Toward enriched Cognitive Learning with XAI

Explainability improves learning outcomes: The paper demonstrates that when AI systems provide explanations for their decisions and reasoning, learners better understand both the AI's internal mechanisms and the underlying concepts being taught—suggesting that transparency in AI can serve as a pedagogical tool, not just a compliance requirement.

XAI tools help bridge knowledge gaps: The research shows that timely explanations and guidance from explainable AI systems address learning challenges related to knowledge deficiencies and skill application, indicating that XAI can be particularly valuable in educational contexts where learners need to understand the "why" behind concepts.

Human feedback validates AI explanations: The CL-XAI system evaluates explainer models through human feedback, emphasizing that responsible AI development requires testing whether explanations actually make sense to real people—not just whether the AI can generate them.

Interactive learning enhances AI comprehension: Using a game-inspired virtual environment, the paper shows that learners engaging with combinatorial problems while receiving AI explanations develop deeper understanding of complex concepts, suggesting that interactive, explanation-rich systems can transform how people learn about both subject matter and AI itself.

Co-learning between humans and AI is possible: The research highlights potential for "co-learning," where humans and AI systems learn from each other, positioning explainable AI not as a one-way tool for human understanding but as a collaborative framework for mutual improvement.