A Safeguarding-Aware Agentic AI Tutor for Montessori-Inspired Personalised Education

This record documents the design and classroom trial of a working AI tutoring system built for adolescents at Blue Blocks Montessori School, Hyderabad, and the architectural argument that follows from it: that personalisation, teacher oversight, and child safeguarding should live inside one auditable pipeline rather than being bolted onto a general-purpose chatbot. The system runs twelve distinct tutor personalities from a single shared codebase. Nothing is duplicated per child — each student's preference profile and evolving memory record are injected into the same orchestration loop, so the same physics question returns a Hinglish film analogy to one student and a headed, step-by-step derivation to another. The preference vocabulary itself was not written by adults: it was elicited from the students in an earlier co-design study, in which the same cohort designed their own ideal AI teachers and built prototypes of them. Around that personalisation layer sits the safeguarding machinery. Every student message passes a four-state triage before any model call is made, so distress, bullying, or self-harm signals never reach the tutoring path at all and instead surface as structured alerts to the supervising adult. A second monitoring loop, the Hydra dashboard, gives one teacher a class-wide view of confidence, confusion, frustration, weak topics, homework status, and open alerts without requiring them to read a single transcript. The paper is deliberate about what it does not claim. The trial was a feasibility exercise with twelve students under a 1:4 adult supervision ratio, not an efficacy study. No learning gains were measured, no baseline comparison was run, and no formal student feedback was collected. The paper states its own boundaries plainly: the triage layer is heuristic rather than clinically validated, it filters student input only and not model output, the dashboard scalars are operational attention cues rather than psychometric measures, and the JSON storage layer is prototype-grade and unfit for child-data processing at scale. Also documented here: full implementation stack, per-turn control flow with code listings, the memory and reflection update logic, the homework and notification subsystem, and the deployment conditions — school-administered institutional API account, no personal student accounts, on-premises sessions only, adult present throughout. The architecture is pedagogy-agnostic. Swapping the profile vocabulary, planner action set, and safeguarding thresholds reconfigures it for exam-preparation, narrative-led, or special-needs settings. It is offered as a reference template for schools now deciding how children will work alongside AI tutors.