Community Playbook: Designing a Unified Exam Delivery & Instant-Score Platform — share patterns, integrations & runbooks
Problem: organizations keep gluing different scheduling tools, delivery systems, proctoring services and reporting stacks, creating manual work, slow score flows, and inconsistent candidate experiences. Goal: co-create a vendor-neutral playbook of reusable patterns, connectors and operational recipes for a single platform that manages candidate workflows, booking & proctoring, secure delivery, scalable grading, real‑time score exports and compliance reporting.
What I’m asking this community to share (please include role, org size, and industry when posting):
- Concrete integration patterns and API contracts: sample OpenAPI specs, webhook designs, event models for candidate lifecycle, and data schemas for scores, audit logs and accommodations.
- Connector examples: LMS/HR/CRM syncs, booking engines, proctoring adapters—links to repos, pseudo-code, or middleware patterns you use.
- Proctoring risk matrices and triage playbooks: how you map risk -> proctor level -> mitigation & evidence retention policies.
- Scheduling & no-show playbooks: automated reminders, rescheduling rules, refund/tokens policies, and UX patterns that reduce dropouts.
- Scalable grading strategies: auto-scoring limits, hybrid human+AI workflows, batching/worker patterns, fairness checks, and latency/throughput numbers you measure.
- Real-time score export and auditing: formats, retained fields, push vs pull architectures, and sample webhooks or streaming schemas.
- Compliance & privacy templates: GDPR/CCPA controls, retention schedules, encryption-at-rest/in-transit practices, and sample compliance reports or checklist items.
- Monitoring, SLAs & runbooks: key SLOs, alert thresholds, synthetic tests, incident playbooks for exam-day outages, and RTO/RPO targets.
- Accessibility and accommodation patterns: testing approaches, record-keeping, and software/human workflows that worked for you.
- Benchmarks and test harnesses: load tests, test data sets, and methods you used to validate scale and reliability.
How to contribute: post links to public repos/gists, snippets (OpenAPI, SQL, example payloads), short how-to notes, or attach templates (policy, SLA, checklist). If you’re willing to curate or lead a section of the playbook, say so and list preferred section(s).
Priority ask: if you can only share one thing right now, post either (a) a minimal candidate lifecycle event model (JSON) that your systems exchange, or (b) a one-page incident runbook for an on-exam outage. Those two artifacts will help seed the repo and conversations.
Please keep contributions practical and attribution-friendly (license note or preferred reuse terms). Looking for first-hand experience and real artifacts—what actually worked, what failed, and tradeoffs you accepted.