Assessment Integrity Studio
Validity, adaptive testing, verified authorship, shorter scoring cycles
Psychometrics · Item banks · Edu-Tech Assessments · regulated assessment rails
◎Interactive verification · Score provenance · Human override
Learning Intelligence Operations Start With The ThesisThis is not a generic AI tutor demo. It is a Learning Intelligence Operations console: strategy, partners, governance, evidence, and workflows in one place.Check-in: can the room see the difference between AI as a feature and AI as an operating system?
Regulated · Graph-Grounded · Assessment-Native AI Platform
✶
Strategy Posture
Build · Partner · Scale
Responsible · Trusted · Impactful
Priority Sequence Priority SequenceThe sequence matters: assessment integrity first, workflow copilots second, internal workflow pods third, then governed tutoring and skills-to-career pathways.Ask: does this ordering make sense given our trust advantage?
Five strategic priorities, sequenced by proof value and trust impact
Faculty Copilot
Lesson planning, rubricing, feedback quality, prep-cycle reduction
Courseware · standards alignment · instructor workflows · virtual learning
□Rubric coverage · Override tracking · Quality review
Internal AI Workflow Pod
Content production, support, engineering, sales ops, cycle-time reduction
Revenue Ops · product operating model · content lifecycle · SOP systems
↗Portfolio intake · SOP rollout · Value tracking
Guided Tutoring
Mastery gains, productive struggle, grounded help, learner trust
Edu+ · MyLab · eText · knowledge graph · learner graph
✳Stepwise hints · Grounded retrieval · Escalation rules
Skills-to-Career Pathways
Credential conversion, enterprise expansion, role redesign, learner mobility
Credly · Faethm · skills graph · English testing · workforce data
◇Task ontology · Credential wallet · Enterprise insights
Partner Ecosystem Partner-Aware, Not Vendor-LockedThe models and cloud platforms can change. Durable value is context, evidence, assessment validity, governance, and portability.Key takeaway: the model is replaceable; the learning intelligence layer is not.
Strategic Partners Across the Value Chain
| Cloud & Infrastructure |     |
|---|---|
| Platform Services |     |
| System Integrators |   |
| Learning Ecosystem |        hevia |
| Data & AI Foundation | Learner GraphContent GraphKnowledge GraphSkill Graph Analytics |
Open Standards·Interoperable·Secure·Composable
Operational Proof Layer Operational Proof LayersThis is where the strategy becomes touchable: verified exam flow, learner intervention queue, skill evidence, materials policy, and prerequisite trace.Tip: Hover or click elements beneath to see live workflow data routing.
Source-informed controls translated into governed, production-safe demo workflows
High-stakes moments are managed through identity, authentication, reservation, session state, submission, and escalation evidence.
Identity checkedReservation activeHuman override required
Avg scoring cycle: 6 days → 2 days with automated gate checks
Learner signals become routed cases with owner, SLA, consent marker, care team role, and auditable next action.
Within SLAFERPA-awareOwner assigned
Intervention SLA met: 94% of cases within 48h
The system separates scaffolded help from independent performance so AI support cannot create false readiness.
Hint level capturedConfidence deltaTransfer checked
Lesson completion: 78% → 91% after scaffolding redesign
Course materials carry source mode and provider permissions: local only, external allowed, pending approval, or blocked.
Local-only sourceProvider routedTrace retained
Policy violations caught at intake: 100% — zero escaped to production
Outcomes connect to prerequisites, content, assessments, and remediation actions before AI generates practice.
