From NetLogo to LLMs: A Decade of STEM Integration Professional Development and an Emerging Verification Competency Model for Educators

Introduction. Educators report that the hardest part of integrating new technology into STEM instruction is knowing how to control it and understand its outputs. This finding recurs across a decade of the authors’ research, from agent-based modeling with NetLogo, through micro:bit programming, through NSA-funded GenCyber camps where most participants embedded cybersecurity in their classrooms, to statewide CS integration (WySLICE) and an NSF-funded RET site (WySTACK). Each intervention introduced a different technology; each exposed the same bottleneck. Generative AI amplifies this: LLMs produce fluent, plausible content requiring verification skills that most STEM educators have never been taught.

Methods. This study synthesizes data across multiple STEM integration PD cohorts. Pre/post self-efficacy measures from earlier WRNN sessions and GenCyber follow-ups provide baseline trajectories. An NSF-funded Noyce workshop series scaffolds a three-stage competency progression, comprising AI-assisted curriculum design, verification of AI-generated STEM content, and applied integration, all using AI tools within authentic disciplinary practice. Instruments capture self-efficacy, verification confidence, and implementation intent. University data (128 course experiences, nine semesters) contextualize the policy environments that educators are preparing students to enter.

Results. Across cohorts and technologies, verification-focused sessions produce the largest self-efficacy gains. Educators completing full progressions describe goals differently than single-session participants, framing AI as something students need to evaluate rather than a tool that saves planning time. University data reinforce this: engineering students in courses with no explicit AI policy report the lowest clarity and highest uncertainty about academic integrity.

Conclusions. Each new technology introduced to educators, NetLogo, micro:bit, GenCyber platforms, and LLMs, required the same scaffolding: guided adoption, structured verification, and disciplinary integration. Self-efficacy gains become most apparent at the verification stage, with novices most challenged by output evaluation. This technology-agnostic competency model, grounded in longitudinal evidence, offers PD designers a replicable framework for whatever classroom technology comes next.