Mapping Cross-Disciplinary Collaboration in AI Curriculum: A Mixed-Methods Case Study of Teacher Learning in PD

• In Event: AERA Roundtable Session Wednesday 11:45 am - Gold 3
  In Roundtable Session: Educators Navigating AI: Identity, Practice, and Pedagogy (Table 2)

Abstract

As AI literacy gains importance in K–12 education, interdisciplinary professional development (PD) is vital—but little is known about how cross-subject teacher interactions unfold in these settings. This study combines thematic analysis and social network analysis (SNA) to examine how three high school teachers from ELA, math, and history negotiated meaning during a hybrid AI-focused PD workshop. The mixed-methods approach reveals that teachers engaged with tasks through distinct disciplinary lenses, co-constructing pedagogical interpretations and anticipating student needs. Structured, goal-oriented activities supported shifts toward student-centered thinking. Findings highlight the generative role of disciplinary tension in shaping adaptive instructional strategies. This study offers a novel analytic approach and practical insights for designing cross-disciplinary PD in emerging domains like AI education.

Authors

• Qiuqing Li, North Carolina State University

• Daria Smyslova, North Carolina State University

• Amanda MacCormac, North Carolina State University

• Shiyan Jiang, North Carolina State University