A Teacher’s Playbook for Vetting AI Tutors: Questions to Ask Vendors

Stop guessing — start vetting: a teacher’s playbook for AI tutors in 2026

Adopting an AI tutor shouldn’t feel like rolling dice. Teachers and school leaders tell us the same pain points again and again: AI outputs that are factually wrong, unclear policies about who owns student data, and vendor claims that vanish under scrutiny. If your district or classroom is evaluating AI tutors in 2026, this playbook gives you the precise questions, checks, and a printable checklist to bring rigor, safety, and instructional value to procurement.

Top-line: why rigorous vetting matters now

AI in education is maturing fast — but trust has not kept pace. By early 2026 we’ve seen major shifts: cloud providers launching sovereign regions for data residency (for example, the AWS European Sovereign Cloud), and more vendors seeking FedRAMP authorization to serve public-sector customers. Still, many AI systems are best used for execution and tutoring tasks — not autonomous decision-making — and require human oversight.

That reality changes what you ask vendors. Your goal is simple: adopt tools that improve learning outcomes while keeping students’ data private and teachers in control. This playbook focuses on the five themes every educator and procurement team should demand: accuracy, transparency, data use, remediation, and support & compliance.

How to use this playbook

Read the quick checklist first. Then use the detailed sections and example vendor questions during demos and RFPs. Finish with the pilot metrics and scoring rubric to decide whether to scale. Print the one-page checklist and keep it at your desk — it’s meant to be used in meetings.

Quick action: printable one-page checklist (scroll or print)

Copy or print this block for vendor meetings. Each line is a binary check: yes/no/needs follow-up.

☐ Accuracy: vendor provides third-party validation of content accuracy (studies or audits)
☐ Transparency: model provenance and training data categories are disclosed
☐ Explainability: tutor provides source links and chain-of-thought on demand
☐ Data use: student data retention, sharing, and deletion policies are explicit
☐ Data residency: options for local/sovereign hosting (EU, state-level) offered
☐ Compliance: FedRAMP/SOC 2/ISO 27001 attestation provided (or roadmap)
☐ PII controls: fine-grained redaction and role-based access available
☐ Human-in-the-loop: teachers can review, override, and edit tutor outputs
☐ Error remediation: incident reporting, rollback, and compensation process
☐ Pedagogy: vendor maps tutoring strategies to standards and learning objectives
☐ Assessment integrity: anti-cheating and exam-mode features exist
☐ Integration: SIS/LMS integration with standards (LTI, OneRoster) is supported
☐ Support & training: SLA, onboarding, and teacher training plan provided
☐ Licensing: clear per-student/per-seat pricing and cancellation terms
☐ Pilot metrics: vendor supports A/B testing and learning outcome reporting
  

Deep-dive: the 9 question themes (and exact vendor questions)

Below are the nine topic areas you should cover in demos, contract negotiations, and pilots. For each, we include specific, copy-paste questions you can use.

1. Accuracy & instructional fidelity

Accuracy isn’t just “no wrong answers.” It’s alignment to curriculum standards, age-appropriate explanations, and measurable learning gains.

• Can you share independent evaluations or peer-reviewed studies that measure learning outcomes when students use your tutor? If so, provide the datasets and methodology.
• How do you measure factual accuracy across subjects and grade bands? What are your baseline accuracy rates by domain (math, ELA, science)?
• Do you maintain a human-curated knowledge base or only rely on a general LLM? How often is it reviewed by subject-matter experts? (Ask for a model card or equivalent documentation.)
• How does the system handle uncertainty? Does it signal low-confidence responses and offer conservative defaults?

2. Transparency & explainability

Teachers need to understand how a tutor reached an answer to decide whether to trust it. Transparency reduces the time teachers spend fact-checking — a problem many classrooms still face.

• What model architecture(s) power the tutor? Which parts are proprietary vs. open-source? Provide model provenance and training categories (not just “proprietary corpus”). See examples of vendor transparency practices in deep-dive guides such as evidence-capture playbooks for how to surface logs and provenance.
• Can the tutor provide source citations or editable explanations for each response (chain-of-thought disclosure)? Consider vendors that support explainable summarization and source links so teachers can verify claims quickly.
• What logs are available to teachers and administrators? (e.g., prompts, model outputs, timestamps, confidence scores)
• Do you publish a model card and data sheet describing training data categories, limitations, and known biases?

3. Data use, privacy, and student sovereignty

Student data protection is often the make-or-break factor for schools. In 2026, expect questions about data residency and sovereign cloud options.

