1 Introduction

This section discusses the impact of large AI models on translation education, highlighting the challenges posed by the new technological revolution to education. Large models have become essential tools for daily work and study, raising questions about existing rules and ethics. These models have transformed the ways knowledge is stored and accessed, influencing human cognitive mechanisms, memory capacity, and knowledge construction. The article explores potential future changes in human cognitive abilities, as well as the new knowledge construction and brain function adaptations under AI assistance. It examines what humans can do beyond tasks completed by AI and whether changes in memory and knowledge structures can unleash innovative thinking. Finally, it proposes the need to cultivate high-level translators capable of harmoniously collaborating with AI, redefining translation pedagogy to equip students with both AI mastery and innovative thinking that surpasses AI—an important topic worthy of in-depth exploration.

2 Changes in Cognitive Mechanisms and Countermeasures

This section explores the definition of memory in psychology and cognitive science, as well as its role in translation practice and training. Memory is viewed as the process of encoding, storing, and retrieving individual experiences, forming the foundation of learning. Alan Baddeley's working memory theory emphasizes the dynamic nature of memory, involving the retention, manipulation, and use of information, which is particularly crucial for translation. Translation not only involves the extraction and manipulation of meaning but also relies on the translator's personal perception, experience, and rational computation. Memory is a complex system of information encoding, storage, and retrieval, essential for learning, thinking, decision-making, and behavior. Encoding converts external information into mental representations, storage retains information in the brain, and retrieval accesses information when needed. AGI may interfere with students' transition from passive to active memory, affecting effective information retrieval. Memory is divided into declarative and procedural memory, with neural bases involving the hippocampus and prefrontal cortex. AI may lead to "memory outsourcing," causing students to rely on AI for factual and declarative memory storage, potentially impacting neural plasticity. However, AI can also serve as a "cognitive scaffold," expanding the boundaries of thinking. Translation training should guide students to leverage AI to free up brain memory space, enhance synaptic plasticity, and develop new procedural memory, thereby revolutionizing cognitive frameworks and translation education paradigms.

3 Revolution in Educational Paradigms—A Closed-Loop Human-Machine Collaborative Learning System

When introducing AI tools in primary and secondary education, Singapore and Finland require students to possess basic computational skills and emphasize the need for "ruminative argumentation" of AI-generated content. Higher education and translation training should follow a similar process, ensuring foundational professional competence before integrating AI. Translation curriculum design is key to achieving human-AI collaboration, as teaching workflows influence brain functions in language-related regions. Addressing cognitive and psychological changes in students in the AI era, a three-step human-machine collaborative teaching model is proposed: 1) Students read thematic articles before translation, manually generating glossaries and language expressions; 2) Students submit individual manual translations first, followed by group-discussed versions three days later; 3) Students submit AI-translated versions and their own post-edited versions. Research shows that students with strong language foundations, thorough preparation, and high cognitive awareness are less susceptible to AI interference in post-editing and exhibit lower AI dependency. AI-assisted translation teaching requires instructors to compare dual versions to assess student progress. Future approaches may use visualized metrics for tracking, generating personalized developmental reports. Teaching and research should explore ways to enhance students' cognitive abilities in information processing, fostering translation competence through cognitive and general knowledge internalization. Instructors must clarify teaching priorities at each stage, align with student expectations, and scientifically design lesson plans while adapting to shifts in teacher roles and student skill development. Real-time documentation of psychological and cognitive changes in student-teacher-AI interactions can significantly improve teaching quality.

4 Cognitive Enhancement Strategies and Pedagogy

This section examines the impact of AI tools on the roles of teachers and students in translation education, emphasizing that teachers should act as learning facilitators and organizers, while students become active explorers and appliers of knowledge. Teachers must guide students in proper AI tool usage, cultivate translation quality assessment skills, and monitor emotional and psychological states. Students should leverage AI for translation practice and learning while developing critical thinking to avoid blind reliance on AI. The chapter introduces a cyclical process integrating cognition and teaching, involving exploration from known to unknown, evaluation, iteration, and improvement. Additionally, it discusses ethical issues in technological design, including choices in cognitive enhancement methods, establishing hybrid cognitive models, embedding Socratic questioning mechanisms, applying motivational stimulation techniques, and automating the recording and analysis of student training data. These technical-ethical considerations are crucial for designing translation teaching models and software systems.

5 Restructuring and Cultivating Professional Competence

McKinsey research shows that proficient AI users tend to employ it for complex scenario simulation and hypothesis analysis, while novices primarily use it for basic information retrieval. Future workplace competitiveness will hinge on the ability to formulate precise questions, discern when not to use AI, and translate AI output into practical wisdom. Human irreplaceability shifts toward meaning-making capabilities, which AI cannot replicate. Teaching must define problem boundaries, enhance proposition operability, and establish judgment criteria for AI use in human emotional communication, ethically sensitive tasks, and innovative breakthroughs. Focus should be placed on cultivating contextualized translation competence, fostering dual-domain thinking, and proposing differentiated solutions. Humans retain enduring advantages in value anchoring, metaphor creation, and similar domains. Teaching should develop human-AI collaboration competency maps, design meaning-making workshops, and appoint AI mediators to establish a comprehensive human-machine cooperation system. The most稀缺 skill in the future will be the ability to navigate跨语义层穿梭—a new form of cognitive translation competence. Teachers and students are advised to maintain personal cognitive balance sheets to actively manage AI dependency indices. Instructors must adjust syllabi, course plans, and teaching methods based on students' psychological-cognitive shifts and learning outcomes.

6 Conclusion

This section summarizes the impact of the large-model era on student psychology and cognitive mechanisms, including hippocampal degeneration and reduced declarative memory, as well as the expansion of brain functions and characteristics through human-machine interaction. It highlights the appropriate use of AI tools in teaching and the development and实践 of human-machine collaborative closed-loop teaching systems. The need for deeper research into cognitive enhancement strategies and pedagogy is emphasized. The section also addresses meaning generation and reconstruction in translation, the importance of translator knowledge structures, updating these structures for the new era, the shift from human translation to machine-translation-human-review, and the enhancement of translators' new knowledge accumulation and logical analysis skills.