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
- MakaleYalnız künye2025Scopus SJRQ1Exploring the transformative power of GenAI in education: an exploratory journey from social studies classes
Yetişensoy, Okan
The potential of GenAI in educational settings has attracted considerable attention, yet practical explorations of its use in K-12 classrooms are still scarce. This research aims to explore the role of GenAI models in social studies, focusing on their influence on learning-teaching processes and understanding of AI. While exploring the influence of GenAI on pedagogical aspects from the perspective of the teacher and students, this qualitative study also embraces AI as a cross-curricular theme and seeks to understand how GenAI-supported teaching activities shape students’ AI understanding. The study group of this research, conducted according to the basic qualitative research design, consisted of 21 middle school students studying in one class and a social studies teacher who implemented the practices. The primary data collection tool for the research was semi-structured interview forms. In the relevant research, a teaching framework supported by GenAI, combining “Teaching with AI” and “Teaching about AI” was developed. During the four-week process, the practitioner teacher covered the curricular outcomes using lesson plans designed in alignment with this framework. In the semi-structured interviews, students stated that engaging in activities supported by GenAI not only made the learning process more effective and interesting but also enhanced their understanding regarding AI. The teacher highlighted that GenAI models encouraged constructivist learning environments, ensured the active involvement of all students, including those in inclusive education, and contributed to AI learning through their engaging structure. Additionally, the teacher highlighted technology’s drawbacks, including encouraging dependence on convenience and potentially providing inaccurate information. The relevant findings of the study, serving as first observations, are expected to provide valuable insights for future research. At this point, it is recommended that further studies be conducted to explore the transformative potential of GenAI in education, especially by employing experimental designs. An additional suggestion, on the other hand, is to disseminate AI throughout the entire curriculum of all subjects within a specific educational level as a cross-curricular theme, as opposed to limiting its teaching to merely a singular, isolated subject area.
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İncele - MakaleAçık Erişim2025Scopus SJRQ1Validity and Reliability of a Novel AI-Based System in Athletic Performance Assessment: The Case of DeepSport †
Aydemir, Burakhan; Aydoğan, Muhammed Talha; Boz, Emre; Kul, Murat; Kırkbir, Fatih; Özkara, Abdullah Bora
This study aimed to examine the validity and reliability of the AI-based DeepSport application by comparing its outcomes with those from the reference device, OptoJump. The primary dependent variables measured were jump height and anaerobic power during vertical jump assessments. Twelve elite male basketball players voluntarily participated in the study (age = 21.53 ± 1.14 years; sports experience = 6.47 ± 1.01 years). DeepSport uses AI-based image processing from standard cameras, while OptoJump uses optical sensor technology. Both DeepSport and OptoJump systems were utilized to assess participants’ Countermovement Jump (CMJ) and Squat Jump (SJ) performances. A G*Power (version 3.1.9.7) analysis determined the required sample size, adopting a 95% confidence level, 90% test power, and an effect size of 0.25. Validity assessments were conducted using Bland-Altman plots and ordinary least products (OLP) regression analysis, while reliability was evaluated through intraclass correlation coefficient (ICC), coefficient of variation (CV), standard error of measurement (SEM), and smallest detectable change (SDC) analyses. DeepSport showed excellent reliability in CMJ and SJ tests with ICC values > 0.90, and CV ranged between 2.12% and 4.95%. Results were consistent with OptoJump, showing no significant differences according to t-test results (p > 0.05). Bland–Altman analyses indicated no systematic bias and random distribution. These findings confirm that both DeepSport and OptoJump devices demonstrate high reliability and consistency, suggesting their validity and reliability for use in athlete performance assessments by coaches and athletes.
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İncele - Derleme MakaleYalnız künye2025Scopus SJRQ3Artificial Intelligence and Environmental Impact: Moving Beyond Humanizing Vocabulary and Anthropocentrism
Karakaş, Ümit; Özdemir, Vural
Artificial intelligence (AI) and its applications in digital health, bioengineering, and society have significant material impacts on the environment owing to AI’s vast energy demands and energy consumption, carbon footprints, and water usage to cool data centers and generate electricity to power the data centers. Yet, the environmental footprints of AI remain underappreciated and inadequately acknowledged. This is significant, particularly in this era of climate emergency and ongoing threats to planetary energy and water supplies. The vocabulary attached to AI often aims to mimic positive human capacities such as “warmness” and “care.” However, these attempts to humanize AI and digital technology come with an anthropocentric gaze and blind spots that bracket out the environmental impacts and footprints of AI and privilege humans and technology over nonhuman animals and planetary ecological limits. In medicine, the environmental impacts of large language models range from water consumption and carbon emission to rare mineral usage. This commentary and innovation analysis question and queer the popular imagination of AI and digital technology as things that only exist in the immaterial world of cyberspace. In the course of research on AI in planetary health, we must be cognizant of its materiality, ecological impacts, and massive energy and water demands. We argue that moving away from anthropocentric narratives and vocabulary in AI design and praxis would bode well to live within planetary ecological limits so that AI and emerging digital technologies best serve robust and responsible science and all life on the planet Earth. Copyright 2025, Mary Ann Liebert, Inc., publishers.