Standards mappedPrereq riskPractice generated
Prereq gap identification: automated vs 3-day manual review previously
Governance & Audit Log
System of record for key actions and controls
Audit Checklist Mode: Check rows below to confirm verified status for cohort rollout. 5/5 gates signed off.
| Timestamp | Domain | Action | Scope / Target | Actor | Risk | Status | Trace Evidence | Action | |
|---|---|---|---|---|---|---|---|---|---|
| Jun 12, 2026 14:32 | Content QA | QA Gate Passed | Module 7: Adaptive Retrieval Practice — STEM Cohort | L. Chen | Low | Completed | All 5 readiness checks passed; lesson approved for learner release | ||
| Jun 12, 2026 11:07 | Workflow Redesign | Intake SOP Deployed | Technical Data Intake System — All Departments | R. W. | Medium | Completed | Centralized GUI intake eliminated 60% of repeat information requests across tech and production teams | ||
| Jun 11, 2026 16:55 | Assessment Graph | Scoring Provenance Rule Added | Module 4: Secondary Math — Fall 2025 STEM Cohort (n=312) | M. O'Neill | High | In Review | No high-stakes score ships without lineage and human override. Avg completion: 78% → 91% post-scaffolding | ||
| Jun 11, 2026 09:18 | AI Controls Board | Pilot Charters Expanded | Faculty Copilot · Assessment Studio · Internal Ops Pod | A. Patel | Medium | Completed | Business case, control gates, and KPI baselines assigned. Content QA cycle: 11 days → 4 days | ||
| Jun 10, 2026 14:07 | Partner Management | Portability Review | AxisPath LMS · Meridian Cloud · ClearStride QA | R. Singh | Low | Completed | Model abstraction and data portability clauses confirmed. No single-vendor dependency. |
Case Study Workflow Transformation: Technical Data Intake & Show Deployment
How eliminating information silos and manual handoffs transformed operational readiness across multi-department teams.
▶ Before State
Input: Technical riders, venue specs, production notes — scattered across email, PDFs, and verbal briefings
Process: Institutional knowledge & manual interpretation — inconsistent role assignments, repeated questions, delayed readiness
Pain: High cognitive load · Information silos · Rework cycles · Missed technical requirements
Manual handoffs
Verbal coordination
No single source of truth
→
✓ After State
Input: All technical documentation entered once through a centralized GUI intake platform
Process: Automated categorization & role assignment → Department-specific action queues auto-populated
Outcome: Faster prep · Fewer missed requirements · Consistent execution · Reduced cognitive load
Repeat requests: ↓ 60%
Onboarding time: halved
Single source of truth
Design Principle: I don't start with AI. I start with the workflow. Find where understanding breaks down, where work piles up, where teams are waiting — then design the system that eliminates the friction. AI is one of the tools, not the destination.
Strategy Atlas
Three strategic routes, one recommended sequence. Click a route to explore.
Assessment First
Defend the trust moat. Integrity studio, adaptive testing, psychometric validation, human override.
PriorityWorkflow Copilots
Faculty copilot, internal AI workflow pod, cycle-time baselines, value realization.
NextGraph Platform
Knowledge, learner, assessment, and workforce graphs. Skills-to-career pathways.
Scale90 daysControls Board, pilot charters, eval harness, first graph domains.
6 monthsFaculty copilot live, assessment studio piloted, internal workflow pod running.
1-3 yearsGrounded tutoring, adaptive assessment, skills navigator, enterprise pathways.
3-7 yearsLifelong learner identity, workforce intelligence, AI-native credentials.
| Route | What we own | Proof metric | Risk posture |
|---|---|---|---|
| Assessment integrity | Validity, adaptive variants, explainable scoring | Reliability, verified authorship | Trust moat |
| Faculty copilot | Planning, rubrics, feedback, standards alignment | Prep time saved, override rate | Oversight required |
| Internal AI workflows | SOPs, workflow redesign, cycle-time reduction | Cycle time, defect escape rate | Adoption variance |
| Skills-to-career | Skill graph, credentials, workforce pathways | Credential conversion rate | Integration complexity |
Partner Ecosystem Studio
Each partner plays a specific role. Click any tile to see what they bring and why they matter. Drag tiles to explore.
Cloud and infrastructure
Platform services
System integrators
Learning ecosystem
Simple version: partners provide infrastructure, platforms, integrations, and learning-system surfaces. The durable value is the governed Edu-Tech-owned context that connects content, assessment, learner evidence, and workforce pathways.
