As computational systems supported by artificial intelligence (AI) techniques continue to play an increasingly pivotal role in making high-stakes recommendations and decisions across various domains, the demand for explainable AI (XAI) has grown significantly, extending its impact into cognitive learning research. Providing explanations for novel concepts is recognised as a fundamental aid in the learning process, particularly when addressing challenges stemming from knowledge deficiencies and skill application. Addressing these difficulties involves timely explanations and guidance throughout the learning process, prompting the interest of AI experts in developing explainer models. In this paper, we introduce an intelligent system (CL-XAI) for Cognitive Learning which is supported by XAI, focusing on two key research objectives: exploring how human learners comprehend the internal mechanisms of AI models using XAI tools and evaluating the effectiveness of such tools through human feedback. The use of CL-XAI is illustrated with a game-inspired virtual use case where learners tackle combinatorial problems to enhance problem-solving skills and deepen their understanding of complex concepts, highlighting the potential for transformative advances in cognitive learning and co-learning.

翻译：随着基于人工智能技术的计算系统在多个领域的高风险推荐与决策中发挥越来越关键的作用，对可解释人工智能的需求显著增长，其影响已延伸至认知学习研究领域。为新颖概念提供解释被视为学习过程中的基础性辅助手段，尤其是在应对知识不足和技能应用带来的挑战时。解决这些困难需要在学习过程中提供及时的阐释和引导，这促使人工智能专家致力于开发解释器模型。本文提出了一种由可解释人工智能支持的认知学习智能系统（CL-XAI），聚焦两大核心研究目标：探究人类学习者如何利用可解释人工智能工具理解人工智能模型的内部机制，以及通过人类反馈评估此类工具的有效性。通过一个游戏化虚拟用例展示了CL-XAI的应用——学习者在其中处理组合问题以增强问题解决能力并深化对复杂概念的理解，突显了其在认知学习与协同学习领域实现突破性变革的潜力。