With the continuous development of artificial intelligence technology, large-scale language models have demonstrated significant potential across various fields. In education, an increasing number of methods leverage large-scale language models to enhance educational quality, introducing new ideas and opportunities for reform. However, training a large language model with substantial professional knowledge to meet teaching needs incurs high labor costs. The fine-tuning approach based on human feedback alignment can significantly lower these model labor costs. Consequently, this article thoroughly investigates the application of this large prediction model method, which is rooted in human feedback alignment, within the educational reform of algorithm analysis and design courses and examines its impact on teaching effectiveness and students’ learning experiences.