Acadexis is engineered to bridge the promise of generative AI with the strict ethical, pedagogical, and security standards of higher education.
Four foundational pillars that protect faculty intellectual property and uphold the university honor code.
Unlike generic consumer AI engines that pull text from unverified internet sources, Acadexis confines inference strictly to uploaded course materials. If an answer cannot be proven with high mathematical certainty from the syllabus, our system explicitly responds with "Not found in course materials".
Faculty lecture slides, notes, and exams uploaded to Acadexis remain 100% the intellectual property of the professor and university. Your documents are never used to train public foundational AI models, nor are they indexed into open search engines.
Student queries, grades, and interaction logs are encrypted at rest with AES-256 and in transit with TLS 1.3. We uphold strict compliance with the Family Educational Rights and Privacy Act (FERPA) and global academic privacy standards.
Institutional accounts are gated behind validated university domains and SAML/Google SSO. This ensures only matriculated students and faculty members from verified departments can access courseware hubs.
How every AI query is traced, bounded, and verified against courseware.
PDF, PPTX, and DOCX files are encrypted and processed into deterministic coordinate bounding boxes with tamper-proof checksums.
When a student or researcher queries the AI, inference is strictly constrained to the course coordinate index.
The response delivers line and slide references. All query events are logged anonymously for faculty struggle heatmaps.
Our academic trust and legal officers are available to review institutional Data Protection Agreements (DPAs) and faculty security protocols.