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- Öztürk, Mesut(25)
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Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ1Integrating artificial intelligence into preservice teacher education: enhancing AI literacy and digital material design competency
Gulen, Hilal Nur; Kucuk, Sevda
The rapid advancement of Artificial Intelligence (AI) has transformed educational tools and practices. Integrating AI into instruction supports complex thinking, enriches teaching, and helps educators in material creation and visualization. Therefore, it is essential to equip both students and educators with AI literacy and digital material design competencies in order to support pedagogically sound and ethically responsible instructional practices. Within this context, the present study examines the extent to which an AI-integrated instructional technology course influences pre-service teachers’ AI literacy and digital material design competencies, as well as their perceptions and experiences of learning within an AI-integrated instructional environment. Using a mixed-methods approach, 50 pre-service social studies teachers (41 female, 9 male) participated in a semester-long course. Data were collected through the Artificial Intelligence Literacy Scale, Digital Material Design Competency Scale, and semi-structured interviews. Paired sample t-tests indicated significant gains in both AI literacy and digital material design competencies, with large effect sizes. Qualitative and quantitative findings showed that AI tools not only improved technical abilities but also enhanced pedagogical thinking. The study highlights the importance of AI-integrated curricula in preparing future teachers for modern educational challenges.
AtıfScopus2WoS1OpenAlex3
İncele - MakaleYalnız künye2026Scopus SJRQ1AI-Assisted formative assessment in social studies education: development of an NLU-integrated smart diagnostic branched tree platform and testing its effect
Yetisensoy, Okan
Formative assessment practices have been a significant focus in Social Studies education for many years. One commonly used technique in these practices is the diagnostic branched tree, which is designed to assess students’ understanding through a branching structure based on their responses. While this technique shows promise for identifying students' learning status and gaps throughout the ongoing process, it presents several structural limitations. This research aims to address these limitations by integrating diagnostic branched trees with natural language understanding, a key AI technique in educational assessment, thereby enhancing their diagnostic capabilities and making them smarter tools in educational settings. Furthermore, this research seeks to evaluate the impact of this revised system in middle schools within a practical setting. Following the development phase, the results of the experimental study demonstrated that the revised system had a significantly positive impact on students' learning performance. Qualitative data, meanwhile, suggested that this success was largely due to the system's ability to provide formative feedback and personalized guidance, which fostered metacognition and self-regulated learning. At this point, considering the positive transformations brought about by technology, it is recommended that innovative technologies be developed to enhance formative assessment across various levels of K–12 education.
- MakaleYalnız künye2026Scopus SJRQ2The effect of practising sports on communication skills in medical students
Kaşali, Kamber; Badicu, Georgian; Al-Mhanna, Sameer Badri; de Sousa Fernandes, Matheus Santos; Şeran, Buket; Akman, Onur
Effective communication is essential for medical students in terms of patient interaction, teamwork and professional success. This study examines the effect of doing sports on the communication skills of medical students. Three hundred forty-five medical students participated in the study and their communication skills were assessed using the Effective Communication Skills Scale. The findings revealed a significant difference between Turkish and English students in terms of 'Using I Language' skills. In addition, when the students' beliefs about the physical, physiological and psychological benefits of sports were compared with the sub-dimensions of the scale, statistically significant differences were found in the sub-dimensions of "Ego Enhancing Language" (p = 0.011), "Empathy" (p = 0.047) and "Using I Language" (p = 0.005). When the relationship between the class levels of the students and the sub-dimensions of the scale was examined, significant differences were observed in the sub-dimensions of 'Ego Enhancing Language' (p = 0.001), 'Active Listening' (p = 0.011) and 'Using I Language' (p = 0.007). The findings show that doing sports is essential in improving medical students' communication skills and suggest that encouraging sports in the medical education process may be beneficial in enhancing communication competencies.
