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- 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 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.
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İncele - 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.
- Derleme MakaleAçık Erişim2026Scopus SJRQ1Artificial 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).
AtıfScopus1WoS1OpenAlex3İncele - MakaleYayıncıda açık erişim2026Scopus SJRQ2A pilot pre–post study of a scaffolded AI chatbot for financial literacy education among university students
Ayberkin, Doruk; Yirik, Emirhan
This pilot study examines pre- to post-test changes in university students’ financial literacy knowledge following a one-week student-directed learning period using a pedagogically scaffolded GPT‑4o Application Programming Interface (API) chatbot. Guided by constructivist learning theory [29], the immediate feedback model [10], and self-determination theory [24], the study employed a single-group pre-test–post-test quantitative design with 32 undergraduate students at a Turkish public university. The primary outcome was financial literacy knowledge, measured with 32 factual statements rated on a six-point agreement scale and keyed against correct responses, across six subscales (Basic Economics and Finance, Individual Banking, Retirement and Insurance, Financial Statements, Investment, and Tax and Legislation). Five objective multiple-choice items served as a limited, complementary performance check. Paired-samples t-tests were used for normally distributed difference scores, and Wilcoxon signed-rank tests for non-normal difference scores; six subscale comparisons were interpreted using Holm-adjusted p values. The overall score increased from pre-test to post-test, with a large standardized mean change (d = 1.09, 95% CI [0.64, 1.52]), and all six exploratory subscale comparisons remained statistically significant after Holm adjustment. Objective item performance also increased on several items, including the compound-interest item (3.1% to 81.2%). However, because the design included no control or comparison group, identical objective items were repeated, and chatbot interaction logs were not systematically recorded, the observed changes cannot be causally attributed to the chatbot. The findings therefore provide preliminary, uncontrolled evidence that warrants testing in controlled studies rather than evidence of intervention effectiveness.
- MakaleAçık Erişim2025Scopus SJRQ1The Effects of Artificial Intelligence Supported Flipped Classroom Applications on Learning Experience, Perception, and Artificial Intelligence Literacy in Higher Education
Yavuz, Mehmet; Balat, Sener; Kayali, Bunyami
Rapid 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.
AtıfWoS12OpenAlex15İncele - MakaleAçık Erişim2023Scopus SJRQ3An Investigation of Digital Transformation Activities of Higher Education in Türkiye
Yavuz, Mehmet; Kayalı, Bünyami; Karaman, Selçuk
This study aimed to examine the digital transformation activities of universities. The document analysis method was used in the study, one of the qualitative research methods. In this context, the websites of 207 universities were examined using the "Digital transformation activities form of universities" developed by the researchers. The data were analyzed by content analysis method. As a result of the analysis, six broad themes, also called dimensions in the current study, emerged, such as research, education, culture, process, technology, and community service regarding digital transformation activities of higher education institutions. Infrastructure studies, educational activities for research and supply and the use of smart systems to provide various cooperation opportunities are discussed within the scope of the research dimension. There are online scientific studies, online education programs, digital resources, and open access opportunities in the education dimension. As for the culture dimension, online manager activities for corporate culture, training of trainers, culture-arts and scientific activities took place. With the transfer of business processes to the digital environment, corporate promotion and communication activities were included in the process dimension. In the technology dimension, smart systems, smart campus applications and innovative technologies took place. Finally, the scope of community service compromised related units, university-industry cooperation activities and social activities. As a result, universities carry out activities to support processes such as education, research, management, and cooperation and to realize them in digital environments. For this purpose, it can be said that they perform effective and efficient processes by using various intelligent systems.
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