On August 17, 2026, SASTRA Deemed University held its Ruby and Silver Jubilee Valedictory Celebrations in Thanjavur. Vice President C. P. Radhakrishnan delivered the address, with Tamil Nadu Governor R. V. Arlekar and the state’s Higher Education Minister also present. Among the six institutional initiatives launched that evening was SASTRA.AI, a full deployment of AI-driven academic infrastructure across SASTRA’s Kumbakonam and Thanjavur campuses.
It was also the day Edwisely received the grant notice for its 7AI patent from the Indian Patent Office, a coincidence that aligned with what we had been building over the previous year.
SASTRA.AI is the public name for something that had been quietly taking shape through months of faculty development, system configuration, and curriculum mapping. Every assessment, every course outcome, and every piece of student feedback now flows into one place, readable in real time by faculty, departments, and institutional leadership at both campuses simultaneously. But what did it actually take to build that, and what does it mean for how SASTRA teaches?
What an AI Campus Should Be
India’s higher education system requires institutions to demonstrate outcome-based education, with evidence available for NAAC and NIRF review cycles. NEP 2020 pushes further in the same direction: continuous assessment, multimodal evaluation, and feedback that reaches students while it can still change something. Most institutions are trying to meet these requirements through tools built for a different job.
A Learning Management System was built to deliver content and record submissions. It tracks what was submitted. It was not built to assist a Head of Department in getting specific information on whether COs have been met.
Intelligent Learning Infrastructure (ILI) fills that gap. It connects assessment data to outcomes in real time, across departments, across campuses, without anyone manually assembling the picture. The difference between CO attainment data available mid-semester and CO attainment data compiled the week before a NAAC visit is the difference between acting on information and reporting it.
How the SASTRA.AI Deployment Actually Worked
The foundation was practical. SASTRA began by sharing syllabi and course material for the programmes selected for the pilot. Edwisely’s technology and academics teams worked together to customise each course within the ILI framework — mapping content to course outcomes, defining rubrics, and structuring the assessment pipeline to reflect how each department actually taught.
Before any assessment ran, faculty worked through the TEATAR framework — Edwisely’s six-stage faculty intelligence cycle. The Teach stage gave faculty tools that most had not used before at this depth: AI-assisted lesson planners that structured sessions around defined learning outcomes, lesson hooks designed to anchor student attention at the start of a topic, and a PPT engine that built presentation content around the mapped curriculum. These were not add-ons to the deployment. They were the reason faculty understood the logic of the system before the first assessment went out.
What TEATAR gave SASTRA faculty at each stage:
- Teach — Lesson planning, lesson hooks, AI-assisted presentations, course file automation
- Engage — Live interactive teaching, live surveys, discussion forums, real-time participation tools
- Assess — Objective, live, video, case study, coding, adaptive, and curiosity assessments
- Track — CO attainment tracking, activity reports, gradebook, attendance visibility
- Analyse — Section analytics, subject analytics, SWOC reports, cohort performance trends
- Remediate — At-risk identification, peer mentoring, personalised remediation tracks
Customer success managers were on campus throughout, working directly with faculty to guide them through features, usage, and any issues — not remotely, and not through documentation. The deployment went live in ten days, mid-semester, adapting to an active academic calendar rather than waiting for a clean break.
CO attainment mapping, which for most universities is a semester-end manual exercise, was automated from the start. After every assessment, results mapped directly to the course outcomes those questions were designed to measure. A faculty member teaching Professional Ethics — a two-credit elective — could run case-based assessments across a full semester and watch attainment update in real time, without a spreadsheet or a separate reporting system.
The COEPE dashboard — the Centre of Excellence for Personalised Education — brought both campuses into a single management view: participation trends, performance patterns, and outcome attainment across departments, updated as assessments happened. For SASTRA’s leadership, mid-semester reviews stopped being about assembling data from the past and became about reading what was happening and deciding what to do.
Where the Innovation Showed Up
The assessment formats SASTRA ran through ILI were not limited to what a conventional LMS supports. Video assessments, where students record themselves explaining concepts and receive AI-generated feedback specific to their own articulation and accuracy, were administered with questions randomised per student to prevent answer-sharing. Case-based assessments, where students work through real scenarios mapped to course outcomes and receive rubric-anchored feedback on their own reasoning, ran across a range of courses — including electives where that level of rigour had previously been impractical to deliver.
Here is a comparison of what workflows looked like across both campuses:
| Activities | Before ILI | After ILI |
| Assessment styles | 1-2 | 8+ |
| Analytics View | Per Campus, Manual | Cross-campus, real-time |
| Student Feedback | Aggregate score-based, general | Rubric-based, personalized |
| Case study assessments | Manual setup, scenario research required, faculty fully involved | Automated according to rubrics, faculty is verifying authority |
| Faculty admin load | High per cycle | Near zero after setup |
Assessment Formats SASTRA Tested — and What Happened
One of the clearest indicators of what ILI enabled was the range of assessment formats SASTRA ran in a single semester. Beyond conventional objective tests, faculty used video assessments — where students record themselves explaining concepts and receive AI-generated feedback specific to their own articulation and accuracy, with questions randomised per student — alongside case-based scenarios, live in-class evaluation, and adaptive formats that most institutions have considered but never run at this scale. Across both campuses in one semester, the activity broke down as follows:
| Assessment Format | Tests Conducted | Avg. Participation | Avg. Performance |
| Objective | 895 | 55.9% | 62.9% |
| Live | 260 | 93.7% | 62.0% |
| Case study | 83 | 72.3% | 68.9% |
| Video | 62 | 92.8% | 50.1% |
Combined data, Kumbakonam and Thanjavur campuses, second semester.
