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.
Abstract (translated)
本文介绍了TrueGradeAI，这是一个基于人工智能的数字考试框架，旨在克服传统纸质评估方法的不足之处，包括过度使用纸张、物流复杂性、评分延迟和评卷人偏见等问题。该系统通过在安全平板电脑上捕捉触控笔输入来保留自然书写，并采用基于转换器的光学字符识别技术进行转录。评估过程则通过一个增强检索功能的工作流程完成，这一工作流程整合了教师提供的答案方案、缓存层以及外部参考文献，使大型语言模型能够依据具体证据给出评分并提供明确的理由说明。 与以往仅仅数字化考生回答的平板电脑考试系统不同，TrueGradeAI在解释性自动化、偏见缓解和可追溯评分记录方面取得了进步。通过结合书写保存和规模化的透明评估方式，该框架降低了环境成本，加速了反馈周期，并逐步建立了一个可重复使用的知识库，同时积极努力减少评分偏见并确保评价的公平性。
URL
https://arxiv.org/abs/2509.22516
