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Journals / Turkish Journal of Engineering / 2026 / Volume: 10 Issue: 4

Reconstructing Lost Urban Memory with Generative AI: An Oral-History Prompt-Engineering Framework

Özlem Kevseroğlu(ABDULLAH GÜL ÜNİVERSİTESİ, MİMARLIK FAKÜLTESİ)0000-0003-1828-2256
İrem Yaşar(ABDULLAH GÜL ÜNİVERSİTESİ)0000-0003-3725-6010
Vildan Yıldırım(ABDULLAH GÜL ÜNİVERSİTESİ)0009-0000-3858-0176
Pages
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Publication Date
2026-10-16
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AI Summary & Key Takeaways

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

Tool G (Google Gemini) achieved the best overall ranking (M = 1.77).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Urban transformation processes not only destroy the physical fabric of historic neighbourhoods but also erode the collective memory and everyday life practices embedded within them.
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Key Findings & Data:Drawing on oral history interviews conducted as part of a TÜBİTAK-funded project, narrative data from 5 former residents were transformed into structured seven-category prompts and fed into Google Gemini- Nano Banana 2 pro, OpenAI GPT-Image-1, and XAI Grok.
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Conclusion & Impact:Tool G (Google Gemini) achieved the best overall ranking (M = 1.77).
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Urban Collective Memory · Generative AI · Digital Heritage

Abstract

Urban transformation processes not only destroy the physical fabric of historic neighbourhoods but also erode the collective memory and everyday life practices embedded within them. This study addresses this spatial memory loss by employing generative AI to visually reconstruct the lost atmospheres of Cumhuriyet Neighbourhood within the outer walls of Kayseri Castle, Turkey. Drawing on oral history interviews conducted as part of a TÜBİTAK-funded project, narrative data from 5 former residents were transformed into structured seven-category prompts and fed into Google Gemini- Nano Banana 2 pro, OpenAI GPT-Image-1, and XAI Grok. A 30-member residents panel evaluated the outputs. Tool G (Google Gemini) achieved the best overall ranking (M = 1.77). The findings confirm that human curatorial oversight and iterative community feedback remain essential for culturally authentic AI-generated heritage visualisations.

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Cite This Article

Kevseroğlu, Ö., Yaşar, İ., Yıldırım, V. (2026). Reconstructing Lost Urban Memory with Generative AI: An Oral-History Prompt-Engineering Framework. Turkish Journal of Engineering, Volume: 10 Issue: 4. https://doi.org/10.31127/tuje.1967989

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