Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Yavuz, Mehmet(7)
- Kayalı, Bünyami(4)
- Bozkurt, Aras(3)
- Yetişensoy, Okan(3)
- Üstün, Ayşin Gaye(3)
- Erdoğdu, Erdem(2)
- Kayali, Bunyami(2)
- Uçar, Hasan(2)
- Aksoy, Dilara Arzugül(1)
- Aydemir, Burakhan(1)
- Aydemir, Uğur(1)
- Aydoğan, Muhammed Talha(1)
- Balat, Sener(1)
- Balat, Şener(1)
- Belibağlı, Mehmet Cenk(1)
- Bilge, Huzeyfe(1)
- Bostan Sarioğlan, Ayberk(1)
- Bostanci, Meric Emre(1)
- Boz, Emre(1)
- Coşkun, Fatma(1)
- Demi̇r, Mustafa(1)
- Dolanbay, Nihal(1)
- Gökmen, Mehmet Yiğit(1)
- Gönen, Mevlut(1)
- Görgülü, Deniz(1)
- Gülden, Bahadir(1)
- Günes, Huseyin(1)
- Gürbüz, Fatih(1)
- Güzel, Sema(1)
- Kaplan, Kadir(1)
- Kara, Abdulkadir(1)
- Kara, Zeynep Avinc(1)
- Karaduman, Hıdır(1)
- Karakaş, Ümit(1)
- Karıncaoğlu, Ergin(1)
- Kayali, Bünyami(1)
- Kul, Murat(1)
- Kursun, Engin(1)
- Köktürk, Seda Nur(1)
- Kırkbir, Fatih(1)
Yayınlar
- Doktora TeziAçık Erişim2026Yapay zekâ destekli yazma eğitiminin 7. sınıf öğrencilerinin yazma başarısına ve öz yeterlik algısına etkisi: Bir karma yöntem araştırması
Murat Kaya
Araştırmada, yapay zekâ destekli yazma eğitiminin ortaokul 7. sınıf öğrencilerinin yazma başarı ve öz yeterliğine etkisini incelemek amaçlanmıştır. Çalışmada karma yöntem araştırmalarından açıklayıcı sıralı desen kullanılmıştır. Araştırmanın nicel bölümünde ön test son test eşleştirilmiş kontrol gruplu yarı deneysel desen; nitel bölümünde ise olgubilim deseni kullanılmıştır. Araştırmanın çalışma grubunu, Giresun'daki bir ortaokulda 7. sınıfta öğrenim göre 34 öğrenci oluşturmaktadır. Araştırma, 2023-2024 Eğitim-Öğretim yılında gerçekleştirilmiştir. Araştırmanın çalışma grubunun belirlenmesinde seçkisiz olmayan örnekleme yöntemlerinden amaçsal örnekleme yöntemi tercih edilmiştir. Çalışmanın uygulama süreci toplam 20 hafta ve yüz yüze gerçekleştirilmiştir. Araştırmanın verileri "Kişisel bilgi formu, elektronik yazma alışkanlıkları ölçeği, bilgilendirici metin yazma başarısını değerlendirme ölçeği, öyküleyici metin yazma düzeylerini belirlemek için kullanılan dereceli puanlama anahtarı, ortaokul öğrencilerine yönelik yazma öz yeterlik ölçeği ve yarı yapılandırılmış görüşme formu" kullanılarak elde edilmiştir. Nicel veriler SPSS 26, nitel veriler ise MAXQDA 22 programı ile analiz edilmiştir. Nicel verilerden elde edilen bulgulara göre yapay zekâ destekli yazma eğitiminin deney grubu öğrencilerin bilgilendirici ve öyküleyici metin yazma başarılarını artırdığı görülmüştür. Ayrıca öğrencilerin yazma öz yeterliklerinin de uygulama sonrasında arttığı ortaya çıkmıştır. Nitel verilerden elde edilen bulgulara göre ChatGPT yapay zekâ aracının öğrencilerin yazma deneyimlerine olumlu yansıdığı, yazmayı kolaylaştırdığı, yazım hatalarını azalttığı, yazmaya karşı motive ettiği ve yazma endişelerini azalttığı öne çıkan öğrenci görüşleri arasındadır. Sonuç olarak yapay zekâ destekli yazma eğitiminin ortaokul 7. sınıf öğrencilerinin yazma başarılarına ve öz yeterliklerine katkı sunduğu sonucuna ulaşılmıştır. Öğrencilerin doğru istemler oluşturma sürecine yönelik eğitimler verilmesi ve farklı yapay zekâ araçlarıyla da yazma eğitimi çalışmalarının yapılması noktaında öneriler sunulabilir.
