Meltem Eryilmaz

This study examines the ethical dimensions of artificial intelligence (AI) and explores the awareness and understanding of how users interact with AI and the potential consequences of these interactions. In recent years, growing awareness of the risks of AI has driven the development of ethical guidelines by various organizations. These guidelines aim to ensure that AI is developed and deployed in a responsible manner, with a focus on human well-being, societal benefits and minimizing risks. However, despite this global movement, there is a lack of consensus on key ethical issues such as bias, discrimination, privacy and human rights issues. The study focuses on the perceptions of 127 participants from 11 countries with diverse professional backgrounds in technology, education and finance. A survey with a 5-point Likert scale was used to assess participants’ attitudes towards AI ethics in relation to various topics such as transparency. The study examines differences in responses across professions and countries using Multivariate Analysis of Variance (MANOVA) test. The results reveal variations in ethical priorities, suggesting that while global ethical frameworks are emerging, further efforts are needed to achieve uniformity in AI ethical standards. The findings emphasize the importance of increasing awareness and understanding of AI ethics to mitigate potential harms.

The Common Correlated Effect (CCE) estimator is widely used in panel data models to address cross-sectional dependence, particularly in nonstationary panels. However, existing estimators have limitations, especially in small-sample settings. This study refines the CCE estimator by introducing new proxy variables and testing them through a comprehensive set of simulations. The proposed method is simple yet effective, aiming to improve the handling of cross-sectional dependence. Simulation results show that the refined estimator eliminates cross-sectional dependence more effectively than the original CCE, with improved power properties under both weak- and strong-dependence scenarios. The refined estimator performs particularly well in small sample sizes. These findings offer a more robust framework for panel unit root testing, enhancing the reliability of CCE estimators and contributing to further developments in addressing cross-sectional dependence in panel data models.

This study aims to enable deaf and hard-of-hearing people to communicate with other individuals who know and do not know sign language. The mobile application was developed for video classification by using MediaPipe Library in the study. While doing this, considering the problems that deaf and hearing loss individuals face in Turkey and abroad modelling and training stages were carried out with the English language option. With the real-time translation feature added to the study individuals were provided with instant communication. In this way, communication problems experienced by hearing-impaired individuals will be greatly reduced. Machine learning and Deep learning concepts were investigated in the study. Model creation and training stages were carried out using VGG16, OpenCV, Pandas, Keras, and Os libraries. Due to the low success rate in the model created using VGG16, the MediaPipe library was used in the formation and training stages of the model. The reason for this is that, thanks to the solutions available in the MediaPipe library, it can normalise the coordinates in 3D by marking the regions to be detected in the human body. Being able to extract the coordinates independently of the background and body type in the videos in the dataset increases the success rate of the model in the formation and training stages. As a result of an experiment, the accuracy rate of the deep learning model is 85% and the application can be easily integrated with different languages. It is concluded that deep learning model is more accure than machine learning one and the communication problem faced by hearing-impaired individuals in many countries can be reduced easily.

It has been known that smartphones are the first thing that comes to mind when technology is mentioned. Almost every person has a smartphone, and they are used for social media, shopping, trade, and more. In the past, phones were just used for calculating something, or text messaging each other. However, nowadays, as mentioned above, they are used for complicated applications or works. Therefore, users need security for their private information. The Biletini Devret application in this study keeps users' private information secure with the help of Google Cloud Platforms and this application has two-factor verification to be more secure and to prevent unauthorized users. In particular, the Biletini Devret application has a Face Recognition System which has the most reliable authentication system all in the world.

In this experimental study, an intelligent tutoring system called the fuzzy Bayesian intelligent tutoring system (FB-ITS), is developed by using artificial intelligence methods based on fuzzy logic and the Bayesian network technique to adaptively support students in learning environments. The effectiveness of the FB-ITS was evaluated by comparing it with two other versions of an Intelligent Tutoring System (ITS), fuzzy ITS and Bayesian ITS, separately. Moreover, it was evaluated by comparing it with an existing traditional e-learning system. In order to evaluate whether the academic performance of the students in different learning groups differs or not, analysis of covariance (ANCOVA) was used based on the students’ pre-test and post-test scores. The study was conducted with 120 undergraduate university students. Results showed that students who studied using FB-ITS had significantly higher academic performance on average compared to other students who studied with the other systems. Regarding the time taken to perform the post-test, the results indicated that students who used the FB-ITS needed less time on average compared to students who used the traditional e-learning system. From the results, it could be concluded that the new system contributed in terms of the speed of performing the final exam and high academic success.

The purpose of this study is to differentiate the effect of cooperative learning strategy integrated with a flipped learning (FL) model from sole FL implementation in promoting students’ performances while decreasing their social and computer anxiety in an undergraduate course. As a method, a classical experimental design is used. The participants were from the department of English Language and Literature, and Translation and Interpretation. Students were randomly assigned to individual FL (the control group) class; and FL with cooperative activities (experimental group) class. The groups were randomly assigned as experimental and control by tossing a coin. The implementation took 10 weeks. Students’ performances (grades), social anxiety, and computer anxiety were dependent variables of the study and they were compared through multivariate analysis of variance. The results indicated that there is no significant mean difference between groups’ performances; however; the group of FL with cooperative activities had less social anxiety, but no significant change occurred at their computer anxiety level.

The Effectiveness Of Blended Learning Environments