Abstract
The rapid growth of artificial intelligence (AI) in education has made teachers’ attitudes and competence in AI integration a critical area of innovation. This study examines the effects of four professional development workshops and school-based practices on teachers’ attitudes and abilities in AI-integrated instruction, focusing on the interplay between teaching experience and workshop participation. Data from 162 teachers across four workshops were analyzed, incorporating questionnaire responses and qualitative feedback. Key variables included “teaching quality,” “teaching efficiency,” “quick learning ability,” and “instructional helpfulness.” Statistical methods included descriptive analysis, repeated-measures ANOVA, interaction analysis, and structural path modeling. Workshops significantly improved “teaching quality” (mean = 4.23) and “instructional helpfulness” (mean = 4.56). Repeated-measures ANOVA confirmed significant effects on “quick learning ability” and “instructional helpfulness” (p < 0.05). Interaction analysis revealed that senior teachers exhibited greater improvements despite lower initial scores. The structural model demonstrated that “workshop participation” influenced outcomes via “teaching quality” and “efficiency.” Workshops and school-based practices effectively enhance teachers’ attitudes and competencies in AI integration, with differentiated impacts based on teaching experience. Personalized training approaches are essential to optimize adoption and innovation in AI-driven education.
Presenters
Yiju LinStudent, PhD, Education and Learning Technology at National Tsing Hua University, Taiwan
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
ARTIFICIAL INTELLIGENCE, TEACHER PROFESSIONAL DEVELOPMENT, AI-INTEGRATED INSTRUCTION, TEACHING COMPETENCE, EDUCATIONAL
