Research on the Effectiveness of Human-Machine Collaborative Teaching Based on Data Analysis in the Era of Digital Intelligence

In the era of digital intelligence, the integration of artificial intelligence (AI) into educational practices has the potential to transform traditional teaching methods. Human-machine collaborative teaching (HMCT) is a new paradigm that leverages the strengths of both human educators and AI tools to enhance learning outcomes. This study investigates the effectiveness of three human-machine collaborative teaching strategies—directive, guided, and collaborative-implemented in a university—level software engineering course. The experiment involved 99 third-year students and aimed to evaluate the impact of these teaching strategies on creativity and academic performance. The results show that the collaborative teaching method, enhanced by generative AI tools, led to the highest performance in final exams and overall academic achievement, as well as increased participation in extracurricular innovation activities in homework. While directive teaching produced consistent results, it did not foster the same level of engagement or creativity. Guided teaching demonstrated moderate success, but the greatest benefits were observed in the collaborative approach, where students took an active role in their learning. This study suggests that integrating AI into collaborative learning environments can enhance both academic outcomes and student creativity, and recommends expanding the use of AI-supported strategies in higher education. Future research should explore the scalability of these findings across disciplines and investigate long-term impacts on student development and career readiness.