Currently, we observe the active development of artificial intelligence (AI) and digital tools in education (Zhai, 2021; Kim et al, 2022; Chan, 2023; Porayska-Pomsta, 2024). Some AI language models (e.g., GPT) emerged several years ago (Ausat et al, 2023; Tan et al, 2024), which are positioned as innovative solutions in education. Then other AI language models (Claude, Gemimi, Gigachat) have been proposed (Patarakin et al, 2023; Martins, 2024), and more recently new variants of AI models (DeepSeek, Qwen, Stargate) have emerged. To some extent, we can observe a race of neural networks being created by scientists from different countries. However, these neural networks, when integrated into educational settings, have the potential to enhance the development of educational products and the organisation of teaching processes.

It is important for university and school teachers to acquire proficiency in leveraging the capabilities of AI, since the quality of students' utilisation of AI depends on the competence and accuracy of their teachers (Khreisat et al., 2024). This may also facilitate the establishment of a proper culture of AI usage and adherence to ethical standards when handling and employing AI products. Consequently, numerous face-to-face, online, and hybrid courses are being designed to equip teachers with the necessary skills to utilise AI and digital tools effectively.

The purpose of this study was to design a course for teachers on the use of AI and digital tools in teaching and educational product development (learning and testing materials, online courses, etc.) and define its effectiveness.

The following research questions were posed:

- What approaches and instructional models should be used to develop an AI and ICT course for teachers?

- How does the integration of the 'station rotation' model of blended learning correlate with students’ (i.e. school and university teachers) engagement in the learning process?

The hypothesis was that training students using active teaching methods and practice-oriented assignments would increase their interest and engagement.

L. Vygotsky's theory of social constructivism was the theoretical framework of our study. According to this theory, the purposeful self-development and 'self-construction' of the personality is realised throughout active interactions with society and the environment. The significance of the individual's activity in learning, the ineffectiveness of transferring knowledge, the need to create conditions for self-regulated cognition, the value of cooperation etc. are emphasised. (Petrova et al., 2017).In line with the theory, the training was designed to be group-based with constant interaction between students.

When analysing teaching approaches, the 'station rotation' model of blended learning was selected due to its capacity to combine optimal teaching practices from face-to-face and online learning, aligning with the principles of social constructivist theory. Station rotation is a blended learning model in which the instructor divides students into two or three groups based on the learning activity type. One group undertakes activities at an online learning station, another group works on a group project at an independent work station, and the third group works with the instructor, which resembles a traditional class. During the class, groups work at stations for a predetermined amount of time and change stations when the designated time is reached. Each group of students works at all stations during the class (Davlatova, 2022). The model involves the use of different forms of work and active learning methods.

We studied the effectiveness of the course during two academic years in HSE University, particularly, from September 2023 to November 2024. The study involved the participation of nine groups, comprising a total of 148 students, who were engaged in the instruction of different subjects at both the school and university levels.

The course was developed using the ADDIE model. Blended learning, specifically the ‘station rotation’ model, was taken as the primary teaching approach. A mixed-methods approach was employed in the study, incorporating both qualitative and quantitative data collection methods. An online student survey was conducted, and student feedback was collected in both written and oral formats. The study included two stages: 1. September to November 2023 (6 groups of students) 2. September to November 2024 (3 groups) The total number of students in the groups was 148. The average number of students in a group ranged from 12 to 25. The effectiveness of the developed course was determined based on several types of data: student evaluation of teaching (SET), oral feedback from students at the end of each class, and an anonymous online student survey. SET data is a tool for monitoring the quality of courses and teaching at the university level, conducted in the form of an online survey where students evaluate course content on a 5-point Likert scale. The questions are related to the assessment of the degree of usefulness of the course for a career, for broadening of horizons and diversified personal development, the novelty of the gained knowledge, and the complexity of the course. The survey also contained questions to determine the quality of teaching, where students rated teaching on the Likert scale according to the following criteria: clarity of requirements, clarity and consistency of study materials, communication between the teacher and the audience, opportunity for extracurricular communication on academic and scientific issues. In addition, there were open-ended questions about the usefulness of the course content, features of the organization, suggestions for course improvement, successful and unsuccessful teaching practices. The anonymous online survey contained closed-ended Likert scale questions where students were asked to rate their satisfaction with the course content, course organisation, whether they liked working at different stations, communication with the instructor and with peers. There were also open-ended questions where students could provide justifications for their ratings and to offer suggestions for enhancing the course. The classes were conducted using a 'station rotation' model. Students were scheduled to move between stations during the class at predetermined intervals, engaging in a variety of tasks. This approach enabled them to explore the learning problem from diverse perspectives and formulate solutions. Consequently, students developed different types of learning materials for their own students.

The findings serve to substantiate the efficacy of the station rotation model in facilitating students' acquisition of AI and ICT competencies for the educational products development. Quantitative data analysis revealed a mean course content rating of 4.42 out of 5, and a mean teaching rating of 4.91 out of 5, indicating a high level of satisfaction with the implementation of the 'station rotation' model. The results of the anonymous online survey revealed that 93% of students expressed satisfaction and strong satisfaction with the course instruction. A mere 7% expressed a dissatisfaction. Based on the students' responses to the open-ended questions, it was found that AI and digital tools training using station rotation was preferred by the students for the following reasons: - fulfilling different assignments and interacting with peers, e.g., ‘I enjoyed working as a team’; - learning about a variety of educational resources, e.g. ‘I learnt a lot of new digital tools, websites, AI services’. - immersion in using AI, e.g. ‘I liked learning new ways of using artificial intelligence’ - developing different types of material, e.g., it useful for teaching, since you can generate different tasks’. - practice-oriented activities, e.g., ‘Theory + instant practice = 100% of learned information from the classroom’ However, some of the 7% of students who did not enjoy the course, seemed that the course was ‘too creative’, others 'did not learn anything new'’. The average student grade is 7 (max. 10), which may indicate a satisfactory level of achievement. It suggests that the course may have a moderate level of complexity, and can be adapted to different learning styles. It can be concluded that the design and implementation of the course aimed at training teachers in the use of AI and ICT within the 'station rotation' model is a highly effective pedagogical approach.

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