• Exactly what categories of student data do you collect, process, and store? Provide a data inventory.
• Do you use student data to train or fine-tune models? If so, is it opt-in and can customers opt out? How is data anonymized or aggregated? Vendors that promise non-training clauses should make that explicit in contract language and DPA amendments; see model non-training clauses in RFP templates and discussions around reducing AI exposure.
• Where is customer data hosted? Do you offer region- or country-specific data residency (for example, EU sovereign clouds)? Consider edge and low-latency hosting options explored in edge migration writeups like edge migrations.
• Which certifications and attestations do you hold? (FedRAMP Moderate/High, SOC 2, ISO 27001, COPPA/FERPA compliance statements)
• What is your data deletion and porting process at contract end or on request?

4. Security & compliance (FedRAMP & sovereignty concerns)

Government funding and district-level procurement increasingly require FedRAMP or equivalent assurances. If you serve public schools, demand proof.

• Are any parts of your stack FedRAMP-authorized or do you run on a FedRAMP-authorized cloud? If not, what is your roadmap and timeline for authorization? Vendors are increasingly naming specific timelines in security roadmaps—ask for those timelines and evidence.
• Do you support deployment to sovereign cloud environments (e.g., AWS European Sovereign Cloud, or region-specific private clouds)? Consider whether on-device or local-region hosting is needed; see notes on on-device and localized storage.
• Provide recent penetration test reports and remediation records. Who conducts your third-party audits?
• How do you isolate customer workloads? Explain role-based access, encryption-at-rest, and key management policies.

5. Remediation, error handling, and liability

No system is error-free. Great vendors plan for harm mitigation — from incorrect tutoring to data breaches.

• Describe your incident response playbook for both content errors (misleading/incorrect instruction) and data security incidents.
• How are teachers alerted when the system flags a high-risk or low-confidence response?
• What remediation options are available to affected students (regrading, content correction, credit restoration)?
• Explain contractual liability limits and indemnification clauses related to incorrect instruction or data breaches. Legal teams often reference audits like legal tech audits when negotiating incident and liability language.

6. Human-in-the-loop & teacher control

AI should augment teachers, not replace them. Demand controls that let educators supervise and adapt the tutor’s behavior.

• Can teachers review and edit AI-generated feedback before students see it? Is there an approval workflow?
• Are there adjustable pedagogical settings (e.g., scaffolding level, hint frequency, Socratic vs. direct-tutorial style)?
• Does the system support teacher-created content and rubrics, with the AI adapting to those artifacts? Human-in-loop patterns are central to defensible deployments; see how human review loops are used in broader guided-AI discussions like guided AI learning tool playbooks.

7. Assessment integrity and academic honesty

AI tutors can both help and hinder assessment integrity. Ask how vendors support trustworthy assessment practices.

• How does the tutor detect or deter misuse during high-stakes assessments? Describe exam-mode features and proctoring integrations.
• Does the system help teachers design higher-order tasks that reduce cheating risk and encourage authentic work?
• What logging and audit trails exist for suspected misconduct? Consider whether audit trails meet standards discussed in evidence capture and preservation guidance.

8. Integration, interoperability & operational fit

Practical integration matters: if it’s a silo, adoption will stall. Demand standards-based interoperability.

• Which standards do you support? (LTI, OneRoster, SFTP, SCORM, xAPI)
• Can you export learning data in interoperable formats so our data team can run custom analytics?
• Explain single-sign-on (SSO) options and role mappings with our SIS or identity provider. If the vendor integrates with consumer platforms or voice assistants, confirm compatibility and any privacy implications discussed in pieces like Siri + Gemini developer notes.

9. Support, training, and pilot metrics

Good onboarding makes or breaks classroom adoption. You’ll need concrete SLAs and a pilot plan tied to outcomes.

• What onboarding, professional development, and in-class coaching do you provide? Include hours and costs.
• What is your standard SLA for uptime, response times, and critical incident resolution?
• Can you support a randomized pilot and deliver pre/post learning measurements (effect sizes, gains by subgroup)? Vendors that publish pilot playbooks and real-world results (including pilot effect sizes) demonstrate maturity—look for documented pilots or case studies and ask for raw data access.

Scoring rubric: a quick way to compare vendors

Turn qualitative answers into a decision-ready score. Use a 0–3 scale per theme: 0 = fails, 1 = partial, 2 = good, 3 = best-in-class. Weight the themes to reflect local priorities — most districts put data use/privacy and accuracy at high weight.

