Design of an iOS Mobile Application for the Automated Evaluation of Open-Ended Exams via Artificial Intelligence and Image Processing

Year 2025, Volume: 8 Issue: 1, 1 - 35, 31.05.2025

Nazmi Ekin Vural

https://doi.org/10.53047/josse.1691312

Abstract

Evaluating open-ended exams presents significant challenges in terms of time management and consistency in educational processes. This study aims to develop an iOS-based mobile application, “Exam Reader” to streamline the evaluation of handwritten open-ended exam responses by integrating visual recognition and language analysis tools, enabling educators to deliver timely and fair assessments. Developed using the Swift programming language, the application relies on two core technologies. First, handwritten student responses are converted into digital text using Optical Character Recognition (OCR) via the Google Cloud Vision API. These texts are then analyzed for clarity and coherence using the OpenAI API and GPT-4o model, ensuring that students’ ideas are presented in a structured, accessible format for evaluation. Finally, the evaluation results and related data are provided to users in PDF format. Designed with a user-friendly interface, the application allows educators to quickly interpret responses and align them with expected learning outcomes through integrated language and image analysis tools. This system offers an innovative model for digitizing, standardizing, and automating open-ended exam evaluations, contributing to the systematic improvement of educational assessment processes. However, the application has limitations. Variations in handwriting and low-quality scans may reduce OCR accuracy, and AI-supported content analysis risks missing contextual nuances. Additionally, the system requires a stable internet connection, limiting offline functionality. Future enhancements, including advanced OCR models, multilingual support, and an offline mode, are planned to address these issues. The application developed in this direction is expected to make a significant contribution to the digitalization of educational assessment and to adapt to next-generation technologies.

Keywords

Image Processing, Handwriting Recognition, AI, Cloud Vision API, OpenAI API, iOS

Açık Uçlu Sınavların Yapay Zeka ve Görüntü İşleme Aracılığıyla Otomatik Değerlendirilmesi için iOS Mobil Uygulama Tasarımı

Abstract

Açık uçlu sınavların değerlendirilmesi, eğitim süreçlerinde zaman yönetimi ve değerlendirme tutarlılığı açısından önemli zorluklar yaratır. Bu çalışma, el yazısı ile yazılan açık uçlu sınav yanıtlarının değerlendirilmesini kolaylaştırmak için iOS tabanlı bir mobil uygulama geliştirmeyi amaçlar. “Sınav Okuma” adlı uygulama, görsel tanıma araçlarını dil analiziyle birleştirerek eğitimcilerin zamanında ve daha adil değerlendirmeler yapmasını amaçlar. Swift programlama diliyle geliştirilen uygulama, iki ana teknolojik bileşene dayanır. İlk olarak, öğrencilerin el yazısı yanıtları, Google Cloud Vision API aracılığıyla Optik Karakter Tanıma (OCR) teknolojisi kullanılarak dijital metne dönüştürülür. Bu metinler, OpenAI API ve GPT-4o modeli ile netlik ve tutarlılık açısından incelenir, böylece öğrencilerin fikirleri daha yapılandırılmış ve değerlendirme için erişilebilir hale gelir. Son olarak, değerlendirme sonuçları ve ilgili veriler, kullanıcıya PDF formatında sunulur.

Kullanıcı dostu bir arayüze sahip olan uygulama, eğitimcilerin öğrenci yanıtlarını hızlıca yorumlamasını ve entegre dil ve görüntü analiz araçlarıyla beklenen öğrenme çıktılarına eşleştirmesini sağlar. Bu sistem, açık uçlu sınav değerlendirmesinin dijitalleştirilmesi, standartlaştırılması ve otomasyonu için yenilikçi bir model sunarak eğitimde ölçme ve değerlendirme süreçlerinin sistematik olarak iyileştirilmesine katkıda bulunur. Ancak, uygulamanın bazı sınırlamaları vardır. El yazısı tanıma farklılıkları ve düşük kaliteli taramalar OCR doğruluğunu etkileyebilir; yapay zeka destekli içerik analizinde bağlamın tam anlaşılmaması riski bulunabilir. Ayrıca, sistem tam performans için kesintisiz internet bağlantısı gerektirir, bu da çevrimdışı kullanım senaryolarını sınırlar. Bu sınırlamalar göz önüne alındığında, daha gelişmiş OCR modellerinin entegrasyonu, çok dilli destek ve çevrimdışı mod geliştirilmesi gibi gelecekteki iyileştirmeler hedeflenir. Bu doğrultuda geliştirilen uygulamanın, eğitimde ölçme ve değerlendirme alanında dijitalleşmeyi destekleyen ve yeni nesil teknolojilere uyum sağlayan önemli bir katkı sunacağı düşünülmektedir.

Keywords

Görüntü İşleme, El Yazısı Tanıma, Yapay Zekâ, Cloud Vision API, OpenAI API, iOS

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