How to Teach Students to Vet AI Platforms: A Hands-On Evaluation Lab

Hook: Turn student anxiety about “which AI to trust” into practical skills

Students, teachers, and lifelong learners are overwhelmed by AI platforms that promise personalization but hide complex risks: leaked data, biased outputs, and unclear compliance. In 2026, those risks matter more than ever. This hands-on vendor lab teaches students how to run an evidence-based AI evaluation—from security review and bias testing to regulatory compliance—using real public-company moves (for example, BigBear.ai’s 2025 strategic pivot) and a practical vendor questionnaire they can reuse.

Why this lab matters in 2026

Late 2025 and early 2026 saw three trends that make AI vendor vetting a core skill for students:

• Stronger public-sector standards: FedRAMP adoption accelerated across government and contractors—BigBear.ai’s acquisition of a FedRAMP-approved platform is a teachable signal that government-readiness is a market differentiator.
• Legal/compliance pressure: The EU AI Act and expanded data-protection scrutiny are moving from rulemaking into enforcement, meaning organizations must demonstrate documented risk assessments.
• Transparency expectations: More vendors publish model cards, safety reports, and red-team results. Students need to know how to find and interpret them.

Lab overview: goals, duration, and outcomes

Learning objectives

• Perform a practical security review of an AI platform using public documentation and basic pentesting checks.
• Measure model accuracy and robustness using small labeled datasets and adversarial prompts.
• Run repeatable bias testing and interpret fairness metrics.
• Complete a vendor compliance questionnaire and create a mitigation plan.
• Produce a vendor scorecard and recommendation for adoption or further controls.

Materials and class setup

• Teams of 3–4 students, one platform per team (examples: large cloud providers, specialized AI vendors, or open-source stacks).
• Public vendor docs (security whitepapers, model cards, SOC/FedRAMP attestations), demo accounts or sandbox keys, and a small labeled dataset relevant to the use-case (education, HR, or health depending on class).
• Tools: Python notebooks, evaluation libraries (AIF360, Fairlearn, OpenAI Evals or similar), and vulnerability scanning basics (OWASP ZAP for web interfaces).
• Timeframe: 2–4 class sessions (3 hours each) or a week-long module for deeper dives.

Step-by-step lab activity

Phase 0 — Prep and discovery (30–60 minutes)

1. Assign teams and platforms. Encourage a mix of public cloud services and smaller vendors so students experience different transparency levels.
2. Collect public filings and press: follow the vendor’s recent moves (e.g., acquisitions, FedRAMP approvals). Use those items to set hypotheses—does FedRAMP approval improve security posture or primarily serve government sales?
3. Distribute the vendor questionnaire (see template below) and ask teams to fill it using public sources first, then request missing items from the vendor if possible.

Phase 1 — Security review (2–4 hours)

Goal: identify obvious weaknesses and confirm compliance claims.

• Checklist items (students must tick and justify):
  • Certification review: SOC 2, ISO 27001, FedRAMP (Low/Moderate/High). What does each attestation cover?
  • Encryption: Data-at-rest and in-transit, key management, BYOK (bring-your-own-key) options.
  • Access controls: SSO, RBAC, MFA. Can the vendor segregate tenant data?
  • Logging & monitoring: Audit trails, retention periods, and which events are logged (inputs, outputs, API calls).
  • Software supply chain: Dependency scanning, SBOM availability, patch cadence.
  • Pen-test and red-team results: Are summaries available? What gaps were found and fixed?
• Practical checks:
  • Review API behavior with safe test inputs. Look for verbose errors that leak stack traces or secrets.
  • Run a basic vulnerability scanner against any web interface (respecting terms of service).
  • Check public breach databases and vendor transparency reports for incidents.

Phase 2 — Accuracy & robustness (3–6 hours)

Goal: quantify how well the model performs on realistic tasks and whether small perturbations break it.

• Choose 200–1000 labeled examples in the domain (smaller classroom-scale datasets are fine).
• Compute metrics: precision, recall, F1, accuracy, and calibration (probability vs. true likelihood).
• Robustness tests:
  • Prompt perturbation: paraphrase inputs, add noise, or use synonyms to measure stability.
  • Adversarial examples: tiny edits that flip classification—document how often this happens.
  • Latency and failure modes: record times and any undefined outputs or hallucinations.
• Deliverable: confusion matrix, example failure cases, and a short explanation of likely root causes (training data gaps, fine-tuning artifacts).

Phase 3 — Bias testing (3–5 hours)

Goal: surface disparate outcomes across protected groups and measure fairness with standard metrics.

• Design slices: race/ethnicity, gender, age, and intersectional slices (e.g., older women of a particular region).
• Compute fairness metrics:
  • Demographic parity (difference in positive rates)
  • Equalized odds (difference in true positive/false positive rates)
  • Counterfactual testing: change protected attribute in input and measure change in output.
• Use open-source tools like AIF360 or Fairlearn for automated reports. Document limitations: small sample sizes, label quality, and proxy attributes.
• Deliverable: fairness report with prioritized mitigation suggestions (data augmentation, reweighting, post-hoc calibration).

Phase 4 — Compliance & vendor questionnaire (2–4 hours)

Goal: confirm legal fit and identify contractual controls.

