Search and Filter

Repository Feedback

Your feedback helps us improve the repository's content relevance and usability. Please share your thoughts to help us better serve researchers and practitioners.

Submit feedback

Submit a research study

Contribute to the repository:

Add a paper

Relief Or Displacement? How Teachers Are Negotiating Generative Al'S Role In Their Professional Practice

Aayushi Dangol, University of Washington
Smriti Kotiyal, University of Washington
Robert Wolfe, Rutgers, The State University of New Jersey
Alex J. Bowers, Columbia University
Antonio Vigil
Jason Yip, University of Washington
Julie A. Kientz, University of Washington
Suleman Shahid, Lahore University of Management Sciences
Tom Yeh, University of Colorado Boulder
Vincent Cho, Boston College
Katie Davis, University of Washington
Date
Publisher
arXiv
Link
http://arxiv.org/abs/2510.18296v1
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.
Study at a glance
  • Question: How do K-12 teachers perceive and navigate the opportunities and challenges of generative AI tools in their practice?
  • Setting and data: A large public school district in the United States, spring 2025, semi-structured interviews with 22 K-12 teachers.
  • Method: Semi-structured interviews analyzed using reflexive thematic analysis.
Why it matters

As generative AI rapidly enters classrooms, policies and procurement often lack teacher input, risking the deepening of inequities and disruption of relational teaching practices.

Key findings
  • Teachers experienced a central tension between relief from administrative burnout and fear of displacing their professional voice, judgment, and relationships with students.
  • Educator adoption was driven by district policy signals, peer networks, and burnout prevention, while reluctance stemmed from time limits, cheating stigmas, traditional preferences, and environmental concerns.
  • Teachers established strict boundaries around tool use by manually editing outputs, redacting student data for privacy, and restricting generative AI during mastery assessments.

Summary drafted with AI from the full paper and may contain errors. See the full paper for details.

What is the application?
Teaching – Instructional Materials,
Teaching – Assessment and Feedback,
Teaching – Professional Learning,
Learning – Student Support,
Communicating / Social Tools,
Organizing,
Analyzing
Who is the user?
Educator
Who age?
Elementary (PK5),
Middle School (6-8),
High School (9-12)
Why use AI?
Efficiency,
Outcomes – Literacy,
Outcomes – Numeracy,
Outcomes – Other Academic,
Outcomes – Differentiation,
Outcomes – Durable Skills,
Reimagined Schooling
Study design
Descriptive – Implementation and Use
Download PDF