A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model

Generative AI is reshaping k–12 learning as a multi-agent system in which goals, activities, and roles co-evolve across formal and informal environments. Following PRISMA and appraising quality with MMAT, we synthesize 84 peer-reviewed empirical studies (2020–2025) involving learners aged 3–18. Using Biggs’s 3P model as a systems lens and embedding CIMO logic, we code learning objectives, activity designs, AI role paradigms, and outcomes. Seven recurring objectives emerge (language/literacy; STEM; creativity; socioemotional skills; feedback literacy and self-regulation; motivation; AI literacy). Five dominant activity patterns are identified: dialogic tutoring and formative feedback, generative iterative co-creation, project-based problem-solving, simulation/game-based learning, and assessment support. Across studies, AI roles shift from AI-directed to AI-supported/empowered, re-allocating agency among students, teachers, and caregivers via feedback loops. Reported outcomes span three categories—epistemic, practice, and affective/identity—with opportunities of deeper knowledge, improved practice, and stronger engagement, and risks of hallucinations, reduced originality, over-reliance, motivational loss, and ethical concerns. We propose a goal–activity–role alignment heuristic for instructional design, plus safeguards around teacher professional development, feedback literacy, and ethics. We call for longitudinal and cross-cultural research to evaluate the impacts of GenAI in k–12.