachers negotiate AI’s role amid resource disparities. Findings reveal that adoption motivations, resistance strategies, and perceived value boundaries are shaped by intersecting sociotechnical dynamics—including policy constraints, professional norms, and pedagogical relationships. The study’s key contribution lies in moving beyond technological determinism: it situates teacher practice within structural inequities and educational justice frameworks, challenging dominant technical-solutionist paradigms. Empirically grounded and theoretically informed, this work provides critical evidence and conceptual pathways for redesigning AI-in-education policies, teacher support systems, and human-centered technology design—centering fairness, contextuality, and educator autonomy.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: AI for Accessibility
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Examining teacher motivations and resistance to generative AI adoption
• Analyzing sociotechnical dynamics shaping AI integration in classrooms
• Investigating how teachers align AI use with educational values

Innovation

• Semi-structured interviews with early adopter teachers
• Analyzing sociotechnical dynamics shaping AI integration
• Exploring teacher motivations and value alignment boundaries