Go to DBLP homepageCL-XAI: Toward Enriched Cognitive Learning with Explainable Artificial Intelligence
Abstract: Artificial Intelligence (AI) is transforming education by providing personalized learning paths that cater to individual needs. Explainable AI (XAI) plays a vital role in assisting learners with limited prior knowledge and skills, offering transparency by elucidating how AI systems reach conclusions. In a co-learning environment, where learners engage collaboratively, the role of XAI becomes even more significant. This transparency not only fosters trust in AI-driven learning but also empowers co-learners to actively participate in and enhance their cognitive learning journey. 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 supported by XAI, focusing on two key research objectives: (i) exploring how human learners comprehend the internal mechanisms of AI models using XAI tools; and (ii) 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 deepened their understanding of complex concepts. The analysis of a pilot study of 21 participants indicates improved learning outcomes with explanations, showing a positive correlation with the complexity of tasks. Notably, over 60% of the participants expressed satisfaction, recognizing explanations as valuable aids in achieving learning targets. This paper is a pathway highlighting the potential for transformative advances in cognitive learning and co-learning.
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