Course Builder
No lesson goes live without evidence. Every item here must pass a readiness gate — rubric, QA sign-off, and source provenance — before it can publish.
D
1
Draft
Review queue handoff
Checklist · QA owner missing
Draft
P
2
In Progress
Convert friction into a requirement
Practice · 23 min
In progress
Write the readiness evidence field
Assignment · Content Ops
Needs rubric
B
1
Blocked
Tutor guardrails and approved context
AI support · 4 stale files
Blocked
P
2
Published
Define the learning operation
Text · 12 min
Published
Identify roles and handoffs
Activity · 18 min
Published
Learner Progress
Each learner moves through four evidence levels. Blocked learners need intervention before they can advance.
AssistedNeeds hints
→
ScaffoldedGuided pass
→
TransferNew context
→
UnassistedIndependent
| Learner | Group | Evidence | Current issue | Status | Owner / SLA |
|---|---|---|---|---|---|
| Maya Chen | June Ops A | Assisted only | Cannot explain readiness evidence without hints | Blocked | Instructor / 12h |
| Andre Wilson | June Ops B | Transfer partial | Review queue handoff needs owner proof | Needs review | QA Partner / 24h |
| Priya Shah | June Ops A | Scaffolded pass | Convert friction into requirement | Active | Advisor / 48h |
| Lucas Martin | June Ops C | Unassisted pass | Complete | Ready | Program owner / closed |
| Elena Ruiz | June Ops B | Hint level 5 | Needs parallel example before unassisted check | Blocked | Instructor / overdue |
REPORTS: ACTIVE & RESOLVED
Incident logging queue - Click any report for dispatch details
🚨
Overdue learner intervention (Elena Ruiz)
⚠️
Readiness evidence rubric missing
⚠️
Tutor context stale
📡
QA handoff checklist completion
✓
Portability review complete
✓
AI SOP published
✓
Grounding dataset approved
Readiness Rules
Each rule is a gate — content cannot publish until all gates pass. Click any rule to see what happens when it fails.
Content Quality
| Rule | What it checks | Evidence | Status |
|---|---|---|---|
| Every lesson has one objective | Each lesson maps to a learning outcome | Objective field present | Passing |
| Practice lessons require a rubric | Assignments can be graded consistently | Rubric + sample response | Failing |
AI Governance
| Rule | What it checks | Evidence | Status |
|---|---|---|---|
| Tutor uses approved context only | AI answers draw from vetted sources | Knowledge pack freshness | Review |
| External AI requires permission | Material doesn't leave the boundary without approval | Source policy flag | Passing |
Learner Evidence
| Rule | What it checks | Evidence | Status |
|---|---|---|---|
| Scaffolded support needs unassisted check | AI-helped learners prove independent competence | Hint level + confidence + transfer | Review |
| High-stakes assessment needs session evidence | Exam integrity is verifiable end-to-end | Identity, start, submit, override | Blocked |
| Blocked learners have an owner | No learner is stuck without a responsible party | Owner assignment | Passing |
TriggerEvent or timer fires
→
ClassifyMap to risk type
→
RouteAssign to owner
→
Human GateApprove or reject
→
ExecuteRun the action
→
AuditLog the result
Workstreams
Automated triggers that detect problems, assign owners, and require human approval before acting.
Triggered
1
Assessment session
Type: Time based
Assessment Lead
Failed
Classified
1
Tutor answer flagged
Type: Event based
Learning Systems
Draft
Routed
1
Hint level reaches 5
Type: Event based
Advisor
Dispatched
Human Gate
1
External AI requested
Type: Event based
Content Ops
Held
Executed
1
Lesson updated
Type: Event based
QA Partner
Succeeded
Audited
1
Learner blocked 48h
Type: Time based
Instructor
Succeeded
AI QA Release Gate
Before any AI feature goes live, it must pass these checks: accuracy, safety, grounding, and human review.
Grade level, vocabulary, subject-matter knowledge, purpose, and text complexity checks run before release.
