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November 30, 2025International Journal of Artificial Intelligence in Education7 citationsOpen Access

Examining Student Teachers’ Agency in an AI-Supported Learning Environment: Q Methodology Research

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YCYoumen ChaabanSJSong-gyo JungJMJohanne Medina

Key Points

  • Q methodology identified distinct student teacher agency profiles in an AI-supported classroom environment.
  • Students showed varied engagement levels and ethical considerations when interacting with AI tools.
  • Assessment within a classroom management course utilized insights from a socio-cognitive framework.
  • Findings highlight the complexities in preparing teachers for responsible AI use in education.

Abstract

Abstract Drawing on a socio-cognitive framework, we explored student teachers’ agency along intrapersonal, behavioral and environmental dimensions within the context of a classroom management course. The study investigated the enactment of student agency in an AI-supported learning environment using an innovative AI conversational chatbot whose outputs are based solely on curated course material. Using Q methodology, the analysis of 22 Q sorts revealed three distinct factors: (1) “I got this…just leave me alone” – self-reliant users confident in their abilities but exhibiting limited ethical awareness and reflective practices, (2) “Cipherbot is helpful , but…” – balanced users demonstrating intentional and ethical engagement, and (3) “A blind date with Cipherbot” – uncritical users heavily reliant on Cipherbot with diminished agency and criticality. The results are discussed in light of ongoing deliberations on the complexities of preparing student teachers for ethical and agentive GenAI use.

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Cite This Study

Chaaban et al. (2025) studied this question.

synapsesocial.com/papers/692b943e1d383f2b2a378b54https://doi.org/10.1007/s40593-025-00529-y
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