Research on Generative Artificial Intelligence Technologies in Education: Opportunities, Challenges, and Ethical Aspects

The article investigates that in Ukraine there are a number of regulatory documents aimed at supporting the use of artificial intelligence (hereinafter referred to as AI) in education and in various industries, starting from October 2019. The advantages and risks of using generative AI technologies are substantiated. The advantages and risks of using AI technologies in the educational process of professional training of higher education students and the education system are characterised. Using the example of practical work and generation tasks in Adobe Firefly with AI, it was found that the task program with abstract concepts (on the example of the Ukrainian fairy tale ‘About Truth and Wrong’) failed to generate images. At the same time, the author's fairy tale with real literary characters ‘The Princess on the Pea’ by Hans Christian Andersen was generated. The article also discusses the challenges that were identified when the programme performed the task of generating images for the query ‘Artificial intelligence and humans: good or evil? A series of images‘, which, in the authors’ opinion, require a thorough study by professional annotators. The authors have analysed the JourneyDB program and the Midjourney AI program, which runs on the JourneyDB platform. The results of a survey of pedagogical and scientific workers concerning the ethical aspects of AI in education are analysed. The paper contains an analysis of opportunities and new challenges in the field of AI application in education and other industries The authors conclude that the existing models of developed AI generative programs require more comprehensive testing before they can be launched for mass use. If inaccuracies are found in the results of generations, they recommend changing the data on which they operate (changing the DataSet). We believe that generative AI technologies in education have a promising and rapid future. Generative AI technologies require research into their broader capabilities and challenges.