1 heldTask tier chooses an approved provider while tutor, quiz, summary, and assessment workflows stay stable.
PortableHigh-stakes use requires item provenance, explainable scoring, verified session state, and human override.
Blocked| Check | Dataset / Evidence | Pass rule | Result | Owner |
|---|---|---|---|---|
| Approved model | Model registry | Uses allowed provider and task tier | Passing | AI Ops |
| Grounded answer | 24 learner questions | Uses approved source set | 23 / 24 | Learning Systems |
| Grade-level fit | Sample generated feedback | Reading level matches learner band | Passing | Learning Science |
| Vocabulary load | Course explanation samples | Terminology is defined or scaffolded | Passing | Instructional Lead |
| Subject matter knowledge | Expert-reviewed items | No factual or concept drift | Review | Assessment Lead |
| Purpose alignment | Prompt version samples | Output matches tutor, feedback, or assessment task | Passing | AI Ops |
| Stale context | Knowledge pack freshness | No file older than lesson draft | Failing | Content Ops |
| Red-team prompt | Policy bypass set | Refuses unsafe or ungrounded requests | Review | QA Partner |
| Trace review | Latest 50 simulated runs | No hidden context leak | Passing | AI Ops |
| Assessment integrity | Adaptive item samples | Explainable scoring and item provenance | Review | Assessment Lead |
| Verified exam session | Reservation and session state | Identity, authentication, start, submit, completion | Blocked | Assessment Lead |
Architecture + Integrity Layer
The system's continuity plan: what exists, where it came from, how it is tested, and how a future team can pick it up.
Click a layer to filter the table below. Click again to show all.
Inventory, provenance, and verification answer: what exists, where did it come from, and how do we know it works?
Inventory nextSecurity, operations, recovery, and controlled change keep the demo hostable and maintainable.
Backend plannedIntent, comprehension, and governance preserve understanding for future operators.
Docs active| Domain | Layer | Plain English | Current evidence | Next production step |
|---|---|---|---|---|
| Inventory | A | List what the system contains. | Single HTML prototype plus docs | Create inventory.json and asset registry |
| Provenance | A | Record where artifacts came from. | Internal source-map boundary documented | Track AI-assisted edits and asset origins |
| Verification | A | Show how it was tested. | JS parse checks and route checks | Add browser smoke tests and interaction audit |
| Security | B | Protect keys and viewer access. | No API key in frontend | Server-side PIN gate plus OpenRouter function |
| Recoverability | B | Make it restorable. | Stable local file path | Hosted release records and rollback notes |
| Documentation | C | Record intent so future operators understand decisions. | ASIF-X framework documented in prototype | Publish operator guide and decision log |
| Compliance | C | Prove the system meets governance requirements. | AI SOP, policy center, and controls board in LIO | Map each control to a regulatory requirement |
Governance Control Room
Responsible AI in operation — not a policy document. Five live gates, each with an owner, evidence requirement, and a status that blocks or clears downstream deployment.
✓
AI Controls Board
Active
Approves every model route and workflow deployment. Nothing goes live without a signed board record. Meets fortnightly.
📄 Decision record
Governance
🧑⚖️
Human Override
Required
High-stakes decisions require a human review step. No AI output can trigger a grade change, exam block, or remediation without an override path.
🔀 Override path documented
Assessment Lead
📂
Material Permissions
Under review
Controls which source files can cross the local boundary into external AI routes. Review triggered by three newly approved example packs pending classification.
📋 Source policy v2 in draft
Content Ops
⚖️
Bias + Fairness
Monitored
Tracks learner-outcome variance across demographic segments. Eval runs flag drift above threshold for Learning Science review before next release.
📊 Eval run — last May 19
Learning Science
🗂
Audit Retention
Draft
Every AI-influenced decision must be explainable after the fact. Retention rule defines what is logged, for how long, and who can access it. Rule is in draft — not yet blocking but must resolve before production hosting.
