Polish and Ukrainian Future and Early-Career Educators' Perspectives on AI Competencies and Curriculum Priorities in Primary Computer Science Education

Synthesis: Primary computer science classrooms increasingly ask teachers to cultivate AI Literacy in young learners, making Teacher AI Competency a curriculum problem as much as a technical one. This cross-sectional, mixed-methods survey compared 64 future and early-career educators from Poland and Ukraine (32 per country) on curriculum priorities, pupils' AI-related competencies, and professional-development needs in primary computer science education. Critical evaluation of AI-generated content and understanding AI-related risks were among the highest-rated competencies, while prompt development drew comparatively less emphasis; a Ukrainian advantage in prompt development (U = 294.5, p = 0.007) did not survive Holm adjustment (adjusted p = 0.056). After adjustment, significant cross-national differences remained only for robotics (adjusted p = 0.016) and STEAM (adjusted p = 0.038). Both samples placed considerable importance on digital safety, online interaction, foundational computer skills, and AI-related education as curriculum areas. The results support a human-centered, competency-based teacher education approach pairing critical evaluation and Computational Thinking with responsible AI use in primary curriculum design.

Key Findings

1. In a cross-sectional comparative mixed-methods study with 64 respondents (32 Poland, 32 Ukraine), ratings were analyzed with descriptive statistics, Mann-Whitney U tests with Holm adjustment, rank-biserial correlations, and qualitative thematic analysis.
2. Critical evaluation of AI-generated content and understanding AI-related risks were among the highest-rated AI competencies, whereas prompt engineering and AI-project creation were comparatively less emphasized.
3. Ukrainian respondents rated prompt development higher in unadjusted analysis (U = 294.5, p = 0.007), but not significant after Holm adjustment (adjusted p = 0.056).
4. After adjustment, significant cross-national differences remained only for robotics (adjusted p = 0.016) and STEAM (adjusted p = 0.038), both rated more highly by Ukrainian respondents.
5. Both samples showed broadly similar descriptive priorities, and the results support a human-centered, competency-based approach to teacher education.

Why primary computer science raises new AI demands

Computer science education in primary school shapes children's earliest ideas about digital technologies, computational thinking, and responsible participation in digital environments, and AI is becoming an explicit component of that curriculum. The authors argue that preparing for AI-enhanced primary classrooms requires more than technical familiarity: future teachers must introduce age-appropriate AI concepts, support critical evaluation of AI-generated content, and manage concerns about misinformation, bias, and privacy. Prior work shows pre-service teachers often lack such preparation, and comparative evidence from Central and Eastern Europe is scarce. This study therefore asked how Polish and Ukrainian educators rank curriculum priorities, which AI competencies primary pupils should develop, and what professional-development needs follow, treating the three as related but distinct dimensions.

Comparing two national samples

The final analytical sample comprised 64 respondents (Poland: n = 32; Ukraine: n = 32) from the University of Silesia in Katowice and Borys Grinchenko Kyiv Metropolitan University, recruited through student groups and online course forums with non-probability purposive sampling. Most participants (54/64, 84.4%) were student teachers or pedagogical students; 27 of 32 Polish respondents (84.4%) and 17 of 32 Ukrainian respondents (53.1%) reported no professional teaching experience or teaching practice only. Ratings used five-point ordinal scales compared with Mann-Whitney U tests, Holm-adjusted within each family, with rank-biserial correlations as effect sizes; open-ended responses were analyzed through thematic content analysis and integrated with the ratings at the interpretation stage.

Competencies, priorities, and professional-development needs

Across both samples, the ability to critically evaluate AI-generated content and understand AI-related risks received some of the highest ratings, whereas developing effective prompts and creating simple AI projects were rated lower — a prioritization of critical and responsible AI Literacy over specialized interaction skills for younger learners. The only unadjusted cross-national difference in competencies, higher Ukrainian ratings for prompt development (U = 294.5, p = 0.007), disappeared after Holm correction (adjusted p = 0.056). Professional-development needs were substantial across domains, with significant adjusted differences only for robotics (adjusted p = 0.016, rrb = 0.409) and STEAM education (adjusted p = 0.038, rrb = 0.357), both rated higher by Ukrainian respondents; the authors caution that similar descriptive priorities are not evidence of cross-national equivalence.

Human-centered implications for teacher education

The findings support integrating AI literacy into authentic pedagogical tasks rather than isolated technical instruction: evaluating AI-generated output, designing age-appropriate activities, exploring simple AI applications, and reflecting on privacy, bias, and ethical responsibilities. Participants' recommendations — foundational AI concepts, critical evaluation, ethical and responsible use, visual programming, age-appropriate AI-supported projects, and practice-oriented training — point toward curriculum innovation in initial teacher preparation that combines shared competency principles with locally adapted implementation; robotics and STEAM needs are framed as areas for further investigation rather than generalized national differences. The authors position these as research-informed recommendations requiring implementation and evaluation studies before being treated as validated interventions.

What this means for practice

• Instructors. Teach future primary teachers to prioritize critical evaluation of AI-generated content and understanding of AI risks over prompt fluency; these were the highest-rated competencies in both national samples.
• Faculty developers. Build AI coursework into teacher preparation that pairs foundational AI concepts with authentic tasks such as evaluating AI output and reflecting on privacy, bias, and ethical responsibilities.
• Curriculum designers. Anchor primary computer science curricula in digital safety, computational thinking, and responsible AI use, and treat robotics and STEAM professional development as context-sensitive priorities rather than fixed content.
• Policymakers. Avoid reading similar descriptive priorities across systems as equivalence; cross-national comparison requires measurement invariance that this study did not establish.

Limitations

• Small, non-probability samples from two institutional contexts (n = 32 per country) limited statistical power and prevent generalization to national populations.
• The study measured perceived priorities and professional-development needs, not objectively demonstrated AI competence.
• Formal measurement invariance was not established, so the absence of significant differences is not evidence of cross-national equivalence.
• Open-ended response rates differed by question and country (53 of 64 respondents gave at least one substantive response: Poland 24/32, 75.0%; Ukraine 29/32, 90.6%), and only the Ukrainian version of Q7.3 elicited training-format preferences.

Citation

Smyrnova-Trybulska, E., Przybyła-Kasperek, M., Morze, N., & Shkurenko, O. (2026). Polish and Ukrainian Future and Early-Career Educators' Perspectives on AI Competencies and Curriculum Priorities in Primary Computer Science Education. Education Sciences, 16(11):1877.