Designed around institutional evaluation
College evaluation is not a single-user task. It may involve paper collection, scanning, answer-key preparation, allocation to evaluators, moderation, result export and rechecking. EvalAI organizes the AI-assisted steps around this broader academic process.
Departments can define question-level criteria and use a common review process so that routine calculations and exports are consistent while academic decisions remain with authorized faculty.
Start with a controlled pilot
Choose one subject, one question type and a representative set of answer sheets. Record manual marks independently, then compare OCR quality, question mapping, suggested marks, faculty correction rate and total time.
A pilot should include difficult cases—not only clean handwriting. Use the findings to decide where automation is useful, where rules need adjustment and where evaluation should remain fully manual.
Governance and accountability
Colleges should decide who can upload papers, edit rubrics, override marks and publish results. Keep an audit trail for changes and document how student data is stored and deleted.
For high-stakes examinations, require human approval and follow the institution’s examination policy. AI-generated marks should be treated as recommendations until verified by an authorized evaluator.
Reporting for departments
Approved question-wise data can support marksheets, class summaries and topic-level analysis. These outputs can reduce repeated spreadsheet entry and help faculty identify questions that need reteaching.
Analytics should be based only on verified results and interpreted alongside syllabus coverage, paper difficulty and assessment design.