- MakaleAçık Erişim2026Statistical and qualitative analysis of ChatGPT and human raters in preservice teachers' writing assessment
Gülden, Bahadir; Bilge, Huzeyfe; Uysal, Pinar Kanik
Teachers spend a significant amount of time providing feedback. This study compared expert and ChatGPT assessments and feedback on written texts to determine the suitability of AI for writing skill assessments that are time-consuming to assess and provide feedback. Three experts and ChatGPT graded 14 Turkish undergraduate students' assignments using rubric that included content, language use, vocabulary, organization, and mechanics, and justified their decisions. The study involved document review and triangulation, a qualitative design. In addition, an intraclass correlation coefficient was used to assess the consistency of the ChatGPT and the experts' scores. All feedback was qualitatively analyzed to identify the strengths and weaknesses of the experts and their similarities with ChatGPT. Experts and ChatGPT had moderate to weak consistency in the writing subscales, while good reliability was found in the total score. Experts excelled in 'explanatory feedback', 'interpretation' and 'experience', while ChatGPT excelled in 'automation and continuity' and 'data processing capacity'. Experts' weaknesses included 'limited time and energy' and 'comparison bias', while ChatGPT's weaknesses were 'ambiguous expressions' and 'repetition'. The study also found that experts and ChatGPT preferred to provide constructive and supportive feedback.
AtıfWoS1OpenAlex2
İncele - MakaleAçık Erişim2026Scopus SJRQ1Physical Activity, Technology Use, and AI Anxiety Among University Students: Associations With Academic Integrity Attitudes
Yaman, Menzure Sibel; Çakir, Zekai; Çatikkas, Fatih; Türkmen, Mutlu; Sengönül, Abdullah; Gönen, Mevlut; Öktem, Tuncay; Güzel, Sema; Yel, Kader
This study examined the associations between university students' physical activity, technology use habits, and anxiety related to artificial intelligence (AI) within an academic integrity context. Since the study did not measure verified misconduct, academic integrity was conceptualised as attitudes and self-reported behaviours. Perceptions of uncited AI use and citation practices were therefore defined as the primary integrity-related outcomes. A survey was completed by 1,018 students (TUHS, AIAS), complemented by focus group interviews with 25 participants. Quantitative analyses (t-tests, ANOVA, regression) indicated that regular physical activity was associated with lower self-reported technology dependence and lower AI anxiety. Non-active female students and younger students (18-22) recorded the highest scores. Daily internet use exceeding six hours and social media-focused engagement were associated with greater dependence and anxiety. Qualitative findings provided explanatory depth: physically active students described a more routine, focused, and self-regulated approach, while inactive students emphasised longer screen time, distraction, and stress. These patterns were consistent with the quantitative results. Overall, the findings suggest that balanced technology use, physical activity, and clear ethical awareness (e.g., citation behaviour) are associated with lower technology dependence and AI anxiety. Integrating digital literacy and ethics training with programmes that encourage physical activity may support responsible technology use and integrity-related awareness in higher education. Findings are interpreted as patterns of co-occurrence rather than causal effects, because the cross-sectional design does not allow directionality.
- MakaleAçık Erişim2026Scopus SJRQ1Ecological and nutritional analysis of artificial intelligence-based sustainable dietary models
Köktürk, Seda Nur; Yardımcı, Hülya
Growing concerns over environmental sustainability and health have increased the demand for sustainable diets. Advances in artificial intelligence (AI) offer new opportunities for diet planning. Evaluating the nutritional adequacy and environmental impact of AI-generated sustainable diets is essential. This study aimed to compare the nutritional quality and ecological footprints of sustainable diets created by four popular AI tools (Gemini, Copilot, ChatGPT, and Grok). Each AI tool generated a sustainable diet plan for a healthy adult woman. Diets were evaluated for energy, nutrient content, antioxidant capacity, glycemic index, protein quality, fatty acid profiles, Healthy Eating Index-2015 scores, and ecological footprints. All diets showed low ecological footprints but did not meet energy targets (1800 kcal/day). Gemini had the most sustainable composition. Vitamin B12 was consistently below recommended levels in diets from Gemini, Copilot, and ChatGPT. All diets had adequate carbohydrate, protein, fat quality, and overall diet quality. Carbon footprint associated with protein, energy from protein, cholesterol, pyridoxine, folate, and vitamin C. Water footprint was linked to multiple nutrients, and fruits, meat, poultry, and fish; vegetable intake correlated with carbon footprint. AI-assisted diets have potential for sustainability but nutritional completeness concerns remain, especially for vulnerable groups. Further studies comparing AI and dietitian plans are needed to assess long-term effects.
