# Generative AI in Education

A family nurse practitioner (FNP) program had 98 students use a generative AI persona prompt in Claude Sonnet to create a virtual patient on a video-conferencing platform, and after a 30-minute prebriefing, about 75 minutes of scenario work, and a 45-minute PEARLS-based debriefing, the most common one-word reaction was “helpful” (n = 83), 95% said the activity facilitated their learning about SDOH (n = 82), 78% chose the two highest comfort levels for screening for and addressing SDOH (average 4.1/5), the SDOH quiz average was 94% (n = 98), and 54% answered the question on specific strategies to address SDOH correctly.

A family nurse practitioner (FNP) program had 98 students use a generative AI persona prompt in Claude Sonnet to create a virtual patient on a video-conferencing platform, and after a 30-minute prebriefing, about 75 minutes of scenario work, and a 45-minute PEARLS-based debriefing, the most common one-word reaction was “helpful” (n = 83), 95% said the activity facilitated their learning about SDOH (n = 82), 78% chose the two highest comfort levels for screening for and addressing SDOH (average 4.1/5), the SDOH quiz average was 94% (n = 98), and 54% answered the question on specific strategies to address SDOH correctly.

This Perspective argues that generative AI should not be understood as just another teaching tool for efficiency and personalization, but as an active, persistent, generalist, and increasingly autonomous cognitive artefact and 'epistemic infrastructure' that restructures schools' cognitive ecologies by redistributing epistemic labour and consolidating feedback, assessment, explanation, content generation, and tutoring within a single interface; on this basis the authors identify two enduring misalignments, a pedagogical gap between learning sciences and AI design and a goal gap between measurable performance and developmental aims, both reflecting logics of efficiency, standardization, and control already embedded in existing educational systems, which GenAI does not introduce but risks en

This Perspective argues that generative AI should not be understood as just another teaching tool for efficiency and personalization, but as an active, persistent, generalist, and increasingly autonomous cognitive artefact and 'epistemic infrastructure' that restructures schools' cognitive ecologies by redistributing epistemic labour and consolidating feedback, assessment, explanation, content generation, and tutoring within a single interface; on this basis the authors identify two enduring misalignments, a pedagogical gap between learning sciences and AI design and a goal gap between measurable performance and developmental aims, both reflecting logics of efficiency, standardization, and control already embedded in existing educational systems, which GenAI does not introduce but risks en