604 KB0 KB2026Ahdad, Munadirah M.Ismaniati, ChristinaKhairaty, Nurul InayahRasyid, FauziahYusuf, Nurainištevilka:letnik:1 spletni vir (1 datoteka PDF (25 str.))DOI:10.26529/cepsj.2146ISSN:2232-2647COBISSID_HOST:283706883URN:URN:NBN:SI:doc-QJLIAFBFen= University of Ljubljana PressCEPS journalAI readinessartificial intelligenceIzobraževanje učiteljevpripravljenost na umetno inteligencosamoučinkovitostself-efficacyteacher educationtehnološko-pedagoško znanje o vsebiniTPACKUmetna inteligencaDo differences matter?| a comparative study of AI integration readiness among pre-service teachers across disciplines and genders| Ali so razlike pomembne?| primerjalna študija pripravljenosti na vključevanje umetne inteligence med bodočimi učitelji različnih predmetnih področij in spolov|The rapid advancement of artificial intelligence (AI) technologies is reshaping the future of education and redefining the competencies required of teachers. As part of national reforms in Indonesia’s teacher education system, the Pendidikan Profesi Guru (PPG) program has integrated digital training aimed at enhancing pre-service teachers’ readiness to adopt emerging technologies, including AI. This study investigates whether pre-service teachers’ readiness to integrate AI, measured through the Technological Pedagogical Content Knowledge (TPACK) framework, and self-assessment of self-efficacy and AI readiness, varies significantly based on gender and disciplinary background. Quantitative data were collected from 200 pre-service teachers through an online Likert-scale questionnaire comprising 52 validated items. The constructs measured included TPACK, AI-related self-efficacy, and AI readiness. Data were analysed using independent samples t-tests to examine gender-based differences and one-way ANOVA followed by Tukey HSD post-hoc tests to analyse differences across five disciplinary categories: Natural Sciences, Social Sciences, Mathematics, Primary Education, and Language Education. The results indicated no significant differences in AI readiness, self-efficacy, or TPACK across gender groups. However, significant differences in self-efficacy emerged across academic disciplines, particularly between participants from Social Sciences and Primary Education backgrounds. Boxplot visualisations further highlighted disparities in score distributions, suggesting uneven levels of confidence within specific subject groups. The findings underscore the need for discipline-sensitive strategies in AI-related teacher education. While gender parity appears to have been achieved in digital readiness, differentiated pedagogical interventions are essential to address discipline-specific gaps in self-efficacy and ensure equitable integration of AI across educational contextsTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Pedagoška fakulteta