Multi-format answers
Candidates answer with typed text, uploaded documents, diagrams or files — speech-to-text lets them compose answers by speaking instead of typing.
AI descriptive evaluation
Eklavvya administers online descriptive tests and evaluates typed answers, uploaded documents and diagrams with AI that learns from your evaluators’ own marking pattern, backed by AI proctoring so the test stays secure end to end.
How evaluation works
Candidates answer with typed text, uploaded documents, diagrams or files — speech-to-text lets them compose answers by speaking instead of typing.
Evaluators grade the first 20–25% of a batch manually, establishing the marking pattern the AI will learn from.
The AI learns evaluation patterns, marking criteria and scoring logic, then applies them to the remaining 75–80% of answers.
AI compares answers against model answers, auto-detects keywords, analyzes structure and coherence, and reads for semantic meaning rather than keyword matching alone.
Partial marks are awarded for incomplete answers, with detailed feedback explaining the score rationale for every response.
Random sampling and moderation checks the AI’s grading before results are finalized, keeping a full audit trail.
Accuracy at scale
75%
Reduction in evaluation time
20–25%
Answers evaluators grade manually before AI learns the pattern
5M+
Evaluations completed
100%
Platform uptime
FAQ
No — it learns from them. Evaluators grade the first 20–25% of a batch manually, and the AI applies that same marking pattern to the rest, with random sampling and moderation as quality assurance.
The AI matches answers against model answers, detects keywords, analyzes structure and reads for semantic meaning — not just keyword matching — with random sampling and moderation as ongoing quality assurance.
Typed answers, uploaded documents, diagrams and files, with speech-to-text available for candidates who prefer to compose answers by speaking.
Yes — descriptive tests run with Eklavvya’s AI proctoring to eliminate cheating during the exam, in addition to the AI-driven evaluation afterward.
Institutions typically see a 75% reduction in evaluation time versus fully manual grading, while keeping a full audit trail of AI and evaluator scores.
Bring a sample answer set — we'll show you AI evaluation next to your own grading.