AtıfScopus1WoS1OpenAlex3
İncele - MakaleAçık Erişim2026Scopus SJRQ1How accurately do large Language models interpret sport safeguarding principles: an evaluation using the International Olympic Committee framework
Gökmen, Mehmet Yiğit; Belibağlı, Mehmet Cenk; Karıncaoğlu, Ergin; Nazlıcan, Mahmut Çağatay; Türkmen, Eren; Yönal, Mehmet; Uluöz, Eren
Background: Safeguarding in sport, defined as the prevention of abuse, harassment, and exploitation, has become a core ethical responsibility for sport organizations. With the growing influence of artificial intelligence, large language models are increasingly being used to interpret, summarize, and operationalize policy documents. However, their reasoning fidelity in value-laden contexts such as athlete protection remains uncertain. Methods: This document-based comparative study evaluated two advanced large language models, ChatGPT (OpenAI) and NotebookLM (Google), against twenty-five decision points derived from the 2024 International Olympic Committee Consensus on Interpersonal Violence and Safeguarding in Sport. Responses were independently scored by two evaluators across four dimensions: accuracy, ethical reasoning, applicability, and responsibility awareness. Quantitative and qualitative analyses assessed concordance with IOC recommendations, inter-rater reliability, and reasoning patterns. Structured PICO-style prompts were used to ensure standardized and comparable model interrogation across all decision points. Results: ChatGPT achieved a higher mean composite score (3.84 ± 0.41) than NotebookLM (3.32 ± 0.77) (Wilcoxon Z = 3.41, p = 0.001, r = 0.68; n = 25 paired decision points), with full concordance in 21/25 (84.0%) versus 14/25 (56.0%) decision points. Inter-rater agreement was excellent (Cohen’s kappa = 0.89). ChatGPT demonstrated stronger procedural reasoning and clearer alignment with IOC-defined responsibilities, while NotebookLM emphasized empathy and cultural nuance. Both models underperformed in survivor-centred reasoning and outcome evaluation metrics. Conclusion: LLMs can partially reproduce the ethical and procedural logic embedded in sport safeguarding frameworks, but their reasoning remains incomplete without human oversight. Their integration into governance, education, and policy translation should follow clear ethical guardrails to prevent misinterpretation or amplification of harm. These systems may support policy drafting, education, and communication, yet they should function strictly as augmentative tools rather than independent decision-makers. To our knowledge, this is the first study to systematically benchmark LLM reasoning against an official international safeguarding framework in sport. Future research should evaluate interactive and multilingual applications to enhance contextual accuracy and equitable safeguarding outcomes.
AtıfScopus1WoS1
İncele - Kitap BölümüAçık Erişim2026Scopus SJRQ4Turkish 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.
- MakaleAçık Erişim2026Scopus SJRQ1Parents’ perspectives on the use of artificial intelligence chatbots to promote physical activity among individuals with autism spectrum disorder
Yarımkaya, Erkan; Aydemir, Uğur
Purpose: Advances in artificial intelligence (AI) have led to the increasing use of chatbots to promote physical activity (PA) through personalised guidance and interactive feedback. However, little is known about how AI chatbots can support parents of individuals with autism spectrum disorder (ASD) in promoting home-based PA. Understanding parents’ perspectives on the use of AI chatbots to promote PA may be critical for designing practical guidance and interventions for individuals with ASD. Therefore, the present study aimed to examine parents’ perspectives on the use of AI chatbots to promote PA among individuals with ASD using the Theory of Planned Behaviour (TpB). Method: Participants in this descriptive qualitative study were 16 parents (13 females and 3 males) who participated in one-on-one semi-structured interviews. The interviews were transcribed for analysis and later analysed using thematic line-by-line analysis. Results: The findings revealed that parents had positive attitudes towards the use of AI chatbots to promote PA among individuals with ASD. According to parents, AI chatbots were useful for learning new information about PA and increasing PA levels among individuals with ASD. On the other hand, parents stated that AI chatbots did not provide much detail about some PAs and that they did not understand some PA terms. Therefore, parents stated that they repeatedly gave AI chatbots commands/prompts for each PA and often needed expert support. Conclusions: Results indicated a need for guidance to parents on the use of AI chatbots to promote PA among individuals with ASD.
- 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.
- 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 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.