Provide this vendor questionnaire template to students and require answers with evidence (links to documentation, screenshots of attestations):

• Data handling
  • What data does the vendor retain? For how long? Can data be deleted on request?
  • Does the vendor use customer data to retrain models? If yes, how is consent handled?
  • Where are data centers located? Are there options for regional data residency?
• Regulatory controls
  • FedRAMP/SOC 2/HIPAA/HITECH/FERPA or other relevant attestations—provide scope docs.
  • Has the vendor performed a DPIA (Data Protection Impact Assessment) or equivalent?
• Model governance
  • Is there a model card? Version history? Responsible ML team contacts?
  • Are red-team or adversarial test results available, and how often are they run?
• Liability & contract
  • What indemnities or SLAs are offered? Are limitations of liability fair for the use-case?

Phase 5 — Scoring, mitigation, and presentation (2–3 hours)

Create a simple weighted rubric (example below), produce a vendor scorecard, and present a 10-minute recommendation: adopt, adopt with controls, or reject.

Sample scoring rubric (classroom-ready)

Use a 100-point scale. Suggested weights:

• Security & certifications: 30 points
• Accuracy & robustness: 25 points
• Bias & fairness: 20 points
• Compliance & legal fit: 15 points
• Transparency & explainability: 10 points

Thresholds for recommendation:

• >85: Candidate for adoption with monitoring
• 65–85: Adopt with mitigations (specific contractual and technical controls)
• <65: Reject or pilot further with strict isolation

Case study: BigBear.ai as a teaching moment

In late 2025 BigBear.ai publicly eliminated debt and acquired a FedRAMP-approved AI platform—moves that reshape risk and opportunity. Use this public company news as a classroom prompt:

• What does acquiring FedRAMP approval buy a company? (Access to government contracts, demonstrated baseline security controls.)
• What are the limits? (FedRAMP scope might cover infrastructure but not downstream model behavior or data provenance.)
• How do revenue trends and customer concentration affect vendor risk? Falling revenue or government dependence can increase long-term support risk.

Ask students to synthesize a vendor brief: summarize the public move, infer likely risk changes, and recommend contractual clauses to protect an educational buyer (e.g., data portability and escrow for models/config).

Advanced strategies and 2026 trends students should know

• Continuous monitoring: the new standard—adopt runtime monitoring for drift, hallucinations, and unusual output distributions.
• Model provenance registries: organizations increasingly keep ML registries to track training data and lineage; ask vendors for provenance metadata.
• Watermarking & content provenance: in 2026, watermarking standards matured—check if the vendor supports provenance metadata for generated content.
• Responsible procurement: procurement teams demand modular SLAs that attach to specific model versions and use-cases rather than blanket platform promises.
• Adversarial testing culture: red-team reports, third-party audits, and bug-bounty programs are becoming buyer expectations.

Tools and resources for the classroom

• Fairness libraries: AIF360, Fairlearn
• Evaluation frameworks: OpenAI Evals (or similar) for automated scenario testing
• Security references: OWASP, MITRE ATT&CK for ML (where available), and the FedRAMP marketplace listings
• Documentation sources: vendor model cards, SOC 2/FedRAMP documentation, and public red-team reports

Assessment: deliverables and grading rubric

Require each team to submit:

1. Vendor scorecard (one page)
2. Technical appendix: test scripts, confusion matrices, fairness reports
3. Vendor questionnaire responses with evidence and a list of open items
4. Presentation: 10-minute recommendation and mitigation plan

Classroom tips and common pitfalls

• Warn students about small-sample fallacies—interpret statistical metrics cautiously when datasets are small.
• Emphasize reproducibility: keep notebooks and seeds so others can replicate tests.
• Teach students to separate product marketing from attestation evidence—press releases are useful context but not proof.
• Encourage creative adversarial tests but respect vendor terms of service and legal boundaries.

“An attestation like FedRAMP signals a baseline, not a guarantee. Real risk reduction comes from continuous testing and contractual safeguards.”

Practical takeaways for teachers and learners

• Turn public-company news into lab prompts: acquisitions, FedRAMP approvals, and financial moves reveal vendor incentives and risk.
• Use a simple rubric to make subjective judgments objective and reproducible.
• Require evidence: screenshots, links, and attestations should back every claim on a vendor questionnaire.
• Teach mitigation-first thinking: if a vendor scores poorly, propose specific, time-bound mitigations rather than only rejecting it.

Final project idea: institutional AI policy brief

As a culminating assignment, have students produce a 2–3 page institutional AI policy that maps a use-case to procurement controls, vendor requirements (minimum FedRAMP/SOC 2, DPIA), runtime monitoring expectations, and an incident-response playbook. Use the BigBear.ai example to show how vendor moves change institutional risk profiles.

Closing: Make vendor vetting a repeatable classroom practice

In 2026, AI evaluation is no longer an abstract topic—it's a practical, repeatable skill. This lab-style approach teaches students to probe platforms for security, accuracy, bias, and compliance using public signals and concrete tests. By combining a vendor questionnaire, scoring rubric, and the discipline of continuous monitoring, students leave prepared to make defensible procurement recommendations.

Call to action: Try this lab in your next course. Start by assigning one team to analyze the public record (press releases, FedRAMP listings) of a vendor like BigBear.ai and one team to run hands-on tests. If you’d like a downloadable vendor questionnaire and scoring spreadsheet based on this lab, sign up for the Learningonline.cloud instructor pack or contact us to get classroom-ready materials and answer your implementation questions.

Related Reading

• ‘Very Chinese Time’: How Viral Memes Become Global Fan Culture — A Football Case Study
• How TMS-Integrated Autonomous Fleets Could Make Medical Supply Chains More Resilient
• DIY Syrups as Fragrance Accents: How Cocktail Flavours Inform Perfume Layering
• How Transit Marketers Should Think: Sprint vs Marathon for Passenger Experience Upgrades
• Festival Money Checklist: What to Carry When Attending Paris’ Unifrance Rendez-Vous