Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/7

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Department: search.filters.department.Fenerbahçe UniversitySubject: search.filters.subject.Large Language Models

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  • Article
    The Potential of AI Chatbots as Diagnostic Tools in Mental Health: Evaluating Exercise Dependence Symptoms
    (Springer, 2025) Saraç, H.; Yüzakı, E.; Aşçi, F.H.
    This study aimed to evaluate the effectiveness of AI chatbots (Claude-3.5 Sonnet, ChatGPT-4o, and Gemini-1.5 Pro) in identifying exercise dependence symptoms using a hypothetical case study. To this end, three sport psychologists, each with a minimum of five years of experience, assessed the chatbots' performance in diagnostic assessment competency, implementation of diagnostic criteria, summary quality, terminology use, and overall effectiveness. The results indicated that while all chatbot models successfully identified major symptoms, their performance varied. Specifically, the Claude-3.5 Sonnet model demonstrated superior performance in specific areas, such as providing a clear case summary and using accurate terminology. However, all chatbots exhibited limitations in recognizing symptom severity and distinguishing between primary and secondary dependence. The sport psychologists expressed a willingness to use at least one AI chatbot model as an assistive tool in initial client assessments. These findings highlight the potential value of AI chatbots in mental health assessments. Future research should prioritize the optimization of algorithms and training data through expert collaboration and controlled real-world testing to improve the reliability and practical application of these tools. © 2025 Elsevier B.V., All rights reserved.
  • Conference Object
    Sts: AI-Driven Smart Test Scenario Generation Tool
    (IEEE, 2025) Baglum, Cem; Yayan, Ugur
    One of the most critical steps in the software testing lifecycle, test scenario generation, reduces process efficiency due to its high time and resource requirements. As an innovative solution to this issue, the Smart Test Scenario Tool (STS) has been developed. Smart Test Scenario Tool (STS) enhances contextual accuracy and automation in test scenario generation by analyzing documents in xlsx, py, cpp, txt, and docx formats using large language models. This approach minimizes time loss, and the risk of errors encountered in traditional manual testing processes while transforming test procedures into a context-driven and systematic framework, offering an innovative contribution to the literature. Strengthened with a Streamlit interface, MongoDB-supported database management, and Ollama integration, the system enables the test scenario generation process, a critical component of the software testing cycle, to be conducted more efficiently and reliably. The validity of the study was confirmed through two distinct projects, the first implemented in Python and the second in C++.