Preservice Teachers’ Negotiation of Ethical Integrity and Professional Tensions in Generative AI Use in Lesson Planning



Author Information

Jiwan Lee, Georgia State University, United States
Ju Lim, Georgia State University, United States

Abstract

Generative AI (GenAI) has been positioned as a tool to support teachers’ instructional work, particularly lesson planning (e.g., Sun & Huang, 2025; van den Berg & du Plessis, 2023). Building on existing scholarship, we examine how preservice teachers (PSTs) make sense of GenAI use in practice, focusing on how they interpret, evaluate, and negotiate its affordances and risks in relation to professional responsibility and ethical integrity. The study examines: How do PSTs negotiate the affordances and risks of GenAI when using it for lesson planning? This qualitative multi-case study followed six elementary PSTs through a three-phase lesson-planning process, analyzing artifacts, AI outputs, and interviews. The analysis focused on PSTs’ perceptions of affordances, risks, and evaluative decision-making related to GenAI use. The findings show that PSTs’ engagement with GenAI involves an ongoing process of negotiation rather than adoption or rejection. PSTs moved back and forth between perceived affordances including efficiency, structural support, and idea expansion and concerns related to reliability, professional insecurity, and ethical integrity. This negotiation was enacted through evaluative decision-making as PSTs determined what to accept, adapt, or reject from GenAI suggestions, reflecting their instructional judgment, agency, and sense of responsibility. Ethical integrity guided how PSTs set boundaries around GenAI use. PSTs’ engagement with GenAI was closely tied to their instructional decision-making. PSTs maintained ownership over lesson planning through selective integration and contextual modification of AI-generated suggestions. These patterns illustrate how reflective judgment, professional agency, and ethical boundary-setting shaped the scope and limitations of GenAI use in lesson planning.


Paper Information

Conference: WCE2026
Stream: Professional Training

The full paper is not available for this title


Virtual Presentation


Comments & Feedback

Place a comment using your LinkedIn profile

Comments

Share on activity feed

Powered by WP LinkPress

Share this Research

Posted by James Alexander Gordon