Batch processing needs more than speed
A bulk workflow must ensure that every page belongs to the correct student, every question is mapped correctly and failed pages are visible. Processing faster is not useful if records become mixed or incomplete.
EvalAI groups the workflow into upload, extraction, evaluation, review and export stages so faculty can see progress and identify exceptions.
Prepare a batch correctly
Use a consistent naming rule, confirm page order, remove blank duplicates and verify that student identifiers are readable. If a scanner combines several booklets into one PDF, define how boundaries will be detected or separated.
Create the question paper, model answers and marking rubric before starting the batch. Changing criteria halfway through can produce inconsistent results.
Review by exception and by sample
Low-confidence OCR, missing pages, uncertain question mapping and unusual answers should enter an exception queue. In addition, review a random sample of apparently successful papers to catch systematic errors.
Departments may choose full review for high-stakes work and exception-focused review for low-risk practice assessments. The policy should be documented before results are published.
Export and reconciliation
Before exporting, compare the expected student count with processed, failed and approved records. Check maximum marks, absent students, duplicate identifiers and incomplete questions.
A final Excel file should contain only reviewed data and should be reconciled with the institution’s official student roster.