Practical exploration of English translation activity courses in universities under the background of artificial intelligence - Jiexia Dai (Systems and Soft Computing)
In recent years, along with the increasing international exchange, cultivating English translation talents has become particularly important. This study aims to explore the application effect of artificial intelligence technology in college English translation activity courses and compare the differences between traditional teaching methods and AI-based methods. Through comparative analysis of the improvement of the translation ability of the two groups of students, the practical value and feasibility of AI technology in translation teaching is evaluated to provide reference and guidance for future translation education. This paper used the neural network machine translation model in artificial intelligence to solve the practical problems in implementing of college English translation activities. Intelligent translation through Recurrent Neural Network (RNN) can better leverage the role of artificial intelligence, which provides new methods for the sustainable development of translation activity classes in universities. Group A adopts the traditional English translation teaching method, mainly relying on teachers' explanations and students' practice. Course content includes interpretation of translation theory, text translation practice, and discussion and analysis based on translation cases. It mainly uses paper textbooks and reference books, as well as some online translation resources, but does not involve the application of artificial intelligence technology. Group B integrates artificial intelligence technology into English translation teaching method, using neural network translation model (such as RNN) to assist teaching. In addition to traditional translation theory and practice, the course also includes training and practice on the use of AI translation tools. Using AI translation software, an online translation platform and a specially developed AI-based translation practice platform, the platform is an artificial intelligence translation model integrated with RNN, designed to assist teaching and improve students' translation skills. The experimental results of this article indicate that the average translation scores of Group A and Group B students in the first 6 months of the experiment were 65.77 and 65.71, respectively. The scores of Group A and Group B students after the experiment for 6 months were 68.57 and 82.69, respectively. The results show that translation teaching in the context of artificial intelligence significantly improves the efficiency and accuracy of students' translation, and enhances the interaction and interest of learning. This finding shows that the AI-based technology in translation teaching is not only feasible and effective, but also can provide new ideas for the modernization and intelligent development of translation education, and improve students' practical translation ability and professional competitiveness.