Conference Agenda

Education has been heavily affected by the introduction of Generative Artificial Intelligence (GAI), such as ChatGPT and Gemini. Due to its recency, it is important to understand the public perspective on GAI and to assess its benefits and drawbacks on education. A number of studies have been conducted, but they mostly rely on academic literature reviews. While such studies are reliable, they might be less timely than those relying on social media or news. The findings might not be holistic. Therefore, we employ three different content sources to capture the holistic perspective of GAI: social media (X/Twitter), news (Google News), and academic literature (Google Scholar). A total of 3932 contents are analysed via sentiment and thematic analyses. We found that GAI has promising benefits like improved work productivity and access to vast amounts of information. The use might be encouraged in education. However, some concerns have been raised, including misuse and over-reliance. Penalty and identification mechanisms might be needed. It is also expected to prohibit the use on some assessments to prevent over-reliance. Educational stakeholders could integrate GAI in their learning environments with clear policies.

The modern development of artificial intelligence (AI) resembles scenes from science fiction movies, which are now becoming a reality. Each of us has the ability to generate informational content with just a word, symbol, or even sound, and by enhancing text prompts with images, we can achieve the desired results more effectively. This paper explores the generative capabilities of AI and their application in optimizing project actions for the training of engineer-designers. The primary focus of the research is the analysis of key technologies, such as neural networks and deep learning algorithms, which underlie generative models like GPT, DALL·E, and others.

The study highlights the use of generative AI in fields such as creative industries, design, web development, and programming, showcasing its ability to improve the processes of creating new ideas and products. A separate section discusses the optimization of business processes through generative AI, where models can automatically generate new scenarios, projects, or even proposals to enhance productivity. Equally important is the review of AI-based optimization, which can contribute to reducing costs and development time while improving the quality of the final product.

The paper also examines the challenges faced by generative AI, including ethical concerns and questions of responsibility. Overall, the research demonstrates the significant potential of generative AI as a powerful tool for optimizing complex systems and processes, offering new opportunities for innovation and resource savings, and becoming an effective instrument for modern professionals.

CONTEXT

The growing popularization of artificial intelligence (AI) has generated debate about its impact on higher education. AI tools such as ChatGPT and Bard offer new possibilities for teaching and learning, but they also raise concerns regarding technology dependency, lack of human interaction, and the potential for plagiarism.

This study will analyze the use of AI tools in higher education, exploring students' perceptions about their benefits and risks, their purposes for use and their influence on the teaching-learning process. For this study, a questionnaire was used with a sample of higher education students, which collected information about the AI tools they use, their frequency of use, their levels of satisfaction and their expectations regarding the future.

PURPOSE OR GOAL

This study aims to analyze the use of AI tools in higher education, based on student perceptions, with the following intents: Identifying the most popular AI tools among students; Understanding the primary purposes of using AI tools; Assessing the benefits and risks students perceive about using AI tools; Analyzing the relationship between the use of AI tools and academic performance; Investigate students’ expectations regarding the future of AI in teaching.

APPROACH

An online questionnaire was used to collect data and was applied to a group of higher education students. The questionnaire covered topics such as the frequency of use of AI tools, their intended use, perceived benefits and risks, satisfaction levels and expectations regarding the future of AI. The collected data was analyzed using quantitative and qualitative methods. Quantitative analysis allowed the identification of patterns and trends in using AI tools. Qualitative analysis allowed the exploration of students' perceptions and experiences.

ACTUAL OR ANTICIPATED OUTCOMES

The results of the analysis of the collected data revealed some key points. Firstly, the most popular AI tools among students are ChatGPT, Gemini, and Bard. Students said that the main purposes of using AI tools are searching for information, clarifying doubts, carrying out academic work, creating texts, translating, generating ideas, solving programming problems, and entertaining. The benefits most frequently cited by students are ease of research, speed in obtaining information, ability to solve problems and time savings. They also highlighted some risks. The risks most frequently cited by students were the lack of human interaction, technological dependence, plagiarism and limited creativity. However, students express positive expectations regarding the future of AI in teaching.

CONCLUSIONS/RECOMMENDATIONS

The results of this study suggest that AI tools are already being used significantly in higher education. Students recognize the benefits of these tools but are also aware of their risks. The acceptance of AI in higher education is, in general, positive, but it is essential that the use of these tools be discussed and regulated in a responsible and ethical way.

Regulation of the use of AI tools in higher education must consider the need to guarantee the quality of learning, student autonomy, the ethics of using AI, and meaningful human interaction in the teaching-learning process.

Artificial Intelligence (AI) has been shown to perpetuate biases, leading to the marginalisation of minority groups. In the United States, anti-discrimination laws mandate affirmative measures and European legisla-tion, such as the European anti-discrimination law, the Race Directive and the Framework Convention for the Protection of National Minorities, pro-hibit discriminatory practices and encourages institutions to implement positive actions. Ensuring that AI Tools in Education (AITED) does not replicate discrimination is a legal requirement at both the European Union (EU) and in the US.

This paper examines approaches to developing affirmative tools in educa-tion, focusing on the European context. The research question is: How can an educational tool for coding be designed to promote equality, equity, and legality.

Initially, we review existing research indicating that factors such as sex, ethnicity, and race influence students’ learning progress. We then explore the possibility of creating an affirmative educational tool for teaching cod-ing fundamentals, concentrating on minority groups defined by sex, race/ethnicity, and religion, as recognised in European anti-discrimination Law. Educational institutions are legally obligated to address the needs of these groups, ensuring equal learning opportunities to prevent indirect dis-crimination.

Utilizing Political Economy Analysis (PEA) and Critical Race Theory (CRT), we propose a design approach for an educational tool offering three distinct learning variants. The first variant, the contextualized path, sup-ports learning within a humanistic framework. The second variant, the con-firming path, is designed to be sensitive and supportive. The third variant, the conventional path, serves as a traditional approach for comparative pur-poses. The tool development process involves engaging the target groups.

By designing an educational tool with varied learning paths, we aim to cre-ate a more affirmative, inclusive, equitable, and modern AITED that com-plies with European anti-discrimination Law and related regulations.

This study explores the integration of Artificial Intelligence

(AI) and remote experimentation in STEM education. AI-assisted instruction offers personalized feedback to foster critical thinking, while remote experimentation provides hands-on learning experience remotely. The research focuses on the combined use of AI-assisted instruction and remote experimentation in remote laboratories utilizing real hardware.

A mixed-methods approach will be used to assess the system’s impact

on student engagement, learning outcomes, and problem-solving abilities. Quantitative data, including task completion times, error rates, and

AI intervention frequency, will be paired with qualitative insights from

student surveys and interviews. The anticipated outcomes include improved student performance, reduced errors, and faster task completion,

alongside enhanced confidence and critical thinking. The findings will

highlight the potential of integrating AI and remote experimentation to

enhance STEM education and guide future system development.