The continuous evolution of generative artificial intelligence, natural language processing and learning analytics technology is promoting journalism and communication education from experience-led to data-supported, process traceable and feedback adjustable teaching mode. This paper focuses on the transformative effect of artificial intelligence technology on the teaching mode of journalism and communication education, and constructs a teaching reform framework consisting of intelligent content generation, fact verification support, text revision feedback, learning behavior analysis and teacher regulation. A quasi-experimental study is carried out on 116 students from two natural classes of a university’s journalism and communication course. There were 58 people in the experimental group and 58 in the control group. The research comprehensively collected platform logs, course grades, practical assignments and questionnaire data for analysis. The results showed that the average score of the experimental group was 85.2, which was higher than 79.4 of the control group. The average score of practical work was 89.1, which was higher than that of the control group (82.8), and the differences reached a significant level (p<0.001). At the same time, the average completion rate of teaching videos in the experimental group was 89.3%, and that in the control group was 76.1%. The number of revision rounds was positively correlated with the score of practical assignments (r=0.61, p<0.001). The research shows that the effective value of artificial intelligence in journalism and communication education does not lie in replacing students to complete content production, but in enhancing their ability of information screening, structure optimization, fact checking and continuous correction by relying on computer technology. This paper can provide reference for the digital transformation and intelligent teaching design of journalism and communication education.
