Core Features · Features & Capabilities
AI exam auto gradingBuilt for high volume institutes.
The challenge
High-volume institutes face significant delays and inter-rater variability when manually grading thousands of handwritten or digital mock exams, leading to inconsistent feedback cycles and teacher burnout.
How AI Professor™ helps
The platform utilizes advanced RAG (Retrieval-Augmented Generation) on prescribed textbooks and official marking schemes to ensure grading precision. It provides granular feedback through syllabus heatmaps, identifying specific learning gaps across thousands of students while maintaining a centralized dashboard for institutional performance monitoring.
Why high volume institutes pick AI Professor™
Most "ai exam auto grading" pitches are a thin wrapper around a generic LLM. AI Professor™ is the opposite: deeply integrated with features & capabilities workflows, with branding, rosters, syllabus, exams and admin controls every decision-maker recognises.
What's in the box
A branded portal, an AI Teacher persona for every subject, 24×7 doubt-clearance, auto-graded worksheets and exams, handwriting evaluation, parent dashboards in regional languages, and a real admin console — usage, safety incidents, faculty workload.
Pricing & deployment
from $0.99 per student per month. A 14-day pilot covers branding, syllabus loading, teacher onboarding and parent communication. Full rollout typically takes 4–6 weeks.
Compliance & safety
Syllabus-aware, exam-pattern aware, with a mandatory safety layer and per-institution admin controls.
Frequently asked questions
How does the system handle subjective or long-form answers in an Indian board context?
The system uses Natural Language Processing to compare student responses against the specific keywords, concepts, and structural requirements defined in NCERT or state board marking schemes. It evaluates semantic meaning rather than just exact word matching, ensuring fair credit for diverse phrasing.
Can the grading engine detect and penalize AI-generated content or plagiarism?
Yes, the grading framework includes integrated linguistic analysis tools that identify patterns indicative of non-human generation or verbatim duplication from online sources. This ensures the integrity of the evaluation process for high-stakes internal assessments and mock examinations.
What measures are in place to prevent hallucination in grade awarding?
AI Professor employs a grounded RAG architecture that restricts the grading logic to the provided textbook data and official rubrics. By locking the model's knowledge base to specific syllabus documents, the platform prevents the introduction of external, irrelevant, or inaccurate information during the evaluation process.
Is the auto-grading system compatible with handwritten answer sheets?
High-volume institutes can digitize handwritten papers via OCR (Optical Character Recognition) processing. Once digitized, the engine parses the text and applies the same rigorous rubric-based evaluation as it does for digital submissions, allowing for a seamless transition from traditional paper-based testing.