标题： 生成性认知诊断 显示英文标题

标题： Generative Cognitive Diagnosis

摘要： 认知诊断（CD）模型通过分析学习者在诊断测试上的响应模式来推断其潜在的认知状态，是教育评估和评价中一种关键的机器学习技术。 传统的认知诊断模型通常遵循一种归纳预测范式，该范式优化参数以适应响应分数并提取学习者的能力。 这些方法存在显著的局限性，因为它们无法在不进行计算成本高昂的重新训练的情况下对新学习者进行即时诊断，并且产生的诊断结果可靠性有限。 在本研究中，我们引入了一种新颖的生成诊断范式，从根本上将CD从预测建模转变为生成建模，使得无需参数重新优化即可进行认知状态的归纳推理。 我们提出了这种范式的两种简单而有效的实例：生成项目反应理论（G-IRT）和生成神经认知诊断模型（G-NCDM），它们在传统方法上实现了出色的性能提升。 生成方法通过一个设计良好的生成过程，结合可识别性和单调性条件，将认知状态推理与响应预测分离。 在真实世界数据集上的大量实验表明，我们的方法在解决可扩展性和可靠性挑战方面非常有效，特别是在$\times 100$诊断新学习者的速度提升方面。 我们的框架为人工智能中的认知诊断应用开辟了新的途径，特别是在智能模型评估和智能教育系统方面。 代码可在 https://github.com/CSLiJT/Generative-CD.git 获取。 显示英文摘要

摘要： Cognitive diagnosis (CD) models latent cognitive states of human learners by analyzing their response patterns on diagnostic tests, serving as a crucial machine learning technique for educational assessment and evaluation. Traditional cognitive diagnosis models typically follow a transductive prediction paradigm that optimizes parameters to fit response scores and extract learner abilities. These approaches face significant limitations as they cannot perform instant diagnosis for new learners without computationally expensive retraining and produce diagnostic outputs with limited reliability. In this study, we introduces a novel generative diagnosis paradigm that fundamentally shifts CD from predictive to generative modeling, enabling inductive inference of cognitive states without parameter re-optimization. We propose two simple yet effective instantiations of this paradigm: Generative Item Response Theory (G-IRT) and Generative Neural Cognitive Diagnosis Model (G-NCDM), which achieve excellent performance improvements over traditional methods. The generative approach disentangles cognitive state inference from response prediction through a well-designed generation process that incorporates identifiability and monotonicity conditions. Extensive experiments on real-world datasets demonstrate the effectiveness of our methodology in addressing scalability and reliability challenges, especially $\times 100$ speedup for the diagnosis of new learners. Our framework opens new avenues for cognitive diagnosis applications in artificial intelligence, particularly for intelligent model evaluation and intelligent education systems. The code is available at https://github.com/CSLiJT/Generative-CD.git.