📝 Retention rule — draft v1
AI Ops
Model Registry
Which AI models are approved, what they are used for, and what happens if one is unavailable.
| Route | Model / provider | Use case | Boundary | Status |
|---|---|---|---|---|
| Live tutor | OpenRouter / google/gemma-4-31b-it:free | Presenter and tutor assistance | Server-side key only | Scaffolded |
| Safety check | Optional guardrail route | Policy and source-mode review | Backend only | Next |
| Static fallback | Local simulated response | Offline presentation backup | No network | Available |
The model registry abstraction means switching providers requires a config change, not a code rewrite. No vendor lock-in.
PortableEach route is classified by risk level and cost. High-stakes assessment tasks require more expensive, more reliable models.
TieredNo model route goes live without passing the evaluator gate: accuracy, grounding, safety, and grade-level checks.
Gate activePolicy Center
Every rule that governs material use, AI boundaries, assessment integrity, data handling, and access control — in one place.
Active Policies
| Policy | Rule | Scope | Enforcement | Status |
|---|---|---|---|---|
| Local-only materials POL-2026-001 | Do not send restricted content to external AI. | All content | Automated gate | Active |
| External AI routing POL-2026-002 | Only approved examples or public-safe content may use live AI routes. | AI tutor, QA | Source flag check | Active |
| Assessment boundary POL-2026-003 | AI supports practice; high-stakes use requires provenance and human override. | Assessment | Rubric + override | Active |
| Data residency POL-2026-004 | Learner PII stays within the governed boundary. Identity is separated from model prompts. | All systems | Architecture layer | Active |
| Vendor portability POL-2026-005 | Policy rules are provider-agnostic. Switching vendors does not require policy rewrites. | Infrastructure | Abstraction clause | Active |
Incoming Policies
| Policy | Rule | Scope | Target date | Status |
|---|---|---|---|---|
| Viewer access control POL-2026-006 | PIN gate and analytics for demo sharing; not suitable for confidential data. | Portfolio | Q3 2026 | Draft |
| Formative vs. summative split POL-2026-007 | Formative AI is dialogic; summative AI requires provenance, psychometrics, human override. | Assessment | Q3 2026 | Review |
Phased-Out Policies
| Policy | Original rule | Reason retired | Replaced by | Status |
|---|---|---|---|---|
| Manual-only review POL-2025-001 | All AI output required line-by-line human review before display. | Replaced by confidence-threshold gating | Assessment boundary | Retired |
| Single-vendor lock POL-2025-002 | All AI routing must use a single approved provider. | Multi-vendor abstraction proved more resilient | Vendor portability | Retired |
Audit Log
Every important demo action leaves a visible receipt with timestamp, actor, and resulting state.
Audit view opened: current session receipts visible.
Presentation mode, partner selections, exports, filters, and tutor actions are recorded here.
| Event | What changed | Evidence | Owner |
|---|---|---|---|
| Dashboard opened | Strategy atlas active | Breadcrumb and active nav | Richard W. |
| Partner selected | Partner detail context updates | Interactive map card | Viewer |
| Board brief generated | Text artifact previewed | Preview modal | Presenter |
AI Tutor Console
An AI tutor that asks for the learner's attempt first, gives progressive hints, and tracks whether the learner can succeed independently.
Live AI Sandbox & Playground
Compare responses and telemetry parameters side-by-side across three registered LLM engines based on complexity tier.
Complexity Routing Bias:
Simple = Gemma 9B (Local/Fast) | Intermediate = GPT-4o Mini (Cost-Optimized) | Advanced = Claude 3.5 (Max Fidelity)
Gemma 2 9B
Simple Route
Click Compare to see response simulation.
Latency:—
Tokens:—
Cost:—
Grounding:—
GPT-4o Mini
Intermediate
Click Compare to see response simulation.
Latency:—
Tokens:—
Cost:—
Grounding:—
Claude 3.5
Advanced Route
Click Compare to see response simulation.
Latency:—
Tokens:—
Cost:—
Grounding:—