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Öğe Artificial Intelligence Supported Online Assessment and Evaluation in Higher Education: A Systematic Review With SWOT Analysis(Sage Publications Inc, 2026) Ustun, Aysin Gaye; Kayali, Bunyami; Yavuz, MehmetArtificial 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.Öğe Development and Validation of AI Acceptance Scale (AI-AS) for Learners in Higher Education(Springer Int Publ Ag, 2026) Kayali, Bunyami; Yavuz, Mehmet; Ustun, Aysin Gaye; Ucar, Hasan; Erdogdu, Erdem; Bozkurt, ArasThe 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 () 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.Öğe Examining the relationships between artificial intelligence literacy, AI-TPACK, job satisfaction, and well-being among teachers through structural equation modeling(Elsevier, 2026) Kayali, Bunyami; Balat, Sener; Yavuz, MehmetThis 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.Öğe From Technology Adoption to Organizational Governance: Mapping the Intellectual Structure and Shifting Paradigms of AI Governance in Educational Leadership(Routledge Journals, Taylor & Francis Ltd, 2026) Kayali, Bunyami; Yavuz, Mehmet; Bozkurt, ArasThis 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.Öğe Investigating the relationship between social presence and transactional distance in online distance learning environments through structural equation modeling analysis(Routledge Journals, Taylor & Francis Ltd, 2025) Yavuz, Mehmet; Kayali, Bunyami; Ustun, Aysin Gaye; Ucar, Hasan; Erdogdu, Erdem; Bozkurt, ArasWhile 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.Öğe Investigation of student experiences with ChatGPT-supported online learning applications in higher education(Australasian Soc Computers Learning Tertiary Education-Ascilite, 2023) Kayali, Bunyami; Yavuz, Mehmet; Balat, Sener; Calisan, MucahitThe purpose of this study was to determine university students' experiences with the use of ChatGPT in online courses. The sample consisted of 84 associate degree students from a state university in Turkey. A multi-method approach was used in the study. Although quantitative data were collected using the Chatbot Usability Scale, qualitative data were collected using a semi-structured interview form that we developed. The data were analysed using descriptive and content analysis methods. According to the findings, ChatGPT exhibits advantages such as a user-friendly interface and fast, concise, relevant responses. Moreover, emphasizing its contribution to the learning process, the information provided was sufficient and topic-oriented. The understandability of the chatbot's functions and the clarity of their communication were emphasized. However, there are disadvantages such as performance issues, frequency of errors and the risk of providing misleading information. Concerns have also been raised about the potential difficulties chatbots may face in ambiguous conversations and providing insufficient information on privacy issues. In conclusion, ChatGPT is recognised as a potentially valuable tool in education based on positive usability impressions; however, more research is needed for its safe use.Implications for practice or policy center dot Based on positive usability impressions, students and instructors can use ChatGPT to support educational activities.center dot ChatGPT can promote and enhance students' personalised learning experiences.center dot ChatGPT can be used in all higher education courses.center dot Users should be cautious about the accuracy and reliability of the answers provided by ChatGPT.center dot Decision-makers should take precautions against risks such as privacy, ethics, confidentiality and security that may arise from using artificial intelligence in education.Öğe Multi-level Emergency Remote Teaching Framework for K-12 Distance Education: A Stakeholder-based Qualitative Study(Acad Conferences & Publ Int Ltd, 2026) Kayali, Bunyami; Karaman, SelcukThis study develops a multi-level Emergency Remote Teaching (ERT) framework for K-12 distance education, grounded in the experiences of students, teachers, parents, and administrators during the COVID-19 pandemic. Prior ERT research has predominantly focused on single stakeholder groups or produced descriptive findings without synthesizing perspectives into a structured response model. Using a qualitative case study design, semi-structured interviews were conducted with 100 participants (25 per stakeholder group) selected through maximum variation sampling. Thematic content analysis was employed to identify convergent and divergent priorities across groups, which were subsequently mapped onto the five layers of the Holistic Analytical Layer Framework (HALF): policy, system, program, course, and activity, based on their functional relevance to each layer. Findings indicate that digital infrastructure functions as a foundational system-level prerequisite shared across all stakeholder groups, while pedagogical, psychosocial, and governance priorities diverge by role. Teachers emphasized assessment reliability