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Journals / Uluslararası Sosyal Bilimler ve Eğitim Dergisi / 2026 / Cilt: 8 Sayı: 15

Constructing the algorithmic gaze: viewer positioning in AI-generated cinematic images

Seda Aktaş(ISTANBUL ESENYURT UNIVERSITY)0000-0002-1735-699X
Pages
517–544
Publication Date
2026-10-15
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AI Summary & Key Takeaways

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

By bringing together film theory, digital visual culture, and generative AI studies, the study aims to establish a theoretical bridge between cinematic gaze and algorithmic visuality..

📌 3 Key Takeaways

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Objective & Hypothesis:This study examines how cinematic gaze is reconstructed within generative artificial intelligence systems.
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Key Findings & Data:To investigate this process, the same cinematic scene was regenerated through differently structured prompts using Midjourney V6, Leonardo AI, and Stable Diffusion based systems.
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Conclusion & Impact:By bringing together film theory, digital visual culture, and generative AI studies, the study aims to establish a theoretical bridge between cinematic gaze and algorithmic visuality..
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:algortimik bakış · üretken yapay zeka · sinematik bakış

Abstract

This study examines how cinematic gaze is reconstructed within generative artificial intelligence systems. It argues that AI-supported image generation processes do not merely produce visual content, but also establish algorithmic regimes of spectatorship that organize the viewer’s position within the scene. To investigate this process, the same cinematic scene was regenerated through differently structured prompts using Midjourney V6, Leonardo AI, and Stable Diffusion based systems. The research combines a practice-based approach with comparative visual analysis. The generated images were analysed through three main categories: viewer-addressing gaze, viewer-aligned gaze, and external observer positioning. Particular attention was given to facial orientation, gaze direction, camera alignment, and spectator positioning. The findings demonstrate that generative AI systems tend to privilege frontal, highly readable, and viewer centred compositions. The study also reveals that different platforms respond to identical prompts through distinct visual organizations, suggesting that image generation is shaped not only by user input but also by model-specific aesthetic tendencies and learned visual biases. In several cases, the systems maintained dominant visual patterns even when prompts explicitly attempted to redirect spectator positioning, indicating forms of prompt resistance and algorithmic preference. The article argues that AI generated imagery should be understood not simply as aesthetic output, but as a post-cinematic visual regime that reorganizes perception, spectatorship, and visual orientation through algorithmic processes. By bringing together film theory, digital visual culture, and generative AI studies, the study aims to establish a theoretical bridge between cinematic gaze and algorithmic visuality..

Özet

Bu çalışma, üretken yapay zekâ sistemlerinde sinematik bakışın nasıl yeniden kurulduğunu incelemektedir. Araştırma, yapay zekâ destekli görsel üretim süreçlerinin yalnızca görsel içerik üretmediğini, aynı zamanda izleyicinin sahne içerisindeki konumunu organize eden algoritmik bakış rejimleri oluşturduğunu ileri sürmektedir. Çalışma kapsamında aynı sinematik sahne, farklı düzeylerde yapılandırılmış girdiler aracılığıyla Midjourney, Leonardo AI ve Stable Diffusion tabanlı sistemlerde yeniden üretilmiştir. Araştırma, uygulama temelli bir yöntemi karşılaştırmalı görsel analiz ile birleştirmektedir. Üretilen görseller; izleyiciye yönelen bakış, izleyiciyle hizalanan bakış ve dış gözlemci konumu olmak üzere üç temel kategori altında analiz edilmiştir. Analiz sürecinde özellikle yüz yönelimi, bakış doğrultusu, kamera hizalanması ve izleyici konumlandırmasına odaklanılmıştır. Bulgular, üretken yapay zekâ sistemlerinin güçlü biçimde frontal, okunabilir ve izleyici-merkezli kompozisyonlar üretme eğilimi taşıdığını göstermektedir. Ayrıca farklı platformların aynı girdilere farklı görsel organizasyonlarla yanıt verdiği gözlemlenmiştir. Bu durum, görsel üretim sürecinin yalnızca kullanıcı girdileriyle değil, aynı zamanda modele özgü estetik eğilimler ve öğrenilmiş görsel önyargılar tarafından da şekillendiğini ortaya koymaktadır. Bazı durumlarda sistemlerin, girdiler izleyici konumlandırmasını açık biçimde değiştirmeye çalışsa bile baskın görsel örüntülerini sürdürdüğü görülmüş; bu durum girdi direnci ve algoritmik tercih biçimleriyle ilişkilendirilmiştir. Çalışma, yapay zekâ görsellerinin yalnızca estetik çıktılar değil, aynı zamanda algıyı, izleyici deneyimini ve görsel yönelimi algoritmik süreçler aracılığıyla yeniden organize eden post-sinematik temsil alanları olduğunu ileri sürmektedir. Bu yönüyle araştırma, sinematik bakış kuramı ile algoritmik görsellik tartışmaları arasında kuramsal bir köprü kurmayı amaçlamaktadır.

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Cite This Article

Aktaş, S. (2026). Constructing the algorithmic gaze: viewer positioning in AI-generated cinematic images. Uluslararası Sosyal Bilimler ve Eğitim Dergisi, Cilt: 8 Sayı: 15, 517–544. https://doi.org/10.67567/usbed.1958517

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