AtıfScopus10WoS7OpenAlex13İncele - MakaleYalnız künye2025Scopus SJRQ1A psychometric analysis of the artificial intelligence skills scale developed through chat gpt
Görgülü, Deniz; Coşkun, Fatma; Demi̇r, Mustafa; Si̇pahi̇oğlu, Mete
This study aims to examine the psychometric properties of a scale developed to measure teachers’ ability to use artificial intelligence tools in education. The scale was originally proposed as having 33 items across 5 sub-dimensions by Chat GPT-4, an AI system. To establish content validity, the scale form was submitted to expert review. An exploratory factor analysis (EFA) was then conducted on a sample of 347 teachers to determine if the 33-item, 5-subdimensional structure proposed by Chat GPT was confirmed by empirical data. The EFA results supported Chat GPT-4’s proposed structure. A confirmatory factor analysis (CFA) was applied to a new sample of 603 teachers to assess the scale’s validity in an independent sample. The CFA results did not fully support the structure consisting of 33 items and 5 subdimensions proposed by Chat GPT. Reliability was analyzed using Cronbach’s Alpha and test-retest methods.
AtıfScopus5WoS5OpenAlex10İncele - MakaleAçık Erişim2024Scopus SJRQ1The effect of AI-powered chatbots in social studies education
Yeti̇şensoy, Okan; Karaduman, Hıdır
The aim of this research is to investigate the educational potential of AI-powered chatbots in Social Studies learning-teaching processes. The study was conducted using embedded design, evaluated within the framework of mixed methods research. The study group consists of 78 6th-grade students studying in three different classes, along with one teacher who implemented the practices and guided the process. During the experimental phase of the study, lessons in the experimental group were taught by the practitioner teacher using an AI-powered chatbot named “SosyalciBot”. Meanwhile, lessons in two control groups were taught following the constructivist approach. At the end of the process, it was determined that the experimental group students’ post-test and permanency test scores were significantly higher than the control group students’ scores in both post-test and permanency test. In the semi-structured interviews, both students and the practitioner teacher provided positive evaluations of the pedagogical and design features of the chatbot, indicating that these features positively impacted the learning-teaching process. They also identified some shortcomings while offering suggestions for improvement. Based on the study findings, it is evident that chatbots have a high potential to contribute to Social Studies education. However, to fully harness this potential and achieve optimal effectiveness, further advancements and refinements in chatbot technology are required. At this point, it is recommended to conduct theoretical or applied studies focusing on developing chatbots with high communication capacity and explore innovative and constructive ideas regarding the integration of chatbots into educational environments.
AtıfScopus58WoS37İncele - MakaleAçık Erişim2024Scopus SJRQ2Behind the Scenes: A Critical Perspective on GenAI and Open Educational Practices
Aksoy, Dilara Arzugül; Kursun, Engin
Artificial Intelligence (AI) is a rapidly evolving field that is influencing every aspect of life. Generative AI (GenAI) as a sub-branch of AI is used to create content in various formats such as text, images, video, and audio. This paper discusses the implications of GenAI for Open Educational Practices (OEP), highlighting the potential affordances and challenges. GenAI can address the challenges within the OEP by leveraging openness and ethical use. GenAI’s “generative” nature and human-like language capability can provide resources such as course material, activities, examples, questions, assessment, and learning outcomes in the context of OEP. With machine learning and deep learning infrastructure, it can support the discoverability and accessibility of open resources by increasing the metadata quality. GenAI can automatically score student assignments, answer their questions, and provide instant feedback to address the lack of interaction and feedback that arises due to the large number of students, especially in massive open online courses (MOOC). On the other hand, GenAI brings challenges such as data privacy and security, copyright, biased outputs, and the generation of false information. The conclusions emphasize the importance of a nuanced approach that considers not only the advantages but also the risks associated with adopting GenAI in the OEP world. Researching and developing how to apply these technologies to education is important to shape the future of education.
AtıfScopus27WoS18OpenAlex23İncele - MakaleAçık Erişim2023Scopus SJRQ1Investigation of student experiences with ChatGPT-supported online learning applications in higher education
Kayalı, Bünyami; Yavuz, Mehmet; Balat, Şener; Çalışan, Mücahit
The purpose of this study was to determine university students' experiences with the use of ChatGPT in online courses. The sample consisted of 84 associate degree students from a state university in Turkey. A multi-method approach was used in the study. Although quantitative data were collected using the Chatbot Usability Scale, qualitative data were collected using a semi-structured interview form that we developed. The data were analysed using descriptive and content analysis methods. According to the findings, ChatGPT exhibits advantages such as a user-friendly interface and fast, concise, relevant responses. Moreover, emphasizing its contribution to the learning process, the information provided was sufficient and topic-oriented. The understandability of the chatbot’s functions and the clarity of their communication were emphasized. However, there are disadvantages such as performance issues, frequency of errors and the risk of providing misleading information. Concerns have also been raised about the potential difficulties chatbots may face in ambiguous conversations and providing insufficient information on privacy issues. In conclusion, ChatGPT is recognised as a potentially valuable tool in education based on positive usability impressions; however, more research is needed for its safe use. Implications for practice or policy • Based on positive usability impressions, students and instructors can use ChatGPT to support educational activities. • ChatGPT can promote and enhance students' personalised learning experiences. • ChatGPT can be used in all higher education courses. • Users should be cautious about the accuracy and reliability of the answers provided by ChatGPT. • Decision-makers should take precautions against risks such as privacy, ethics, confidentiality and security that may arise from using artificial intelligence in education.
AtıfScopus79WoS48OpenAlex76İncele