AtıfOpenAlex1
İncele - MakaleAçık Erişim2026Scopus SJRQ3The impact of ChatGPT-integrated reading activities on the reading attitudes and habits of middle school students
Kaplan, Kadir; Özen, Nesime Ertan; Özcan, Halil Ziya
This study examines the impact of the artificial intelligence tool ChatGPT on middle school students' attitudes and reading habits during a brief intervention. Twenty-six students from a middle school in Türkiye—13 in the experimental group and 13 in the control group—took part in an eight-session, four-week quasi-experimental study. Both quantitative and qualitative data were collected using a sequential explanatory mixed-methods approach. Data was gathered and analyzed using the Attitudes and Habits towards Reading Scale, the Digital Literacy Scale, and focus group interviews. Preliminary results indicate that students in the experimental group had better reading habits and attitudes than those in the control group. After the ChatGPT intervention was implemented, the experimental group's students showed a significant improvement in their positive attitudes toward reading. However, the experimental group also showed an unexpected increase in negative reading attitudes, warranting further investigation. Three primary themes, covering both the advantages and disadvantages of the application, were identified from the student interview data. These results highlight how artificial intelligence (AI) technologies present a potentially novel way to boost reading motivation in educational settings. However, to use these technologies effectively, educators and students must acquire more knowledge and skills. Longer-term studies with larger sample sizes are also required to validate these initial findings.
AtıfScopus3OpenAlex3İncele - Derleme MakaleAçık Erişim2026Scopus SJRQ1A Meta-Analysis of Self-Management Strategies to Enhance Educators’ Teaching Performance
Aydin, Orhan; Ari-Arat, Ceren; Sulu, Mehmet D.
This meta-analysis examines the effectiveness of self-management strategies in enhancing the teaching performance of educators, with a focus on special education contexts. Thirty-six studies, including peer-reviewed articles and gray literature, were evaluated for methodological rigor using the What Works Clearinghouse (WWC) Design Standards. Of these, 26 studies meeting the standards with or without reservations were further analyzed using descriptive review, meta-analysis, and moderator analysis. Findings revealed self-monitoring is the most frequently used self-management strategy. Among the educators included in the studies, the majority were special education teachers. Effect sizes were calculated using Tau-U for all 26 studies, between-case standardized mean difference (BC-SMD) for 25 studies, and performance-criteria-based effect size (PCES) for 13 studies reporting a mastery criterion. The overall Tau-U indicated a very large effect size of 0.88 (CI95 = [0.74, 1]), and BC-SMD resulted in a large effect size of 0.87 (CI95 = [0.77, 0.98]). The PCES showed an overall effective effect size of 1.15 (CI95 = [1.07, 1.23]). Moderator analysis revealed behavior-specific praise, and fewer years of teaching experience were associated with higher teaching performance effect sizes compared to other relevant categories; however, years of teaching experience and higher teaching performance were positively associated when using BC-SMD calculations. Detailed findings along with corresponding recommendations are discussed. © Hammill Institute on Disabilities 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).
- MakaleAçık Erişim2026Scopus SJRQ1Examining 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.
- MakaleAçık Erişim2026Scopus SJRQ1The 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.
- BildiriAçık Erişim2026Autism Awareness Levels of University Students and Their Relationship with Various Variables
Ari Arat, Ceren; Brunetto, D.; Domenech, J.; Alvarez-Hevia, D.M.; Mart�nez-Varea, A.
This cross-sectional quantitative study examined university students’ awareness of autism spectrum disorder (ASD). The sample consisted of 354 students from different academic departments at a public university in Türkiye. Data were collected using the Autism Awareness Scale and a Demographic Information Form and analyzed through descriptive statistics, Pearson correlation, and independent-samples t-tests. Findings indicated relatively high awareness in social interaction, communication, and restricted and repetitive behaviors, whereas knowledge was lower regarding physical characteristics and early warning signs. Core behavioral knowledge was positively associated with peer relationships and early diagnosis indicators. Gender differences were minimal, except for higher early warning scores among females. Prior interaction with individuals with autism and ASD-related coursework were associated with higher awareness across multiple domains, while students without formal education scored higher on physical characteristics. Overall, the findings highlight the importance of direct experience and structured education in improving autism awareness among university students in higher education settings.
