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Scopus Q: N/ASubject: Artificial Intelligence

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    Article
    An Investigation Into The AI-Assisted Visualization Of Children’s Songs: The Case Of Ali Baba’s Farm
    (Nilgun SAZAK, 2025) Südor, S.; İpekçiler, B.
    This study aims to visualize children’s songs, which are part of primary-level music education, using AI-supported tools. The objectives of the Ministry of National Education’s music course curriculum were examined, and both the themes to be emphasized in song selection and the pedagogical functions of children’s songs were analyzed. In the literature review, the Web of Science and Google Scholar databases were used. The obtained source data were analyzed with the VOSviewer software to generate conceptual maps, through which thematic trends in the field were identified. In the practical part of the study, the children’s song “Old MacDonald’s Farm” was visualized in detail using two different AI-supported tools: RunwayML and WZRD.ai. In RunwayML, prompt-based scenes were generated using the “text-to-video” feature, and visuals compatible with the lyrics of the song were created. On the WZRD.ai platform, visuals were automatically generated in response to sound waves, and the limitations of the platform were examined. Based on the findings, it was concluded that RunwayML offers more effective results for pedagogical content production, while WZRD. ai, despite its technical capabilities, falls short in delivering child-appropriate visual stimuli. The study also provides a theoretical foundation on synesthesia and discusses how AI tools can be integrated into music education in early childhood and primary school levels. The findings indicate that AI-supported visualization tools have the potential to provide engaging and flexible educational materials that support learning at the primary school level. It is recommended that teacher training programs develop hands-on modules for these tools, and that future research focus on how these technologies can be adapted to various songs, age groups, and learning domains. © © 2025 The Author(s). This is an open access article distributed under the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is properly cited.
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    Hybrid AI-Driven Decision Model for Test Automation in Agile Software Development
    (Institute of Electrical and Electronics Engineers Inc., 2025) Bon, Mohammad; Yazici, Ali
    Test automation plays an essential role in Agile Software Development (ASD), but its implementation remains complex. This study conducts a Systematic Literature Review (SLR) to identify key points of test automation and recent developments in Artificial Intelligence (AI). Based on 21 factors proposed by Butt et al., we construct a three-phase decision-support model addressing software, tools, tests, human, and economic dimensions. To improve this model, modern AI techniques - including natural language processing (NLP), machine learning (ML), Mabl (a self-healing, AI-based test automation tool) and Parasoft Selenic - are used. These technologies automate test case generation, prioritization, and maintenance, aligning with Agile's fast-paced demands. Our proposed hybrid model applies NLP to identify effecting factors, ML for impact scoring, and reinforcement learning (RL) for guiding automation strategies. The goal is to decrease manual processes, improve decision accuracy, and to adapt to evolving requirements. However, challenges such as data quality and the need for AI expertise remain. Future work should focus on practical validation and explore applications in non-functional testing. This study offers a practical, AI-enhanced framework to support Agile teams in streamlining test automation. © 2025 IEEE.
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    Article
    Revolutionizing Glaucoma Care: Harnessing Artificial Intelligence for Precise Diagnosis and Management
    (Gazi Eye Foundation, 2025) Ucgul, A.Y.; Aktaş, Z.
    Glaucoma is a leading cause of irreversible blindness worldwide, necessitating early detection and effective management to prevent vision loss. Recent advancements in artificial intelligence (AI) have revolutionized glaucoma care by enhancing diagnostic accuracy, monitoring disease progression, and personalizing treatment strategies. AI models, including machine learning and deep learning algorithms, have demonstrated exceptional performance in analyzing fundus photography, optical coherence tomography, and visual field data, surpassing traditional diagnostic methods. Convolutional neural networks have shown high sensitivity and specificity in detecting glaucomatous changes, while vision transformers and hybrid AI models further refine risk assessment and prognosis. Additionally, AI-powered monitoring systems utilizing multi-modal data integration allow for more precise prediction of disease progression and the need for surgical intervention. The incorporation of AI into telemedicine and wearable intraocular pressure sensors extends glaucoma management to remote and underserved populations. Despite these advancements, challenges remain, including issues related to algorithm generalizability, data standardization, bias, and ethical concerns regarding AI-driven clinical decision-making. To maximize AI’s potential in glaucoma care, further interdisciplinary research, regulatory oversight, and multi-center validation studies are needed. By addressing these challenges, AI can be effectively integrated into clinical practice, leading to improved early detection, enhanced treatment strategies, and more personalized patient care. The future of AI in glaucoma management holds great promise, paving the way for a more data-driven and patient-centered approach to combating this sight-threatening disease. © 2024 The author(s).