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Volume 8, Issue 2 (9-2025)
KCR 2025, 8(2): 17-34 |
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Ashraf Ganjouei M, Shabaninia E. Exploring the Relationship Between Artificial Intelligence Methods and Restoration Education Based on Learning Theories. KCR 2025; 8 (2) :17-34
URL: http://journal.richt.ir/kcr/article-1-296-en.html
URL: http://journal.richt.ir/kcr/article-1-296-en.html
Abstract: (2688 Views)
Artificial intelligence is rapidly transforming the landscape of education, yet its potential in restoration education has remained largely unexplored.
This research investigates how various artificial intelligence tools intersect with the teaching and learning of restoration at the university level.
Drawing on three major learning theories—experiential, constructivist, and connectivist—the study analyzes research from recent years to determine where AI aligns with or departs from effective pedagogical practices. The findings show that AI-driven technologies can significantly
strengthen concrete experience, active learning, social interaction, and prior knowledge integration which are associated with experiential and
constructivist learning, and also lifelong learning, technological facilitation, networking and communication, cognitive skills, and digital collaboration in connectivist. However, certain aspects, particularly those requiring in-depth contextual and textual understanding specific to heritage sites, present ongoing challenges for AI tools. These results provide valuable insights for educators and researchers seeking to implement AI solutions in restoration-related courses.
This research investigates how various artificial intelligence tools intersect with the teaching and learning of restoration at the university level.
Drawing on three major learning theories—experiential, constructivist, and connectivist—the study analyzes research from recent years to determine where AI aligns with or departs from effective pedagogical practices. The findings show that AI-driven technologies can significantly
strengthen concrete experience, active learning, social interaction, and prior knowledge integration which are associated with experiential and
constructivist learning, and also lifelong learning, technological facilitation, networking and communication, cognitive skills, and digital collaboration in connectivist. However, certain aspects, particularly those requiring in-depth contextual and textual understanding specific to heritage sites, present ongoing challenges for AI tools. These results provide valuable insights for educators and researchers seeking to implement AI solutions in restoration-related courses.
Type of Study: Research, Original, Regular |
Subject:
Application of modern sciences, technologies, equipment, materials and methods
Accepted: 2025/09/22 | Published: 2025/09/22
Accepted: 2025/09/22 | Published: 2025/09/22
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