Sürdürülebilir Kalkınma Amaçları

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• Kitap BölümüAçık Erişim2026Scopus SJRQ4/r/bayburt/11548
  Turkish Students’ Use of NotebookLM in an Online Digital Literacy Course

  Kayali, Bünyami; Yavuz, Mehmet

  This experimental study investigates the effects of using NotebookLM on students’ academic achievement and students’ assessment of NotebookLM in online digital literacy courses in higher education. It was conducted using an explanatory sequential mixed-methods design. A one-group pretest–posttest model was employed. During a five-week intervention, 33 Turkish students used NotebookLM for summarizing, concept mapping, and scenario-based problem-solving activities. Data was collected from a pretest, posttest, and interviews. Quantitative data was projected into SPSS 27 for analysis, and qualitative data was thematically analysed. Results reveal a significant difference between the pretest score (M = 66.90, SD = 10.26) and posttest score (M = 74.90, SD = 13.16) in terms of academic achievement, t(32) = −3.51, p = 0.001. The effect size was moderate (Cohen’s d = 0.68). Interview data shows that the students perceived NotebookLM as helpful for comprehension, time efficiency, motivation, and feedback, though constrained by language support and occasional technical problems. The findings suggest implications for using NotebookLM in online courses in higher education in Turkey.

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• MakaleAçık Erişim2026Scopus SJRQ1/r/bayburt/11509
  The 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.

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• Derleme MakaleAçık Erişim2026Scopus SJRQ2/r/bayburt/11458
  From 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.

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• MakaleAçık Erişim2026Scopus SJRQ1/r/bayburt/11292
  Examining the relationships between artificial intelligence literacy, AI-TPACK, job satisfaction, and well-being among teachers through structural equation modeling

  Kayalı, Bünyami; Balat, Şener; Yavuz, Mehmet

  This study explores the relationships between teachers' artificial intelligence literacy (AIL), AI-TPACK competencies, job satisfaction, and well-being. Survey data from 350 in-service teachers were analyzed using PLS-SEM to examine how AI-related competencies are associated with professional and psychological outcomes. The findings reveal that AIL strongly predicts AI-specific knowledge, especially AI-TCK, AI-TK, AI-TPCK, and AI-TPK, with a moderate influence on traditional pedagogical knowledge. At the job satisfaction level, AI-TCK and general pedagogical knowledge are positively associated with satisfaction, while AI-TK has a negative relationship. Mediation analyses show that AI competencies influence well-being through job satisfaction. AI-TCK and pedagogical knowledge are positively associated with well-being indirectly through job satisfaction, whereas AI-TK shows a negative indirect association along the same pathway. The study suggests that AI-related competencies are linked to teachers' work-life outcomes, and highlights job satisfaction as the key mediator between AI competence and well-being. Because the design is cross-sectional and self-reported, these relationships are interpreted as associations rather than causal effects. The study advocates for integrating AI in ways that support pedagogical development rather than just technical skills.

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• MakaleAçık Erişim2026Scopus SJRQ1/r/bayburt/11273
  The 20-Year Evolution of Distance Education: A Bibliometric and LDA-Based Topic Model Analysis

  Yavuz, Mehmet; Balat, Şener; Kayalı, Bünyami; Gülen, Emirhan

  Although research on distance education has increased substantially in recent years, comprehensive analyses capturing the overall thematic development of the field remain limited. Modeling studies are necessary to address this gap and to uncover long-term research trends. This study aimed to reveal the trends of research conducted in the field of distance education in the last two decades by using machine learning methodology and bibliometric analysis. The unique aspect of the study was its use of the Latent Dirichlet Allocation (LDA) algorithm, a machine learning method for analysing large-scale data. Within the scope of the study, 54,444 articles in the Web of Science database were analysed. Bibliometric analysis revealed that distance education had gained significant momentum, especially in the post-pandemic period, with a wide range of applications in different disciplines such as education, management, and health. As a result of LDA analysis, 19 thematic topics were identified. Among these, digitalization, artificial intelligence, Web-based learning, and social interaction and collaboration stood out. Time series analysis showed an increasing trend for topics such as artificial intelligence and emergency distance education over the years, with less interest shown in topics such as Web-based learning and program design. The study emphasized that distance education is in a technology-driven transformation process and pointed to areas that will offer new opportunities for researchers. Furthermore, recommendations for decision-makers, such as investments in digital infrastructure, integration of artificial intelligence-based systems, and the establishment of quality standards, also emerged.

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• MakaleAçık Erişim2026Scopus SJRQ1/r/bayburt/11211
  Investigating 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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  AtıfScopus5·WoS4·OpenAlex7

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• Derleme MakaleAçık Erişim2026Scopus SJRQ2/r/bayburt/11153
  SURVEILLANCE 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.

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  AtıfScopus1·OpenAlex1

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• MakaleAçık Erişim2026Scopus SJRQ1/r/bayburt/11139
  Trust 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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  AtıfScopus3·WoS2·OpenAlex3

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• MakaleYalnız künye2026Scopus SJRQ1/r/bayburt/11128
  Development 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.

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• Derleme MakaleAçık Erişim2026Scopus SJRQ1/r/bayburt/11113
  Artificial Intelligence Supported Online Assessment and Evaluation in Higher Education: A Systematic Review With SWOT Analysis

  Üstün, Ayşin Gaye; Kayalı, Bünyami; Yavuz, Mehmet

  Artificial intelligence-supported online assessment and evaluation (AI-SOAE) systems have the potential to transform assessment and evaluation processes in higher education; however, there is a need for a comprehensive evaluation of the pedagogical, technical, and ethical dimensions of these systems. In this regard, the study systematically examined the characteristics of AI-SOAE systems within the framework of a SWOT analysis. A systematic literature review method was used in the research. Ninety-eight studies published between 2018 and 2025 in the Web of Science (WoS), Scopus, and Education Resources Information Centre (ERIC) databases were analysed in accordance with PRISMA guidelines. The findings show that the strengths of AI-SOAE systems include personalised feedback, advanced assessment processes, increased efficiency, and data-driven decision support mechanisms. In contrast, technical infrastructure limitations, algorithmic biases, data management issues, and negative user experiences were identified as the weaknesses of the systems. The analysis also reveals that AI-SOAE systems offer significant opportunities in terms of pedagogical transformation, digitalisation, and institutional competitiveness; however, they face threats such as data security, ethical concerns, risks to academic integrity, and limited empirical evidence. Consequently, AI-SOAE systems offer context- and application-dependent potential for supporting assessment and evaluation practices in higher education. Effectively realising this potential depends on strengthening technical infrastructure, managing algorithmic risks, and developing transparent, ethical, and institutional policy frameworks. By synthesising the literature in the field of AI-SOAE in a structured manner, this study provides a comprehensive assessment for researchers, educators, and policymakers. © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

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  AtıfScopus1·WoS1·OpenAlex3

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