AI-related collaboration in the networked enactment of professional capital: a mixed methods case study of school-based PLCs

Purpose Recent research has examined teachers' artificial intelligence (AI) competence, trust, and integration strategies, yet less is known about how AI-related work is coordinated through professional relationships. This study examines how AI-related collaboration is organized within professional learning community (PLC) infrastructure over time and how these relationships shape professional capital in practice. Design/methodology/approach A mixed methods case study was conducted in a Taiwanese junior high school engaged in national digital learning initiatives. The schoolwide PLC network provided organizational context, while two-wave AI Use and AI-integrated curriculum design (AI-ICD) networks among focal PLC educators were examined using descriptive network analyses and stochastic actor-oriented models. Interviews, observations, and organizational records were used to interpret the relational patterns. Findings AI-related collaboration showed two distinct but connected relational patterns. AI Use became more boundary-spanning, while AI-ICD remained concentrated within subject-aligned relationships. AI Use ties were positively associated with later AI-ICD ties, suggesting that exploration-oriented ties may have provided relational footholds for curriculum integration. Qualitative evidence showed how these arrangements shaped access to expertise, opportunities to develop AI-related pedagogical competence, and the exercise of professional judgment. It also identified time, readiness, perceived control, leadership recognition, and co-planning and verification routines as relevant conditions. Originality/value The study advances professional capital and network scholarship by showing how recurring AI-related demands are coordinated through existing PLC relationships and routines in distinct but connected relational forms. It contributes to the special issue by showing how relational arrangements shape access to expertise, pedagogical competence, and professional judgment under AI-related conditions.