用可解释的AI系统实现手写答题的透明评分，减少偏见和延迟。
TrueGradeAI: Retrieval-Augmented and Bias-Resistant AI for Transparent and Explainable Digital Assessments
- 通过手写识别与检索增强的评分流程，实现自动打分。
- 评分过程可追溯，支持证据链推理，提升公平性。
- 适合教育机构用于大规模、公正、环保的数字考试。
本文提出TrueGradeAI，一种基于AI的数字考试框架，旨在克服传统纸笔考试的弊端，如过度用纸、物流复杂、评分延迟及评卷偏见。系统通过安全平板捕捉手写输入，并利用基于Transformer的光学字符识别技术进行转录。评分采用检索增强的流水线，整合教师参考答案、缓存层和外部资料，使大语言模型能够基于明确证据链生成可解释的评分。与以往仅实现答题数字化的系统不同，TrueGradeAI在可解释自动化、偏见缓解和评分可审计方面实现突破。该框架在保留自然书写的同时，实现了可扩展、透明的评估，降低环境成本，加快反馈周期，并逐步构建可复用的知识库，主动减少评分偏见，保障评估公平性。
原文摘要 · Abstract (English)
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.
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