• Data & privacy: weight 25%
• Accuracy & pedagogy: weight 25%
• Transparency & explainability: weight 10%
• Security & compliance: weight 15%
• Remediation & liability: weight 10%
• Support & pilot capability: weight 15%

Aggregate weighted scores to prioritize vendor shortlists. Require vendors to verify claims with documentation — not just slide decks.

Pilot design: questions to validate real classroom impact

Don’t buy at scale without a structured pilot. Here’s a minimal pilot plan that teachers can run in 6–8 weeks.

1. Define success metrics: learning gains by standards-aligned assessments, engagement metrics (time-on-task), teacher time saved (minutes per assignment).
2. Randomize classes into control vs pilot groups where feasible.
3. Require vendor-provided dashboards and raw data exports for independent analysis.
4. Document qualitative teacher feedback weekly and log edge-case errors for remediation tracking.
5. After 6–8 weeks, evaluate both quantitative and qualitative outcomes and decide whether to expand, modify, or terminate.

Real-world examples and 2026 trends to watch

Recent developments show the landscape shifting toward candidate solutions you can trust. In early 2026, major cloud vendors began offering sovereign cloud options to meet regional data-sovereignty rules, and some defense- and government-focused AI companies have secured FedRAMP or similar approvals to enter public-sector contracts. Meanwhile, independent research continues to show that practitioners trust AI for execution but remain cautious about strategy-level decisions — a pattern that favors classroom tutors built with clear human oversight.

Case snapshot: a mid-sized district ran a pilot with an AI tutor that provided editable explanations and source citations. The vendor had a SOC 2 Type II report and offered data residency in the state. The district’s pilot showed a 0.28 effect size increase on targeted standards-aligned pre/post tests and a 20% drop in teacher grading time over six weeks. Key success factors: teacher control over outputs, explicit data use agreement, and daily teacher logs for remediation. For context on how practitioners measure wins and operational ROI, review practical case studies and consolidation examples such as consolidation case studies where tool consolidation cut time and clarified ownership.

Contract language to request (boilerplate starter)

When negotiating, include explicit clauses for the following:

• Data Use: "Provider will not use customer student data to train models without explicit opt-in from the customer; any aggregated use will be anonymized and reversible upon request."
• Data Residency: "Customer data will be stored and processed within the agreed geographic region (e.g., EU/US/state) and not replicated elsewhere without consent." (Consider whether you need edge or localized hosting described in edge migration resources such as edge migration writeups.)
• Security Attestations: "Provider will maintain and produce on request current SOC 2/ISO 27001/FedRAMP documentation and remediation records."
• Remediation & Liability: "Provider will maintain an incident response plan and provide credit or remediation for documented instructional harms attributable to vendor errors."

Common vendor red flags

• Vague answers about training data or model provenance ("proprietary corpus") without model cards.
• Refusal to sign FERPA/COPPA-compliant DPA language or to negotiate data deletion terms.
• Lack of support for region-specific data residency or refusal to run in sovereign cloud zones.
• No teacher-facing logs or inability to export learning data for independent analysis.
• Claims of replacing teachers rather than augmenting and giving granular teacher controls.

Actionable takeaways for your next vendor meeting

• Bring the one-page printable checklist to every demo and mark questions as you go.
• Request documentation for every claim: model card, SOC 2/FedRAMP attestation, independent study, and penetration-test reports. Practical guidance on preserving logs and evidence is available in investigation and preservation playbooks like evidence capture.
• Insist on a short pilot with measurable outcomes before any district-wide contract is signed.
• Negotiate clear data residency and non-training clauses to protect student data sovereignty. Also evaluate on-device or local-storage options discussed in on-device AI storage resources.
• Use the scoring rubric to compare vendors objectively, and weight privacy & accuracy highest.

Final thought: trust is built, not marketed

By 2026 the marketplace is clearer: technical options for compliance and sovereignty exist, and mature vendors will support rigorous pilots, transparent documentation, and teacher control. Vendors that resist scrutiny are the ones to avoid. Your students and teachers deserve tools that demonstrably improve learning while protecting privacy and keeping educators in charge.

Call to action

If you’re preparing an RFP or pilot, download our editable checklist and sample RFP language (PDF) or schedule a 30-minute consultation with our curriculum and privacy specialists to tailor the rubric to your district. Let’s make AI tutoring safe, accurate, and teacher-led. For additional reading on responsible deployments and practical explainability approaches, see resources on reducing AI exposure (privacy-first device use) and LLM comparisons when choosing a model provider.

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