and instructional continuity; students highlighted engagement and psychosocial support; parents stressed communication and guidance mechanisms; while administrators prioritized governance flexibility and decision-making autonomy. The resulting framework positions ERT not as an isolated emergency improvisation but as a coordinated, multi-layered response architecture that aligns stakeholder expectations across interconnected educational layers and supports the coordination of policy, infrastructure, programmatic planning, and instructional practices during crisis periods. The study contributes to the distance education literature by transforming multi-stakeholder perspectives into an integrated framework and extending the application of HALF to emergency education contexts. For e-learning practice, the framework offers school administrators and policymakers a concrete diagnostic tool for auditing crisis-preparedness across governance, infrastructure, and instructional layers, while offering teacher educators a basis for designing professional development that addresses both technical and psychosocial readiness for remote instruction. Given its single-region, qualitative design, the framework should be understood as a context-sensitive foundation for future empirical validation rather than a universally transferable model.Öğe SURVEILLANCE OR SUPPORT? A SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE SUPPORTED PROCTORED ONLINE EXAMS(Anadolu Univ, 2026) Ustun, Aysin Gaye; Kayali, Bunyami; Yavuz, Mehmet; Bozkurt, ArasProctored 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.Öğe The Effects of Artificial Intelligence Supported Flipped Classroom Applications on Learning Experience, Perception, and Artificial Intelligence Literacy in Higher Education(Int Council Open & Distance Education, 2025) Yavuz, Mehmet; Balat, Sener; Kayali, BunyamiRapid changes in the field of technology have greatly affected learning and teaching styles as in every field. Artificial intelligence (AI), which is increasingly used in the field of education for different purposes such as improving learning outcomes or solving teaching problems, is one of the important technologies that contribute to this change. It is thought that there is a need for applications based on AI technology that can meet these needs in terms of sustainability of learning, especially in ODE environments where individual learning and instant feedback are important. Accordingly, the study aims to determine the impact of AI-supported flipped classroom applications on students' AI literacy and to evaluate their experiences and perceptions towards these applications. Explanatory sequential mixed design was used as the method in the study. In this context, in the quantitative phase of the study, a one-group pretest-posttest design from the weak experimental design was used to determine students' perceptions of flipped learning and AI literacy levels. In the qualitative phase of the study, a case study was used to determine students' views on learning experiences. The sample comprised 33 university students enrolled in a Web Design and Coding associate degree program. Data were collected through a flipped learning perception scale, an AI literacy scale, and semi-structured interviews. Quantitative findings revealed a statistically significant improvement in students' AI literacy levels from pre-test to post-test. Qualitative analyses indicated that AI-supported flipped classroom practices positively influenced students' motivation, interest in the course, and personalized learning experiences. Furthermore, the model fostered collaboration by promoting interactive learning environments and enhanced critical thinking and problem-solving skills. However, students also reported challenges related to managing their time effectively within this instructional model. The study concludes that AI-supported flipped classroom applications hold promise for improving AI literacy and fostering active, student-centered learning in higher education. Nevertheless, challenges such as time management warrant further attention. It is recommended that future research explore the implementation of this model across diverse courses and educational contexts, investigate strategies to mitigate time management issues, and conduct comparative studies between AI-supported and traditional flipped classroom models to further elucidate the sources of observed positive perceptions.Öğe Trust in AI: scale development and validation for online distance learners(Springernature, 2026) Ustun, Aysin Gaye; Yavuz, Mehmet; Kayali, Bunyami; Ucar, Hasan; Erdogdu, Erdem; Bozkurt, ArasTrust 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 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\chi <^>{2}$$\end{document}/df = 2.17; CFI = .95; TLI = .94; RMSEA = .06). The scale demonstrated high internal consistency (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document} = .94; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\omega $$\end{document} = .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.
