Cognitive Diagnosis (CD) aims to evaluate students' cognitive states based on their interaction data, enabling downstream applications such as exercise recommendation and personalized learning guidance. However, existing methods often struggle with accuracy drops in cross-domain cognitive diagnosis (CDCD), a practical yet challenging task. While some efforts have explored exercise-aspect CDCD, such as crosssubject scenarios, they fail to address the broader dual-aspect nature of CDCD, encompassing both student- and exerciseaspect variations. This diversity creates significant challenges in developing a scenario-agnostic framework. To address these gaps, we propose PromptCD, a simple yet effective framework that leverages soft prompt transfer for cognitive diagnosis. PromptCD is designed to adapt seamlessly across diverse CDCD scenarios, introducing PromptCD-S for student-aspect CDCD and PromptCD-E for exercise-aspect CDCD. Extensive experiments on real-world datasets demonstrate the robustness and effectiveness of PromptCD, consistently achieving superior performance across various CDCD scenarios. Our work offers a unified and generalizable approach to CDCD, advancing both theoretical and practical understanding in this critical domain. The implementation of our framework is publicly available at https://github.com/Publisher-PromptCD/PromptCD.

翻译：认知诊断（CD）旨在基于学生的交互数据评估其认知状态，从而支持诸如习题推荐和个性化学习指导等下游应用。然而，现有方法在跨领域认知诊断（CDCD）这一实际且具有挑战性的任务中，常常面临准确性下降的问题。尽管已有研究探索了习题视角的CDCD（如跨学科场景），但它们未能涵盖CDCD更广泛的双视角本质，即同时涉及学生视角和习题视角的变异。这种多样性为开发场景无关的框架带来了重大挑战。为弥补这些不足，我们提出了PromptCD，一个简单而有效的框架，利用软提示迁移进行认知诊断。PromptCD旨在无缝适应多样化的CDCD场景，其中PromptCD-S用于学生视角的CDCD，PromptCD-E用于习题视角的CDCD。在真实数据集上的大量实验证明了PromptCD的鲁棒性和有效性，其在各种CDCD场景中均能持续取得优越性能。本研究为CDCD提供了一个统一且可泛化的方法，推动了这一关键领域在理论和实践上的理解。我们的框架实现已公开于https://github.com/Publisher-PromptCD/PromptCD。