Curriculum-aware tutoring
AI that answers from the syllabus the student is actually taking — not from the open internet. Concept explanation, worked examples, practice problems generated from the unit map.
Indian universities hold examination data, student records, faculty research, and admissions information that no NEP-aligned institution should be handing to a foreign API. Marxen builds AI for the campus, inside the campus.
Student records, faculty research, examination data, and admissions information are not just operational — they are the university's reputation. Generic cloud AI cannot run over them without raising questions about residency, IP, and academic integrity.
Marxen builds AI that lives on campus infrastructure or in a sovereign-hosted tenancy. Curriculum-aware, research-corpus-aware, examination-aware — and entirely under the institution's control.
Ten concrete workflows where Marxen has deployed — or can deploy — sovereign AI in education institutions.
AI that answers from the syllabus the student is actually taking — not from the open internet. Concept explanation, worked examples, practice problems generated from the unit map.
Semantic search across the university's theses, papers, and grant proposals. Researchers find prior work without leaving the library system.
Marksheets, transcripts, certificates, recommendation letters — verified, structured, and ranked against the programme's criteria. Admission committee reviews proposals, not paperwork.
Attendance, assessment, and library-use patterns flagged for the mentor — not for surveillance, for intervention. Privacy-preserving by design.
Question banks generated from the unit, taxonomy-tagged (Bloom's), and vetted for ambiguity. Faculty curates instead of authoring from blank.
Grading rubrics applied at scale with faculty review. Feedback drafted in the faculty's style. Course outcome and programme outcome mapping for accreditation.
Grounded in the PI's prior work and the funding agency's call. Faster proposal cycles, stronger first drafts.
Cross-checks against the institution's own corpus and external sources. Source-aware, with paragraph-level citation.
Lecture transcription, vernacular summaries, sign-language captioning — generated on the institution's own AI stack.
Resume building, mock interviews, company-specific preparation, and alumni-network search — for students, placement officers, and recruiters in one system.
Indexed against the institution's syllabi, programme outcomes, and assessment patterns. Not generic. Specific to your university.
Proctoring, item analysis, and question-paper QA — all on the institution's own servers. Exam data never leaves.
Vidhya is designed to give faculty time back, not to replace pedagogy. Every AI output is a draft for the faculty to edit.
Built for the National Education Policy's emphasis on multilingual learning, Indian Knowledge Systems, and outcome-based education.
Student records, examination data, and research outputs are treated as the university's data — not Marxen's, and not a shared model's. DPDP-aligned consent, role-based access for students, faculty, and admin, and full audit trails.
Compatible with NIRF, NAAC, and ABC-compliant academic-record interchange where required.
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