Competency, understanding and the role of explanation in AI-driven education.

McDermott, Roger; Brown, John N.A.; Daniels, Mats; Cajander, Åsa

Abstract

The increasing use of artificial intelligence within educational practice raises many important questions about the future role of pedagogical concepts long considered fundamental. One such example is the notion that understanding comes about through forms of explanation. Given the lack of transparency in current generative AI models, it is reasonable to ask what impact this will have on the need for explanations within teaching and what this means for its relationship to student understanding. Will the widespread use of generative AI technologies result in enhanced learning opportunities or does it mean that students will simply offload crucial parts of the learning process without any compensatory benefits? While research in Artificial Intelligence in Education (AIEd) continues to grow, there remains a significant gap in incorporating educational research perspectives. Most AIEd research is dominated by those with an engineering background, focusing heavily on technological design and development. This engineering-centric approach may often overlook the viewpoints of educational researchers and teachers, leading to a narrow understanding of AI’s role in educational settings. This paper takes a distinctly educational research perspective, examining how AI-driven tools may be shaped to enhance learning, understanding, and competency in contemporary education.

Citation

MCDERMOTT, R., BROWN, J.N.A., DANIELS, M. and CAJANDER, Å. 2025. Competency, understanding and the role of explanation in AI-driven education. In Smith, B.K. and BORGE, M. (eds.) Learning and collaboration technologies: proceedings of the 12th International conference Learning and collaboration technologies 2025 (LCT 2025) held as part of the 27th International conference on Human-computer interaction 2025 (HCII 2025), 22-27 June 2025, Gothenburg, Sweden. Lecture notes in computer science, 15807. Cham: Springer [online], part II, pages 304-323. Available from: https://doi.org/10.1007/978-3-031-93567-1_21

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 12th International conference of Learning and collaboration technologies
• Start Date: Jun 22, 2025
• End Date: Jun 27, 2025
• Acceptance Date: May 25, 2025
• Online Publication Date: May 25, 2025
• Publication Date: Dec 31, 2025
• Deposit Date: Jun 26, 2025
• Publicly Available Date: May 26, 2026
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: Part II
• Pages: 304-323
• Series Title: Lecture Notes in Computer Science
• Series Number: 15807
• ISBN: 9783031935664
• DOI: https://doi.org/10.1007/978-3-031-93567-1_21
• Keywords: Understanding; Explanation; Competency; Artificial intelligence in education (AIEd); Explainable AI (XAI)
• Public URL: https://rgu-repository.worktribe.com/output/2892557