Having researched art students’ use of and attitudes towards artificial intelligence between 2024 and 2025 in five art disciplines at seven Hungarian art universities (the results of which we presented at ECER 2025), the question arose whether differences exist between dance students’ perceptions of AI at the institutions involved in our newly formed Arts in Motion CEEPUS Partnership, and whether the time that has passed has had an impact on dance students’ frequency or modes of using AI applications. We aim to investigate both commonalities and discrepancies in order to take action regarding educational practices and policies. Teaching should create learning experiences that encourage students to go beyond the already known and learn with constructive disobedience (Barad, 2003), even when using new technologies. AI tools, with their flexible and adaptive features, may meaningfully shape students’ engagement by offering personalized learning pathways and tailoring content to individual learners (Illingworth & Forsyth, 2026). However, ethical concerns may arise (Roe & Perkins, 2025) and should therefore be addressed.

The sample analysed includes dance students from the institutions of the Arts in Motion Central European Exchange Program for University Studies (CEEPUS) Partnership, formed in autumn 2025: the Hungarian Dance University (HDU) (n = 200) and two other small higher education dance institutions. One of them is the Belgrade Dance Institute (BDI), Serbia (n = 15), and data is also being collected from the Alma Mater Europaea Dance Academy, Ljubljana, Slovenia. The vast majority of students are between 18 and 25 years old. The adapted English version of the original (2025) online Microsoft Forms questionnaire was administered using the same platform for data collection. The survey included demographic and educational background items, as well as multiple-choice and five-point Likert scale questions on AI use and attitudes, supplemented by open ended items for further explanation. Participation was anonymous, and no personal data were collected. The quantitative data analysis is descriptive.

Our preliminary results are in line with data published on the most frequently used AI apps (Németh, 2026), as dance students also most often use ChatGPT, Canva, DuoLingo and Grammarly. Students of the BDI, however, reported less frequent use of AI applications than HDU students: around 13% of BDI students, compared to 30% of HDU students, reported frequent or very frequent AI use. At the same time, 40% of BDI students reported rarely, and 25% of HDU students very rarely, using AI. These data seem to contradict the hypothesis that, over the time passed since the first survey, dance university students might have developed more frequent AI using habits. It is possible that regional factors play a more decisive role. There is also a difference in the self-reported types of AI apps used across universities: 82% of HDU students preferred ChatGPT over other apps, compared with 52% of BDI students. In the more recent survey among BDI students, the EatBetter app—potentially particularly useful for dancers—also appeared, marking a possible tendency toward more dancer focused AI usage. Although the research has limitations, partly due to the small number of participants at the smaller dance universities involved, valuable information has been collected. The results may support collaborative efforts to update AI policies and educational practices in order to address ethical concerns and better meet educational needs.

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