This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, as well as build trust through the need for visibility and explainability by increasing user acceptance. This study primarily examines the nature of AI-based DSS adoption and the challenges of maintaining system transparency and improving accuracy. The results provide practical guidance for professionals and decision-makers to develop AI-driven decision support systems that are not only effective but also trusted by users. The results are also important to gain insight into how artificial intelligence fits into and combines with decision-making, which can be derived from research when thinking about embedding systems in ethical standards.

The emergence of ChatGPT, a high-performance artificial intelligence language model developed by OpenAI, has generated both excitement and concern in academia (Li, 2024). Equipped with advanced natural language processing techniques, ChatGPT is able to generate human-like text that provides coherent and contextually relevant responses to a wide range of queries. This unprecedented capability has raised optimism and concern as it could fundamentally change traditional practices in academia, industry and everyday life (Cambra-Fierro et al., 2024).The basic function of "ask me anything" and "I might have a good answer" is no longer just a concern in many fields. The scientific knowledge disseminated in journals is already struggling with the role that such technology will play. Questions arise about whether it will be, and can be, coauthored (Tang, 2024). Professors who create knowledge immediately face the challenge of assessing students in the presence of such technology. These are practical and legitimate questions.While ChatGPT has many benefits in terms of increased student engagement, collaboration and accessibility outcomes, it also has very serious academic integrity implications: at its core is plagiarism. This paper offers comprehensive strategies on how educators can help mitigate these risks by promoting ethical use and fairness within the academic use of AI tools.ChatGPT was truly disruptive, which should have surprised no one. It can be seen that these technologies are being adopted very quickly from university labs; ChatGPT reached one million users in its first five days and now has over 180 million (Duarte, 2024). This kind of rapid adoption demonstrates a remarkable property of generative AI: that it persists with coherent and contextually relevant text.One of the main problems with AI models like ChatGPT is the range of threats they pose, including black box algorithms, including black box algorithms, discrimination, biases, vulgarity, copyright infringement -plagiarism -and many others, such as the generation of fake text content or fake media (Sloan, Powis & Tan, 2024). Therefore, organisations need disciplined risk management approaches to effectively address these threats. Considering the continuous evolution of artificial intelligence algorithms due to the rapidity of data sources, the review of heterogeneity and variability bias in periodic risk assessments should also be weighed against ethical considerations (Schwartz et al., 2022).The experience was that the resulting text lacked an obvious logical structure, contained speculative information, did not elaborate on critical data, and did not provide original contributions (Giuggioli & Pellegrini 2023). Any article on the topic would be conventional, lack logic and facts, and would not be critically engaging. In addition, ChatGPT references are generally incorrect; titles and authors, as well as other publication details, are misstated. Such inaccuracies require careful double-checking, especially in professional contexts such as journalism and software development.Inaccuracy, poor logical flow, factual inaccuracies, lack of critical analysis and lack of originality of AI-generated content can result from the current state of technology (Yang, 2024). This is based on deep learning models that are trained using very extensive datasets of prior information that may be outdated or of low quality. Although improvements in training models and data quality may improve the performance of AIs, it is not clear that improvements based on technical level necessarily lead to significant gains in innovation (Dwivedi et al., 2023).The recent applications of generative AI in text, film and music production all indicate that these platforms will at best be partners in the innovation process, complementing rather than replacing human intelligence. In the case of complex activities requiring creativity and emotional intelligence, a well-formulated request alone is not sufficient for AI to produce markedly different and original outputs. Human oversight and collaboration remain essential (Liu, 2024). Research, practice, and urgent policy decisions in an era of rapidly evolving AI technologies require researchers, practitioners, and policymakers to critically engage with these changes. Building on the strengths of AI, while being aware of its limitations and making serious efforts to improve them, will foster an environment in which generative AI tools such as ChatGPT are used responsibly and effectively.Integrating ChatGPT into the scientific environment is not without its challenges. The primary concern is the possibility of plagiarism. Students may get used to using ChatGPT to create essays and assignments, which they then submit as their own work. This undermines the educational process and devalues academic credentials. Another challenge is the potential for inequality. Students who have access to ChatGPT can complete assignments in much less time and possibly better, giving them an unfair advantage over students who do not have ChatGPT. This may further increase existing inequalities in educational outcomes. On the other hand, it is difficult to distinguish content created by students from content created by AI. Because ChatGPT generates human-like, coherent text, the difficulty of distinguishing it from the "original" student content makes it difficult for educators to detect AI-assisted plagiarism.While this work focuses on addressing the risks of plagiarism, ChatGPT and other AI tools hold great promise for improving learning outcomes and stimulating creativity. Through adaptive tutoring systems, these tools can improve personalized learning, provide immediate feedback and facilitate deeper interaction with course material. Furthermore, AI-driven creative applications allow students to experiment with problem-solving and critical thinking in new ways, ultimately resulting in a more dynamic and engaging learning...

This article presents the development, implementation and testing of a brain-computer interface (BCI) system, which enables the speed control of the mobile robot called Robotino, manufactured by Festo Didactic. The BCI system was implemented, and the results of the BCI system were evaluated during a students' project, based on the project-based learning methodology. Speed control has been achieved by utilization of NeuroSky MindWave EEG headset-based electroencephalogram (EEG) method, by processing brain bioelectric signals measured on the frontal lobe. Tests of the system evolved by using the brain-computer interface have been performed and evaluated, both regarding implementing speed control and user's experiences, which have been finished with positive results.

In human brain, either in the state of wakefulness or sleeping, different frequency changes are observable in the spectrum of measured electric signals of the brain. Regarding the frequency components of these signals that occur as a consequence of this electric activity, different brain waves can be distinguished. The electric impulse alternations that generated during the operation of neurons can be measured by the EEG (electroencephalograph) device. In the past, devices that could measure these brain signal alterations were mainly used in medicine. However, in the past few years cheaper and user friendly brainwave signal processing units have become available and the use of this technology has expanded. In this paper, the so-called Brain Computer Interface unit will be presented that was developed for further brain wave analysis and ensures the detection of brain waves. Moreover, an application for visualization and further procession of the measured and processed signals will also be described. The application can be used for EEG data acquisition, processing and visualization which could be the base of several further researches.

Keeping up with novel methods and keeping abreast of new applications are crucial issues in engineering education. In brain research, one of the most significant research areas in recent decades, many developments have application in both modern engineering technology and education. New measurement methods in the observation of brain activity open a new frontier in engineering applications. Electroencephalogram (EEG)-based brain activity observation processes are very promising and have been used in several engineering studies, primarily for the implementation of control tasks. This paper presents the development, implementation, and assessment of an EEG-based engineering education project, in which engineering students applied the theory they had learned and improved their knowledge and skills in the area of observation and evaluation of electrical signals generated by brain activity and measured by biosensors. The main project goal was to develop and test a brain-computer interface that is able to measure the average attention level. The effectiveness of this project-based learning was evaluated by student questionnaire responses and analysis of students' exam results; students who had participated in the project were shown to have higher levels of acquired knowledge.