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.

我们介绍了一个使用可解释的人工智能（XAI）的智能系统（CL-XAI）用于认知学习的研究，其两个主要目标是探索人类学习者如何理解使用 XAI 工具的人工智能模型的内部机制，并通过人类反馈评估这些工具的有效性。使用 CL-XAI 在一个基于游戏的虚拟案例中说明了其应用，学习者通过解决组合问题来增强解决问题的能力和加深对复杂概念的理解，突出了认知学习和协同学习中潜在的转变性进展。

拓展认知学习的增强性与可解释性人工智能

Toward enriched Cognitive Learning with XAI

Many machine learning frameworks have been proposed and used in wireless
communications for realizing diverse goals. However, their incapability of
adapting to the dynamic wireless environment and tasks and of self-learning
limit their extensive applications and achievable performance. Inspired by the
great flexibility and adaptation of primate behaviors due to the brain
cognitive mechanism, a unified cognitive learning (CL) framework is proposed
for the dynamic wireless environment and tasks. The mathematical framework for
our proposed CL is established. Using the public and authoritative dataset, we
demonstrate that our proposed CL framework has three advantages, namely, the
capability of adapting to the dynamic environment and tasks, the self-learning
capability and the capability of 'good money driving out bad money' by taking
modulation recognition as an example. The proposed CL framework can enrich the
current learning frameworks and widen the applications.

提出了一种统一的认知学习（CL）框架，它受灵长类动物大脑认知机制的启发，具有动态适应环境和任务的能力、自我学习能力，以及优秀驱逐劣质能力；并且经过了基于公开权威数据集的验证，可以丰富当前的学习框架并扩大应用范围。