The integration and innovative practice of intelligent AI and local opera in college teaching

This paper explores the impacts of integrating AI into the teaching of Chinese Opera using a mixed-methods approach, examining performance, engagement, and psychological factors in students. A quasi-experimental design involving 199 participants over a one-year period was conducted, involving teaching with and without AI enhancement. Quantitative data, derived from standardized tests and analytics provided by AI platforms, were supported by qualitative data from interviews and observational studies. These results suggest that there were significant increases in the AI-enhanced cohort in opera performance competencies (Δ = 13.6, p < 0.001); retention of cultural knowledge (Δ = 15.5, p < 0.001), and overall engagement levels (r = 0.73, p < 0.001). Time series analysis revealed nonlinear learning trajectories, with participants showing greatest gains during the intervention's midpoint. The psychological data showed a strengthening relationship between self-efficacy and in-performance outcomes, demonstrating an increase from r_initial = 0.38 to r_final = 0.67, p < 0.001. This study indicates both the potential of AI in the preservation and development of traditional artistic work and highlights some challenges in initial implementation. The findings facilitate the ongoing discussion of integrating technologies into arts education and provide valuable insights to support curriculum development, in addition to conserving cultural heritage in the modern-day digital world.