AI驱动的实验教学评估框架，解决编程实验中抄袭、记录缺失等问题。
Artificial Intelligence-Powered Assessment Framework for Skill-Oriented Engineering Lab Education
- 基于1万+题库微调AI，为每位学生生成独特代码题
- 通过AI监考答辩与游戏化模拟提升参与度和掌握度
- 支持自动出题、难度自适应、防抄袭，适合工程教育
计算机科学实践实验常面临抄袭、缺乏实验记录、流程混乱、执行与评估不足、实践学习有限、学生参与度低及师生无法追踪进度等问题，导致毕业生动手能力薄弱。本文提出AsseslyAI框架，通过在线实验分配、为每位学生生成唯一实验题目、AI监考答辩及游戏化模拟器，提升参与度与概念掌握。不同于传统按主题生成题目，本框架在10,000+人工智能/机器学习实验题库上微调，实现动态生成多样化、代码丰富的评估题。验证显示题答相似度高，确保答案准确且无重复。该框架整合数据驱动出题、自适应难度、抗抄袭与评估于一体，超越传统自动评分工具，为培养真正具备实战能力的毕业生提供可扩展路径。
原文摘要 · Abstract (English)
Practical lab education in computer science often faces challenges such as plagiarism, lack of proper lab records, unstructured lab conduction, inadequate execution and assessment, limited practical learning, low student engagement, and absence of progress tracking for both students and faculties, resulting in graduates with insufficient hands-on skills. In this paper, we introduce AsseslyAI, which addresses these challenges through online lab allocation, a unique lab problem for each student, AI-proctored viva evaluations, and gamified simulators to enhance engagement and conceptual mastery. While existing platforms generate questions based on topics, our framework fine-tunes on a 10k+ question-answer dataset built from AI/ML lab questions to dynamically generate diverse, code-rich assessments. Validation metrics show high question-answer similarity, ensuring accurate answers and non-repetitive questions. By unifying dataset-driven question generation, adaptive difficulty, plagiarism resistance, and evaluation in a single pipeline, our framework advances beyond traditional automated grading tools and offers a scalable path to produce genuinely skilled graduates.
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