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Journals / Library Archive and Museum Research Journal / 2026 / Cilt: 7 Sayı: 2

Preventive Conservation in Museums: A Theoretical Framework for Teaching Machines to Think Like Conservators

Müzelerde Önleyici Koruma: Makinelere Konservatör Gibi Düşünmeyi Öğretmek İçin Teorik Bir Çerçeve

Muhammed Diler(AYDIN ARKEOLOJİ MÜZESİ)0000-0003-0374-3662
Pages
185–202
Publication Date
2026-09-12
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Ultimately, the study argues that digital preservation infrastructures should not replace conservators but instead operate under principles of human oversight, ethical accountability, and sustainable knowledge production within national and international cultural heritage policies.

📌 3 Key Takeaways

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Objective & Hypothesis:This study examines how artificial intelligence and intelligent environmental monitoring systems can be integrated into museum conservation through ethical, explainable, and human-supervised decision-support frameworks.
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Key Findings & Data:The central argument proposes “teaching machines to think like conservators.” Rather than allowing autonomous systems to directly intervene in cultural heritage objects, the study emphasizes that AI should assist conservators by interpreting environmental conditions, deterioration processes, object sensitivity, and past conservation records within the ethical boundaries of conservation science.
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Conclusion & Impact:Ultimately, the study argues that digital preservation infrastructures should not replace conservators but instead operate under principles of human oversight, ethical accountability, and sustainable knowledge production within national and international cultural heritage policies.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Önleyici Koruma · Akıllı Çevresel İzleme · Müze Koruması

Abstract

This study examines how artificial intelligence and intelligent environmental monitoring systems can be integrated into museum conservation through ethical, explainable, and human-supervised decision-support frameworks. The central argument proposes “teaching machines to think like conservators.” Rather than allowing autonomous systems to directly intervene in cultural heritage objects, the study emphasizes that AI should assist conservators by interpreting environmental conditions, deterioration processes, object sensitivity, and past conservation records within the ethical boundaries of conservation science. The article theoretically positions conservator-supervised AI as a supportive tool in preventive conservation practices. Drawing on conceptual and thematic literature, the research combines discussions on IoT-based sensor networks, machine learning, digital twins, explainable artificial intelligence, archival and evidence theory, and cultural heritage ethics. Its original contribution is the redefinition of intelligent monitoring systems from simple data-collecting sensor infrastructures into material-sensitive, institutionally traceable, and explainable conservation reasoning systems. The proposed “Conservator-Like Reasoning Model” is structured around seven interconnected layers: deterioration mechanisms, environmental data acquisition, risk interpretation, material ontology, explainable algorithmic outputs, conservator approval, and institutional documentation and feedback. Ultimately, the study argues that digital preservation infrastructures should not replace conservators but instead operate under principles of human oversight, ethical accountability, and sustainable knowledge production within national and international cultural heritage policies.

Özet

Bu çalışma, müze konservasyonunda yapay zekâ ve akıllı çevresel izleme sistemlerinin nasıl etik, insan denetimli ve açıklanabilir bir karar destek mekanizmasına dönüştürülebileceğini tartışmaktadır. Araştırmanın temel yaklaşımı, makinelerin konservatörlerin uzmanlık süreçlerini taklit ederek “konservatör gibi düşünmesini” sağlamaktır. Ancak çalışma, yapay zekânın konservatörün yerini almasını değil; eserlerin hassasiyetini, bozulma süreçlerini, çevresel risklerini ve geçmiş müdahalelerini analiz ederken konservatör gözetiminde çalışan bir sistem kurulmasını önermektedir. Müze ortamlarında kullanılan IoT tabanlı sensör ağları, makine öğrenimi, dijital ikiz teknolojileri ve açıklanabilir yapay zekâ uygulamaları birlikte değerlendirilmiştir. Çalışmanın özgün katkısı, akıllı izleme sistemlerini yalnızca veri toplayan teknik araçlar olmaktan çıkarıp, malzeme duyarlılığına sahip, etik sınırlar içinde çalışan ve kurumsal olarak izlenebilir bir koruma akıl yürütme modeli olarak yeniden tanımlamasıdır. Önerilen “Konservatör Benzeri Akıl Yürütme Modeli” yedi temel katmandan oluşmaktadır: çevresel veri toplama, malzeme ontolojisi, bozulma mekanizması, risk yorumu, açıklanabilir algoritmik çıktı, konservatör onayı ve kurumsal kayıt ve geri bildirim. Sonuç olarak çalışma, dijital koruma altyapılarının insan denetimi, etik sorumluluk ve sürdürülebilir bilgi üretimi ilkelerine göre şekillendirilmesi gerektiğini savunmaktadır.

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Cite This Article

Diler, M. (2026). Preventive Conservation in Museums: A Theoretical Framework for Teaching Machines to Think Like Conservators. Library Archive and Museum Research Journal, Cilt: 7 Sayı: 2, 185–202. https://doi.org/10.59116/lamre.1958923

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