Title: TrueGradeAI: Retrieval-Augmented and Bias-Resistant AI for Transparent and Explainable Digital Assessments
Primary area: generative models
Keywords: AI driven assessment, Digital examination framework, Handwriting preservation, Stylus input capture, Transformer based OCR, Optical character recognition, Retrieval augmented evaluation, Automated scoring, Bias mitigation, Auditable grading, Fairness in assessment, Paperless examination, Large language models in education

Abstract: This paper introduces TrueGradeAI, an AI-driven digital examination framework designed to overcome the shortcomings of traditional paper-based assessments, including excessive paper usage, logistical complexity, grading delays, and evaluator bias. The system preserves natural handwriting by capturing stylus input on secure tablets and applying transformer-based optical character recognition for transcription. Evaluation is conducted through a retrieval-augmented pipeline that integrates faculty solutions, cache layers, and external references, enabling a large language model to assign scores with explicit, evidence-linked reasoning. Unlike prior tablet-based exam systems that primarily digitize responses, TrueGradeAI advances the field by incorporating explainable automation, bias mitigation, and auditable grading trails. By uniting handwriting preservation with scalable and transparent evaluation, the framework reduces environmental costs, accelerates feedback cycles, and progressively builds a reusable knowledge base, while actively working to mitigate grading bias and ensure fairness in assessment.

TL;DR: TrueGrade is an AI-powered exam framework that preserves handwriting and employs cache-enhanced retrieval-augmented grading to deliver fast, bias resistant, and auditable evaluation.