Mixed-Method Analysis of Preservice Teachers’ Evaluation of AI-Generated Social Justice Lesson Plans

• In Event: AERA Roundtable Session Friday 9:45 am - Gold 4
  In Roundtable Session: Mixed Methods Frontiers: Leveraging AI in Multiple and Mixed Methods Research

Abstract

This study uses a mixed-method approach to examine preservice teachers’ evaluation of AI-generated lesson plans. Quantitative analysis produces non-linear responses to evaluation criteria, illustrating that PSTs’ evaluations of lesson plans involve multiple aspects of social justice teaching, rather than assuming that additional prompts and details will result in better lesson plans. The qualitative analysis results provide additional insights into PSTs’ evaluations, such as how they recognized human agency and the ethics of AI in their teaching with AI. Considering these aspects, we argue that preparing teachers for education with AI requires fostering a critical partnership that addresses multiple facets and relationships associated with AI. This approach may require reconceptualizing teacher autonomy and agency in collaborations between humans and technology.

Authors

• Younkyung Hong, Ball State University

• Yunsoo Park, Iowa State University

• Sydney McCammon, Ball State University