Primary school teachers’ perceived AI-related professional knowledge in Slovakia: a city-wide ITPACK study

Abstract

The Slovak education system is preparing for curricular updates that are expected to support the inclusion of artificial intelligence (AI) from the 2026/2027 school year, creating a need to understand teachers’ capacity for effective, ethical, and pedagogically meaningful AI integration. Effective AI integration requires a synergy between technology, content, and pedagogy, rather than a narrow focus on the user-level application of digital tools. Using the Intelligent Technological Pedagogical Content Knowledge framework, hereafter ITPACK, this study examines primary school teachers’ self-assessed AI-related professional knowledge as a key factor in integrating AI into education. The study involved 247 primary school teachers from all six municipal primary schools in Komárno, Slovakia, thereby providing full institutional coverage. The main objective was to assess how professional experience and formal career stage are associated with five dimensions of AI-related professional knowledge: technological knowledge (TK), technological pedagogical knowledge (TPK), technological content knowledge (TCK), integrated technological pedagogical content knowledge (TPACK), and ethical awareness (ET). Because the original experience × career-stage structure contained several small or empty cells, professional experience was recoded into four broader professional phases. The revised ANOVA results showed statistically significant effects of professional phase across all five ITPACK dimensions, whereas career stage was not significant in any dimension. Tukey post-hoc comparisons showed that early-career teachers generally reported higher scores than teachers in later professional phases, especially veteran teachers. These findings suggest that teachers’ AI-related professional knowledge varies more clearly by broader professional-experience phase than by formal career stage. The results highlight the need for differentiated professional development that moves beyond universal tool-focused training and emphasises subject-specific AI use, ethical awareness, data privacy, algorithmic bias, and the responsible use of generative tools.