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- Derleme MakaleAçık Erişim2026Scopus SJRQ2SURVEILLANCE OR SUPPORT? A SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE SUPPORTED PROCTORED ONLINE EXAMS
Ustun, Aysin Gaye; Kayali, Bunyami; Yavuz, Mehmet; Bozkurt, Aras
Proctored online exams have become widespread, especially after COVID-19, increasing exam security and efficiency. In recent years, artificial intelligence developments have increased these systems' importance by expanding their use. Accordingly, this study aims to provide an in-depth understanding of the conceptual structure of artificial intelligence-supported proctored online examination (AI-SPOE) research. Systematic literature review and text mining were used as methods. Thirty-two studies in Scopus and Web of Science databases were analyzed according to the PRISMA technique. While all the studies were examined in the context of content analysis, abstracts and keywords of the relevant studies were analyzed by text mining. The current study found it effective in increasing academic integrity and exam security, but problems such as privacy violations, exam anxiety, and algorithmic biases were encountered. Although methods such as biometric verification and behaviour analysis successfully detect cheating, technical difficulties, and false positives negatively affect the user experience. In the future, more inclusive designs, transparent algorithms, and alternative evaluation methods are suggested. In conclusion, the study emphasizes the potential of AI-SPOE systems and highlights the importance of ethical and technical improvements.
AtıfScopus1OpenAlex1
İncele - MakaleYalnız künye2026Scopus SJRQ1Development and Validation of AI Acceptance Scale (AI-AS) for Learners in Higher Education
Kayalı, Bünyami; Yavuz, Mehmet; Üstün, Ayşin Gaye; Uçar, Hasan; Erdoğdu, Erdem; Bozkurt, Aras
The use of artificial intelligence (AI) applications in educational activities is increasing day by day. This situation highlights the need for valid and reliable measurement tools to determine students' levels of acceptance of this technology. This study aims to develop a standard measurement tool to determine students' levels of acceptance of AI. To this end, the systematic scale development steps proposed by DeVellis and Thorpe (2021) were followed. Exploratory factor analysis conducted with 560 participant responses and confirmatory factor analysis conducted with 950 participant responses resulted in a 26-item, 5-factor structure. The factors included in the scale were listed as "usability", "ease of use", "intention and attitude", "facilitating conditions" and "trust and data privacy". The findings revealed that the scale had high internal consistency and exhibited satisfactory model fit indices. The results indicate that the scale is a valid and reliable measurement tool for determining students' levels of AI acceptance. The study offers practical implications for researchers and practitioners and highlights directions for future work on AI adoption in higher education environments.
- MakaleAçık Erişim2024Scopus SJRQ2Behind the Scenes: A Critical Perspective on GenAI and Open Educational Practices
Aksoy, Dilara Arzugül; Kursun, Engin
Artificial Intelligence (AI) is a rapidly evolving field that is influencing every aspect of life. Generative AI (GenAI) as a sub-branch of AI is used to create content in various formats such as text, images, video, and audio. This paper discusses the implications of GenAI for Open Educational Practices (OEP), highlighting the potential affordances and challenges. GenAI can address the challenges within the OEP by leveraging openness and ethical use. GenAI’s “generative” nature and human-like language capability can provide resources such as course material, activities, examples, questions, assessment, and learning outcomes in the context of OEP. With machine learning and deep learning infrastructure, it can support the discoverability and accessibility of open resources by increasing the metadata quality. GenAI can automatically score student assignments, answer their questions, and provide instant feedback to address the lack of interaction and feedback that arises due to the large number of students, especially in massive open online courses (MOOC). On the other hand, GenAI brings challenges such as data privacy and security, copyright, biased outputs, and the generation of false information. The conclusions emphasize the importance of a nuanced approach that considers not only the advantages but also the risks associated with adopting GenAI in the OEP world. Researching and developing how to apply these technologies to education is important to shape the future of education.
AtıfScopus27WoS18OpenAlex23
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