Technological integration and professional competency development: a structural equation analysis of generative AI, learning engagement, and media literacy in journalism education

This study investigates how generative artificial intelligence (AI) integration affects professional skill development in journalism education through learning engagement and media literacy. Conducted between September 2023 and February 2024 across five Chinese universities, this cross-sectional survey study employed structural equation modeling (SEM; Mplus 8.8) to test a sequential mediation model. A stratified random sample of 500 journalism and communication students provided valid data (response rate: 76.9%). Results demonstrate that AI integration in journalism education operates through a developmental pathway rather than direct skill transfer. AI utilization first enhances student learning engagement, which subsequently improves media literacy capabilities, and finally contributes to professional skill development [full sequential indirect effect: β = 0.172, 95% CI (0.115, 0.229)]. These findings suggest that effective AI integration requires structured pedagogical approaches that sequence learning activities from initial engagement with AI tools, through critical evaluation skill development, to professional application. For journalism educators, this implies the need for curriculum designs that recognize these developmental stages rather than treating AI as a simple tool addition. The study contributes to understanding technology adoption in professional education contexts while acknowledging the limitations of cross-sectional research design and single-country sampling.