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 - MakaleYayıncıda açık erişim2025Answer-based and reference-based BERT models for automatic scoring of Turkish short answers: The decisive role of task complexity
Kara, Abdulkadir; Kara, Zeynep Avinc; Yildirim, Serkan
In measurement and evaluation processes, natural language responses are often avoided due to time, workload, and reliability concerns. However, the increasing popularity of automatic short-answer grading studies for natural language responses means such answers can now be measured more quickly and reliably. This study aims to build models for predicting automatic short answer scores using the pre-trained BERT deep learning language model and to reveal their effectiveness. For this purpose, two different score prediction models were created using an answerbased approach that aligns student answers with expert judgements and a referencebased approach that matches student answers with reference answers. The dataset includes answers from 246 Physics department students responding to 4 physicsrelated questions. The performance of these models was evaluated on four physics questions representing varying levels of cognitive complexity, using Cohen's Kappa for statistical comparison of agreement with expert scores. Our findings reveal a clear interaction between model architecture and task complexity. The answer-based model was unequivocally superior for the most complex, multi-class task, effectively capturing diverse, nuanced responses. Conversely, the reference-based model demonstrated a statistically significant advantage for a well-defined, mediumcomplexity binary task. This study concludes that the optimal model for ASAG in Turkish is contingent on the cognitive demands of the assessment task, suggesting that a onesize-fits-all solution may not be the most effective approach. This provides a critical framework for practitioners, demonstrating not only that effective models are feasible for complex languages, but that their selection must be guided by task complexity.
AtıfWoS1OpenAlex2İncele - 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 Erişim2025INVESTIGATION OF SURGICAL NURSES' ANXIETY AND GENERAL ATTITUDES TOWARDS ARTIFICAL INTELLIGENCE
Günes, Huseyin; Okutan, Serafettin; Dolanbay, Nihal; Bostanci, Meric Emre
Purpose: This study was conducted to determine the general anxiety and attitudes of surgical nurses toward artificial intelligence. Method: This study was designed as descriptive and correlational with the participation of 335 surgical nurses working in two different hospitals in the Eastern and Central Anatolia regions of Turkey between 06.02.2025-01.06.2025 dates. Results: The scores obtained from the positive and negative attitude sub-dimensions of the General Attitudes towards Artificial Intelligence Scale are moderate (40.24 +/- 8.94, 23.65 +/- 6.61). The total score of the Artificial Intelligence Anxiety Scale is moderate (46.09 +/- 13.65). A low level of negative correlation was found between the positive attitude scores of the General Attitudes towards Artificial Intelligence Scale and the total scores of the Artificial Intelligence Anxiety Scale (r=-0.239, p<0.001). A high level of positive correlation was found between the negative attitude scores of the General Attitudes towards Artificial Intelligence Scale and the total scores of the Artificial Intelligence Anxiety Scale (r=0.710, p<0.001). In the multiple linear regression model established, it was seen that 53% of the anxiety toward artificial intelligence was explained by the general attitudes towards artificial intelligence variable, which is the independent variable in the model (R2=0.530). Conclusion: As the positive attitudes of surgical nurses toward artificial intelligence increase, their anxiety levels toward artificial intelligence decrease. It is necessary to provide continuous education and support to surgical nurses about artificial intelligence applications.
AtıfTR Dizin1İncele - MakaleAçık Erişim2025Scopus SJRQ3The Comparison of Pre-Service Science Teachers' Drawings of the “Universe” Concept and the Universe Visuals They Created from Artificial Intelligence
Bostan Sarioğlan, Ayberk; Gürbüz, Fatih; Yakupoğlu, Melike
As the use of artificial intelligence increases in areas where people are active in our daily lives such as education and health, the comparison of human and artificial intelligence has started to be discussed. Many artificial intelligence programmes are commanded by humans and artificial intelligence creates products in line with expectations. In this study, it is aimed to compare the drawings of pre-service science teachers about the concept of universe with the universe visuals created by artificial intelligence. Case study was selected as the research model. The study group consisted of 24 pre-service science teachers studying in the 2nd and 4th grades of science teaching. The worksheet prepared by the researchers and the received expert opinion was used as a data collection tool. During the application process, pre-service teachers made drawings related to the concept of universe and described these drawings during two lesson hours. Then, they made drawings related to the concept of universe of artificial intelligence with the descriptions they entered into Microsoft Bing Search application. As a result, it was determined that pre-service teachers had different views in their drawings about the concept of universe. It is seen that the drawings of the pre-service teachers about the concept of universe and the visuals created by artificial intelligence are mostly like each other. In half of the drawings, the universe drawings of the pre-service teachers were more comprehensive, while in half of the drawings, artificial intelligence made more comprehensive drawings. Based on this result, it can be said that human perception and artificial intelligence products are in parallel with each other. Various suggestions were presented in line with the results of the study.
