Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges

Computer Science > Artificial Intelligence [Submitted on 1 Apr 2026] Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges Karina Cortinas-Lorenzo, Gavin Doherty As Artificial Intelligence (AI) systems continue to grow in size and complexity, so does the difficulty of the quest for AI transparency. In a world of large models and complex AI systems, why do we explain AI and what should we explain? While explanations serve multiple functions, in the face of complexity humans have used and continue to use explanations to foster learning. In this position paper, we discuss how learning theories can be infused in the XAI lifecycle, as well as the key opportunities and challenges when adopting a learner-centered approach to assess, design and evaluate AI explanations. Building on past work, we argue that a learner-centered approach to Explainable AI (XAI) can enhance human agency and ease XAI risks mitigation, helping evolve the practice of human-centered XAI. Comments: Accepted at the CHI 2023 Human-Centered XAI workshop Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) Cite as: arXiv:2604.19788 [cs.AI]   (or arXiv:2604.19788v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.19788 Focus to learn more Submission history From: Karina Cortinas Lorenzo [view email] [v1] Wed, 1 Apr 2026 06:25:17 UTC (43 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)