Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ1The Relational Online Learner: Self-Directed Learning, University Belonging, and Student Engagement in Online Higher Education from a Self-Determination Theory Perspective
Kayalı, Bünyami; Yavuz, Mehmet; Üstün, Ayşin Gaye; Uçar, Hasan; Erdoğdu, Erdem; Aydemir, Mesut; Bozkurt, Aras
We examined the relationships between self-directed learning skills, university belonging, and student engagement in open online and distance learning environments within the framework of Self-Determination Theory. The research was conducted using a correlational survey model. Data were collected from 696 university students enrolled at a large-scale open, online and distance learning institution in Türkiye. We used three instruments measuring self-directed learning skills, university belonging, and student engagement. The data were analyzed using partial least squares structural equation modeling. The findings indicate that university belonging significantly and positively predicts all dimensions of student engagement. While self-control skills, learning skills, and sustaining the desire to learn significantly explain university belonging, metacognitive awareness and ability to identify sources were found to have no significant effect. Furthermore, it was found that university belonging acts as a significant mechanism in the relationships between self-control skills, learning skills, and sustaining the desire to learn, and student engagement. In this open and distance learning context, student engagement was not explained by individual learning skills alone. University belonging may therefore be one mechanism that partly accounts for the association between self-directed learning and academic participation. The study contributes by testing self-directed learning at the level of its dimensions, by identifying university belonging as a mechanism that partly accounts for its association with engagement, and by situating this model in a large-scale open and distance learning setting.
- Derleme MakaleAçık Erişim2026Scopus SJRQ2From Technology Adoption to Organizational Governance: Mapping the Intellectual Structure and Shifting Paradigms of AI Governance in Educational Leadership
Kayalı, Bünyami; Yavuz, Mehmet; Bozkurt, Aras
This study maps the intellectual structure and thematic evolution of the intersection between artificial intelligence (AI) and educational leadership in related literature. Employing a hybrid research design that integrates bibliometric analysis with Latent Dirichlet Allocation (LDA)-based topic modeling, we examined 757 unique records retrieved from the Web of Science and Scopus databases for the period 2017–2026. Through computational optimization, the corpus was delineated into six emerging research themes that dictate the field’s shifting paradigms: “Digital Leadership & Transformation,” “Teaching & Student Learning,” “AI Acceptance & Self-Efficacy,” “Data Management & Machine Learning,” “School Leadership & Principals,” and “Technical & Computational Fields.” While “Digital Leadership & Transformation” dominates over half of the literature, a sharp proportional decline in “School Leadership & Principals” uncovers a critical structural imbalance between abstract institutional transformation discourse and the actual human agents executing it. Furthermore, a severe underrepresentation of technical, governance, and transparency themes exposes a critical “opaque box” crisis of institutional agency. The study concludes that the field remains theoretically fragmented and calls for an integrated socio-technical framework reconciling leadership governance with the complex realities of AI systems.
- MakaleAçık Erişim2026Scopus SJRQ1Investigating the relationship between social presence and transactional distance in online distance learning environments through structural equation modeling analysis
Yavuz, Mehmet; Kayalı, Bünyami; Üstün, Ayşin Gaye; Uçar, Hasan; Erdoğdu, Erdem; Bozkurt, Aras
While digitalization has increased flexibility in online distance learning, it has also brought challenges such as emotional strain, interactional issues, and social isolation. In this context, students’ sense of social “presence” is vital for sustaining their well-being and ensuring high-quality interaction. Grounded in Transactional Distance Theory (TDT) and the Community of Inquiry (CoI) framework, this study modeled the relationships between social presence (SP) and interaction types within the using Structural Equation Modeling (SEM). The Social Presence and Transactional Distance Scales were used as data collection instruments. The sample comprised 574 online distance learners, with data collected via an online questionnaire. The results of the SEM analysis showed that SP was a significant positive predictor of all interaction types. Findings show that SP most strongly influences student–student and student–learning environment interactions, while also significantly enhancing student–instructor, student–content, and student–interface interactions. These results indicate that online learning environments should support not only content delivery but also meaningful social interaction. The study highlights the value of SP-oriented design in open, online and distance education systems and offers strategic recommendations for instructors, content developers, and digital learning administrators. Future research should compare synchronous and asynchronous learning structures.
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İncele - 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.
AtıfScopus1OpenAlex1İncele - MakaleAçık Erişim2026Scopus SJRQ1Trust in AI: scale development and validation for online distance learners
Üstün, Ayşin Gaye; Yavuz, Mehmet; Kayalı, Bünyami; Uçar, Hasan; Erdoğdu, Erdem; Bozkurt, Aras
Trust in artificial intelligence (AI) has emerged as a critical factor influencing students’ interactions with AI-supported learning environments. However, validated instruments for measuring student trust in AI within educational contexts remain limited. This study developed and validated a multidimensional scale to assess student trust in AI systems in online distance education. Scale development followed a sequential multi-stage design, including item generation based on literature review, expert evaluation for content validity, and psychometric validation through exploratory and confirmatory factor analyses. A total of 837 distance learning students participated in the study (EFA = 412; CFA = 307; validation sample = 118). Exploratory factor analysis supported a five-factor structure explaining 63.20% of the total variance. Confirmatory factor analysis with an independent sample indicated good model fit (/df = 2.17; CFI =.95; TLI =.94; RMSEA =.06). The scale demonstrated high internal consistency (=.94; =.94). Multi-group confirmatory factor analysis supported metric invariance across gender, indicating comparable measurement across groups. No significant gender differences were observed, and trust in AI was not significantly associated with grade point average or system engagement frequency. These results suggest that trust represents a distinct perception that may influence how students engage with AI-supported learning environments. The resulting 21-item scale provides a reliable instrument for investigating student trust in AI and offers practical implications for the design and implementation of AI-supported learning in higher education.
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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.