Preparing Future Social Educators for Artificial Intelligence: Perceived Use, Self-Reported Learning Competencies, and Professional Knowledge Requirements

Abstract

Artificial intelligence (AI) is rapidly transforming higher education, requiring universities to prepare graduates who can use these technologies critically, ethically, and responsibly. However, socially oriented professions remain comparatively underexplored. This study examined preparedness for AI-mediated professional practice among Social Education students through a convergent mixed-methods design integrating perceived AI use, self-reported learning competencies, and professional knowledge requirements. Participants were 49 undergraduate students enrolled in a Social Education degree programme. Quantitative data were collected using a single self-report item assessing perceived AI use and the abbreviated Basic Learning Competencies Scale (COMPES), while qualitative data were obtained through an open-ended question analysed using the Reinert method with IRAMUTEQ. Participants predominantly reported low to moderate perceived AI use. The association between perceived AI use and the overall COMPES score was small and imprecise, r = 0.16, 95% CI [−0.13, 0.42], precluding firm conclusions. Lexical analysis identified six classes that reflected practical applications, professional knowledge, educational considerations, and ethical concerns related to AI-mediated socioeducational practice. The findings suggest that preparedness for AI-mediated practice may involve perceived AI use, self-reported learning competencies, ethical-professional judgement, and professional knowledge requirements. The study provides a preliminary integrative interpretation with implications for curriculum development in Social Education.