The case-based performance figure is the one worth pausing on. 68.9% average performance on the most demanding format — where students must apply course concepts to a real scenario and reason through it without the option of recalling a definition — is the highest of any format in the semester. That result comes directly from the feedback loop: every submission returned rubric-mapped, individual feedback on that student’s own reasoning, not an aggregate score.
The most pointed demonstration of this came from Professional Ethics, a two-credit management elective. Under ILI, it ran 254 assessments across three formats in a single semester — the same rubric-based evaluation structure, the same CO attainment tracking, the same individual feedback loop as any core engineering course on the same platform. A student sitting a case-based examination in Professional Ethics received the same structured, outcome-mapped assessment experience as a student in a mandatory four-credit technical subject. ILI does not scale down the quality of an examination based on a course’s credit weight. The infrastructure handles both the same way, and the results reflect that.
What Comes After August 17
SASTRA.AI is live, and the data behind the name is measured, not projected. Faculty time has shifted away from administrative grading and toward reviewing outcomes. CO attainment is tracked mid-semester. Management across both campuses is working from current information rather than end-of-term reports.
For other institutions in Indian higher education, the useful question is not whether it worked for SASTRA. It is what it took: faculty willing to engage with a new framework, leadership with a specific direction, and the decision to go live mid-semester rather than waiting for a perfect moment. SASTRA brought all three. The infrastructure was already there.
Frequently Asked Questions
What is SASTRA.AI? SASTRA.AI is an AI-powered academic infrastructure deployed across SASTRA Deemed University’s Kumbakonam and Thanjavur campuses. Launched on August 17, 2026, it is built on Edwisely’s Intelligent Learning Infrastructure (ILI) and covers assessment, CO attainment tracking, faculty teaching support, and institutional analytics through the COEPE dashboard.
What is Intelligent Learning Infrastructure (ILI)? Intelligent Learning Infrastructure (ILI) is an academic intelligence layer built for higher education institutions. It sits above existing ERP and LMS systems, connecting assessment data to course outcomes in real time. Unlike an LMS, which records submissions, ILI tracks whether learning outcomes are being met — mid-semester, across departments, at the student level.
What is the TEATAR framework? TEATAR is Edwisely’s faculty intelligence framework, structured across six stages: Teach, Engage, Assess, Track, Analyse, and Remediate. It covers the full teaching cycle, from AI-assisted lesson planning and lesson hooks through to assessment design, CO attainment tracking, performance analytics, and targeted student remediation.
How is ILI different from an LMS? An LMS manages content delivery and records what students submitted. ILI tracks whether learning outcomes are being attained, returns personalised feedback to students after every assessment, maps results to CO/PO frameworks automatically, and gives institutional leadership a live cross-campus view of academic performance. The two systems serve different functions; ILI is designed to sit above the LMS, not replace it.
What is CO attainment mapping? CO attainment mapping is the process of tracking how well students are meeting defined course outcomes across assessments. Under ILI, this happens automatically after every assessment — results are mapped to the relevant course outcomes and made visible to faculty and management in real time, rather than being compiled manually at semester end for NAAC or NIRF reporting.
What is COEPE? COEPE (Centre of Excellence for Personalised Education) is ILI’s institutional analytics layer. It provides dashboards at the student, section, department, and college level, configurable to each institution’s requirements. For multi-campus institutions, COEPE gives leadership a unified view across all campuses simultaneously.
How long does ILI take to deploy? The SASTRA deployment went live across two campuses in ten days, mid-semester. The timeline depends on the scope of the rollout, but ILI is designed to adapt to an active academic calendar rather than requiring a clean break between terms.
Which assessment formats does ILI support? ILI supports objective assessments, live assessments, video assessments, case-based assessments, subjective assessments, coding assessments, adaptive assessments, curiosity assessments, assignments, and project-based assessments.
How does ILI support NAAC accreditation? ILI automates CO and PO attainment mapping, making outcome evidence continuously current rather than compiled under deadline. Faculty and institutional leadership can access attainment data at any point in the academic year, which means NAAC documentation reflects ongoing academic activity rather than a retrospective assembly of evidence.
What is outcome-based education (OBE) in India? Outcome-based education (OBE) is a framework requiring institutions to define course outcomes, map assessments to those outcomes, and demonstrate attainment for NAAC and NBA accreditation. NEP 2020 reinforces OBE as the standard for Indian higher education, requiring continuous assessment and multi-modal evaluation rather than reliance on end-of-semester examinations alone.