- MakaleYalnız künye2026Scopus SJRQ1The effect of digital and traditional methods on learning and retention in preschool education: a comparison of virtual reality, augmented reality, web-based, and manipulative materials
Tutkun, Cansu
The pedagogical use of digital technologies in early childhood education is increasingly prevalent today. Augmented reality, virtual reality, and web-based content have the potential to make children’s learning processes more interactive and lasting. In this context, the study compared the effects of different teaching methods presented with augmented reality, virtual reality, web-based, and manipulative (traditional) materials on children’s learning and retention levels. In the quasi-experimental study, a total of 400 children attending preschool education institutions in Bayburt, Turkey, were divided into four different groups and evaluated using a “pre-test–post-test–retention test” model. The results revealed that the virtual reality group achieved the highest scores in both learning and retention levels, followed by the augmented reality and web-based groups, while the manipulative group demonstrated the lowest performance. In addition, the virtual reality environment was evaluated by children as the most enjoyable and satisfying method, and children in this group were the most willing to use the training again. These results show that digital technologies such as virtual and augmented reality in preschool education should be evaluated as effective tools in terms of achieving pedagogical goals and increasing children’s interest in learning.
- 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.
AtıfScopus5WoS4OpenAlex7İncele - MakaleAçık Erişim2026Scopus SJRQ1The Effect of Mindfulness Practices on Positive Thinking and Stress Coping Behaviors of Earthquake Survivor Nursing Students: A Nonrandomized Controlled Study
Bal, Betul; Kaba, Nagihan Koroğlu
Purpose: This study was conducted to reveal the effect of mindfulness practices on nursing students’ stress-coping behaviors and positive thinking skills. Method: This was a two-group parallel nonrandomized study. Mindfulness practices were applied to the students in the experimental group. Data were collected using research tools (Personal Information Form, Positive Thinking Skills Scale, and Stress Coping Behaviors Scale for Nursing Students) as pre- and posttests. Findings: After the intervention, there was no significant difference between the two groups in the posttest scores of positive thinking skills (t = 0.624; p = .541) and stress coping behaviors of staying optimistic (t = −0.957; p = .345), transfer (t = −2.028; p = .050), problem-solving (t = −0.663; p = .512), and avoidance (U = 132; p = .230). Conclusions: Mindfulness practices are ineffective in improving earthquake survivor nursing students’ coping behaviors and positive thinking skills.
- MakaleAçık Erişim2026Scopus SJRQ1The effect of cognitive strategy instruction enriched with active learning techniques on word problem-solving skills of students with learning disabilities
Filiz, Tunahan
This study aimed to investigate the effectiveness of cognitive strategy instruction (CSI) enriched with active learning (AL) techniques in teaching word problem solving to primary school students with learning disabilities, as well as to examine the overall instructional process. Word problem solving is the process of using contextual information from real-life or fictional situations to develop mathematical solutions. The research was designed with an embedded mixed-method model. The quantitative phase included 30 third-grade students with learning disabilities, while the qualitative phase involved six randomly selected students from the experimental group. Quantitative analyses revealed that students in the experimental group showed significantly higher achievement in problem-solving tests, indicating the positive impact of CSI enriched with AL techniques. Qualitative findings highlighted increased class participation, enhanced collaboration, and more positive attitudes toward mathematics. Furthermore, teacher and peer interactions supported the problem-solving process, and the use of tools promoting mathematical thinking strengthened performance. The results demonstrated that CSI enriched with AL techniques encouraged strategic thinking and the generation of alternative solutions in word problem solving. The study, limited to three learning outcomes within the third-grade mathematics curriculum in Turkey, suggests that teachers should adapt and implement such instructional approaches across other mathematical domains, given the students’ positive experiences and outcomes.
- 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 SJRQ1A gated multi-scale attention framework for automated short answer grading in Turkish
Kaya, Mustafa; Kavun, Mahir
This study proposes a new deep learning architecture for automated short-answer grading (ASAG) in Turkish. The model integrates transformer-based encoders (BERTurk, RoBERTa-Turkish (RoBERTa), ELECTRA-Turkish (ELECTRA)) with a Gated Multi-Scale Head Attention (GMHA) layer. Additionally, a BiLSTM network and parallel CNN layers are employed to capture both global dependencies and local linguistic patterns.The model was evaluated on a newly created Turkish dataset containing 2200 student responses from 220 learners, each graded by four experts. Among the pretrained Turkish encoders, the BERT-based model with GMHA achieved the best performance (QWK = 0.839, Pearson = 0.856, RMSE = 0.918). The model was also compared with traditional machine learning approaches and large language models (LLMs) using the Leave-One-Question-Out (LOQO) validation protocol. Under this setting, it achieved QWK = 0.337, Pearson = 0.468, and RMSE = 1.213, outperforming all baselines.Ablation studies confirmed the effectiveness of the GMHA and parallel CNN components. These results highlight both the theoretical contribution of multi-scale gated attention and the practical potential of developing reliable automated grading systems for morphologically rich, low-resource languages like Turkish.
