How We Built Our Auto-Grader with Gemini and xAPI Standards

Manual grading consumes faculty hours every semester. Our auto-grader leverages Google Gemini for deep language understanding and xAPI for standards compliance.

The Architecture

1. Input Processing – Gemini analyzes text submissions, identifies key concepts, and compares them against model answers.
2. Scoring Engine – Machine-learning models assign a score while generating human-readable explanations.
3. xAPI Integration – Results are stored as Experience API statements for universal tracking.
4. Instructor Feedback Loop – Teachers can review, override, or enrich comments, improving future accuracy.

Why xAPI Matters

Unlike SCORM, xAPI supports modern learning records — online, offline, or mobile. It ensures interoperability across LMS platforms.

Accuracy and Fairness

We trained our model on anonymized datasets and implemented bias-checking pipelines. The system explains why each score is given, promoting transparency.

The result? Faster grading, richer feedback, and data you can trust.