Curriculum-scale AI without sacrificing the faculty of record.
ArthurAI™ University Learning Edition treats the faculty member as the faculty of record. AI-assisted curriculum, advising, and assessment artifacts surface for review and attestation before any artifact reaches a student or grade.
- Curriculum at scale
Generate course schedules across an entire program.
Course generation runs as an async pipeline through Azure Service Bus — request → queue → AI generates curriculum (subjects → weeks → days → lessons) → status tracking → completion. Scale from one course to a full program without breaking the faculty workflow.
Generation pipeline → - Faculty of record
The AI never publishes. Faculty does.
Every AI-suggested artifact (lesson, assessment, advising note, grade) routes to the faculty member with confidence calibration, source citations, and an explicit review-and-attest checkpoint. Accreditation agencies receive a documented AI-use posture at onboarding.
The faculty-of-record posture → - AI tutor with citations
Tutoring that shows its work.
Context-aware tutoring with optional web-augmented answers, citation discipline, and conversation persistence. The text-selection feature lets students highlight a passage and ask 'Explain this' for an in-context answer drawn from the lesson scope.
Tutor architecture → - Faculty analytics
Per-student, per-cohort, per-assessment.
Quiz session telemetry, attempt patterns, and competency aggregations roll up to faculty dashboards. Analytics support advising and intervention without crossing into autonomous decision-making about student progression.
Assessment + analytics → - Multi-language at scale
9 languages, RTL, and AI in every one.
International cohorts, satellite campuses, joint programs — the platform speaks the language of the student. AI-generated curriculum can target any of 9 supported languages. The tutor responds in the student's selected language. RTL flips automatically for Arabic and Urdu.
Language architecture → - Research-data posture
Research data stays where it should.
Multi-tenant isolation at five layers — database queries always filter by institution, file storage is tenant-scoped, audit logs are tagged with the institution identifier. Research data never crosses tenant boundaries by construction.
Research + IP handling → - Branded surfaces
The platform looks like your university.
Per-institution theming with primary, secondary, tertiary, dark, light, and accent colors. Branded emails. Branded certificates with verification. AI theme generation from a natural-language prompt for fast institution onboarding.
Multi-tenant architecture → - Accreditation-ready
Audit trails the way an accreditor expects.
Immutable-style audit logs capture every operation on student records with actor, timestamp, action, artifact. Logs are retention-aligned and tamper-evident. AI use is documented at the institutional level for accreditation review.
Security + audit →
Eight more capability deep-dives, all code-true.
- LCPResearch-grade cognitive profile
- Lesson generationPer-student adaptive engine
- AI tutorStreaming tutor + safety gates
- AssessmentsFive formats with anti-gaming
- CertificatesVerifiable institutional credentials
- Reports22 prebuilt + custom builder
- DashboardsAt-risk + AI adoption rate
- Multi-languageTranslation pipeline
FERPA · ADA / Section 504 / 508 · WCAG 2.1 AA · GDPR for EU/EEA cohorts · Kenya DPA 2019 for East African deployments. See the full compliance posture, faculty-of-record disclosure language, and data handling.