Stanford tutoring brief distinguishes educator assistance from unsupervised student AI

District tutoring leaders, intervention teams, finance and family representatives need comparable descriptions of proposed services. Ask who maintains the student relationship, who notices disengagement, and what evidence matches the exact delivery model. Offer an options assessment distinguishing tutor assistance from direct student automation. The value hypothesis is avoiding a mismatch between a purchased service and the district's intended intervention. Do not transfer human-tutoring effect sizes to software or claim Stanford endorses a vendor. A short evidence review can identify a bounded pilot, but this guidance alone cannot establish educational return or local staffing capacity.

Translate each tutoring model into a data-flow diagram with the human decision points visible. Reuse existing learning and identity systems where appropriate, with limited transcript access and a tested help route. Prerequisites include defined student groups, available adult support and usable curriculum materials. Validate the whole interaction: login, approved practice, disengagement detection, human handoff and deletion. A proof of value should measure participation and independent performance separately, including learners using accommodations. The brief does not establish a preferred model provider, hosting topology or infrastructure capacity; those require product documentation and local testing.

The intervention lead should own a pilot plan with teachers, tutors, privacy and accessibility staff. Identify who monitors attendance, contacts students and handles distress or inappropriate output. Train staff on those responsibilities before student access, and communicate the limits of AI assistance to families in accessible language.

Proposed acceptance

sampled students reach an empowered adult through the published route, transcript controls pass agreed tests, and independent outcome reporting accompanies usage data. Review staffing and equity findings before expanding. Risks include treating supervision as nominal, underfunding follow-up and allowing attractive engagement metrics to displace the learning objective.