- Kitap BölümüAçık Erişim2025Beyond Algorithms: Unveiling the Intersection of AI Ethics, Citizenship Education, and Social Studies
Yetişensoy, Okan; Zajda, J; Rapoport, A
The rapid rise of artificial intelligence (AI) in recent years has led to various positive transformations in everyday life. However, this rise has also brought about the emergence of some ethical issues that raise concerns in society. Examples of these ethical issues include the manipulative or harmful use of AI, privacy concerns, bias, discrimination, and lack of transparency and accountability. AI ethics, on the other hand, represent a set of guiding principles that stakeholders should adopt to ensure the responsible development and utilization of AI technologies. Considering that young people are potential users and developers of AI, teaching them these principles from an early age can be seen as a natural part of citizenship education. Thus, incorporating AI ethics-related content into the curriculum of various K-12 courses can be regarded as an effective approach. This chapter argued that social studies, with its focus on fostering effective citizens for the future, will emerge as a crucial application area for this approach. At this point, the relevant study aims to explore the intersections between AI ethics, citizenship education, and social studies. It is believed that the study will make a significant contribution to the literature by revealing the new dynamics created by AI in citizenship education and demonstrating the potential role that the social studies curriculum can play in this regard.
AtıfScopus2WoS2OpenAlex2İncele - MakaleYalnız künye2025Scopus SJRQ1Artificial intelligence, automation and employment dynamics: empirical evidence from G7 economies
Okur, Fatih; Özdemir, Enes
Purpose – This study examines how the rapid adoption of artificial intelligence (AI) and automation affects employment dynamics across G7 economies. While previous research has often focused on either AI or robotics in isolation, their combined and long-term effects on employment remain poorly understood. Addressing this gap is crucial for policymakers seeking to balance technological progress with labor market stability. Design/methodology/approach – Using a balanced panel dataset covering 2010–2024 for the G7 countries, the study investigates the relationships between AI investment (proxied by information and communication technology (ICT) investment), robot density (ROBOT), wages, productivity (PRD) and education spending (EDU), and their impact on employment. The analysis employs panel unit root and cross-sectional dependence tests, a panel autoregressive distributed lag (ARDL) framework estimated via the pooled mean group (PMG) estimator, and robustness checks using Driscoll–Kraay fixed effects, common correlated effects (CCE) estimators, country-specific regressions and Dumitrescu–Hurlin panel causality tests. Findings – The results reveal that AI investment has a significant negative effect on employment in the long run, whereas ROBOT shows a positive but context-dependent relationship. Wage levels are negatively associated with employment, while PRD shows only a modest positive influence. Education expenditure exhibits mixed behavior – positive in the short run but negative in the long run – suggesting potential misalignment with evolving labor market needs. Causality tests confirm a unidirectional link from AI investment to employment, underscoring its structural role in labor market change. Research limitations/implications – This study is limited by data availability, particularly the lack of detailed sectoral or occupational breakdowns across countries. As a result, it cannot fully capture the distributional effects of AI and automation across different worker groups. The use of proxies, such as ICT investment for AI, may not reflect the full scope of AI deployment. Despite these limitations, the findings highlight important macro-level dynamics and suggest that technological investments significantly shape employment trends. Future research should utilize micro-level data to explore sector-specific impacts, wage effects and labor force transitions in response to digital transformation. Practical implications – The findings suggest that without targeted policy interventions, increased AI investment may displace workers in the long run. Policymakers should prioritize reskilling, adapt education systems to evolving technological needs, and differentiate strategies across sectors and worker skill levels. Social implications – This study highlights the potential for AI and automation to reshape labor markets, with implications for income distribution, job security and social cohesion. The displacement of routine jobs may disproportionately affect low-skilled and vulnerable workers, increasing the risk of inequality and social exclusion. To prevent deepening divides, social policies must focus on equitable access to education, digital literacy and lifelong learning. Supporting workforce adaptability through inclusive training programs and social safety nets is essential. The results underscore the urgent need for collaborative efforts between governments, educational institutions and industries to ensure a socially sustainable digital transformation. Originality/value – This study is among the first to jointly analyze AI and robotics within a dynamic panel framework, offering new cross-country evidence on their heterogeneous employment effects in advanced economies. By integrating multiple estimation strategies and country-specific perspectives, the paper contributes to a more nuanced understanding of how technological transformation reshapes labor markets and highlights the institutional conditions that mediate these effects.
AtıfScopus5WoS5OpenAlex3İncele