AtıfOpenAlex1İncele - 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.
AtıfScopus3WoS2OpenAlex3İncele - 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 - MakaleYalnız künye2026Scopus SJRQ1AI-enhanced disaster education in middle schools: A mixed study from social studies classes
Yetişensoy, Okan
This research aims to examine the educational potential of AI-assisted chatbots, the popular AI technology of recent years, in middle school disaster education. In this regard, six chatbot characters in human form, each with fictional stories, were developed for various disasters taught within the scope of 5th-grade Social Studies in Türkiye. While the practitioner teacher instructed on disaster-related curricular outcomes in the experimental group through activities that included the use of the relevant chatbots, the same curricular outcomes in the control group were taught using conventional pedagogical methods. The experimental results showed that the disaster knowledge of the experimental group students was significantly higher than that of the control group students. However, no significant difference was observed in students’ disaster awareness levels. In the semi-structured interviews, students expressed that chatbots promoted a more effective, interesting, and student-centered disaster education with their distinctive design and pedagogical features. However, they noted that chatbots had certain inadequacies in developing concrete experiences, as well as psychomotor skills necessary to address disaster risks. Findings from the study indicate that the relevant AI technology is effective in promoting disaster education processes, particularly in facilitating more effective learning. Nevertheless, achieving more complex competencies for mitigating risks requires a higher level of technological advancement. At this point, it is suggested that undertaking initiatives to combine chatbots with extended reality environments will be beneficial in making them a more effective tools in disaster education processes.
AtıfScopus2İncele - Kitap BölümüYayıncıda açık erişim2026Scientific Trends Towards the Use of Generative Artificial Intelligence Tools in Early Childhood STEM Education: A Bibliometric Analysis
Çakir, Zehra; Uğraş, Mustafa
This study aims to analyze scientific publications on the use of productive artificial intelligence tools in STEM education during early childhood between 2021 and 2025 using bibliometric methods. In this context, the international literature published during the relevant period was examined to identify scientific production trends, prominent authors, influential studies, institutional and international collaboration networks, and thematic focuses. A total of 1, 224 documents were included in the analysis. During the data analysis process, the Bibliometrix (Biblioshiny) software running on the R program was used; publication trends, keyword frequencies, citation analyses, author and institution collaborations, and scientific mapping techniques were utilized. The analysis findings show that academic interest in productive artificial intelligence tools has increased significantly between 2021 and 2025. The study also systematically identifies key trends, application areas, and research gaps related to the integration of these technologies into educational processes.
- MakaleAçık Erişim2026Scopus SJRQ3The Effect of Virtual Reality Training on Basic Hitting Performance in Tennis
ÇELİK, Hüsniye; TOZOĞLU, Erdoğan; AĞDUMAN, Fatih
The aim of this study was to investigate the effect of virtual reality (VR) training on basic stroke performance in tennis. In the fall semester of the 2023-2024 academic year at Atatürk University, 20 students without prior tennis experience participated. They were divided into two equal groups: the control group, which received only traditional training, and the experimental group, which received a combination of VR and traditional training. Each group included five male and five female students, all participating in an 8-week training program. A pre-test-post-test control group experimental design was used. Data were collected through a VR application and a performance test administered before and after training. Descriptive statistics and normality tests were conducted, with paired t-tests for intra-group comparisons and ANCOVA for inter-group comparisons, using SPSS 25. Results showed significant improvements in stroke performance in both groups. However, analysis revealed no statistically significant difference in forehand stroke performance between groups. In contrast, the experimental group demonstrated significant improvement in backhand performance compared to the control group. Findings suggest that both VR-based and traditional methods can effectively enhance basic stroke performance in tennis. Specifically, VR training proved particularly effective for improving backhand strokes, while no additional benefit was observed for forehand strokes compared to traditional training.
