Effectiveness, Risks, and Pedagogical Reconstruction in College English Translation Teaching via a ChatGPT-Based Tri-Dimensional Integration Model

Effectiveness, Risks, and Pedagogical Reconstruction in College English Translation Teaching via a ChatGPT-Based Tri-Dimensional Integration Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effectiveness, Risks, and Pedagogical Reconstruction in College English Translation Teaching via a ChatGPT-Based Tri-Dimensional Integration Model Liang Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8729921/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study addresses three persistent challenges in college English translation instruction: the imbalance between student and teacher ratios, inefficiencies in analyzing complex sentence structures, and frequent cultural mistranslations. To tackle these issues, a tri-dimensional integration model—comprising Technology, Cognition, and Ethics—and a PREP teaching framework were developed. A mixed-method empirical study was conducted involving 60 non-English majors. Findings reveal that ChatGPT-assisted instruction significantly improves translation accuracy by 27% and complex sentence processing efficiency by 41%. However, it also introduces a 38% rate of cultural mistranslation, a 27% risk of overreliance on technology, and potential deviations in rendering political terms. To address these risks, the study advocates for multi-engine comparisons and a four-stage task chain to enhance learners’ cultural decision-making competence. It also recommends building a dedicated cultural terminology database and implementing a dual-review mechanism for quality assurance. Furthermore, a “Three-Three Curriculum Model” (30% AI literacy, 30% cross-cultural analysis, 40% human-led revision) is proposed to support the transformation of teachers into operators, instructional designers, and ethical stewards. Ultimately, the study underscores a “Human-led, AI-empowered” principle: while machines can convert language on the surface, the true mission of education lies in interpreting the untranslatable depths of culture—steering with technology, illuminating with humanity, and fostering a dialogue of the soul. ChatGPT translation pedagogy tri-dimensional integration model human-AI collaboration cultural transference Figures Figure 1 1. Introduction The iterative development of artificial intelligence (AI) technology is profoundly reshaping the educational landscape. According to the latest statistics from the Ministry of Education’s “Education Informatization 2.0 Action Plan,” the penetration rate of intelligent educational tools in universities reached 78.6% by the third quarter of 2023, with language learning applications experiencing annual growth exceeding 42%. Amidst this technological revolution, OpenAI’s ChatGPT, with its 175-billion-parameter scale and cross-lingual deep generative capabilities, offers a disruptive solution to traditional translation pedagogy. Current college English translation teaching faces three structural dilemmas: firstly, a resource bottleneck, where the student-to-teacher ratio remains high at approximately 1:120, severely compromising personalized feedback mechanisms; secondly, cognitive overload, with complex sentence parsing consuming over 60% of classroom time; and thirdly, cultural transmission barriers, with research indicating that about 42% of non-English majors commit systematic mistranslations due to deficient cultural schemata (Li Xiaoxiang, 2021). These deep-seated contradictions urgently require paradigm shifts enabled by intelligent technology. ChatGPT, as a prime example of generative AI (AIGC), excels in contextualized knowledge construction through its Transformer architecture. Wang Tian’en (2023) points out in his monograph Artificial Intelligence and Educational Transformation that ChatGPT’s cognitive mechanism comprises three key layers: the foundational layer builds a language probability model through training on 175 billion tokens; the reinforcement learning layer aligns values through Reinforcement Learning from Human Feedback (RLHF); and the application layer iterates knowledge through continuous dialogue. This architecture endows it with four pedagogical values in translation teaching: providing instant feedback with sub-second response latency, enabling precisely stratified personalized guidance, reducing working memory load, and fostering cross-cultural metacognitive development. However, its application also carries potential risks such as the cultural discount effect, technology dependence syndrome, and academic integrity crises. This study empirically explores the efficacy boundaries of ChatGPT in college English translation teaching using a mixed-methods approach, aiming to address three core questions: Firstly, can AI assistance substantially enhance translation accuracy and cognitive efficiency? Secondly, what cognitive and ethical risks arise during technological integration? Thirdly, how can a novel educational paradigm of human-AI collaboration be constructed? The findings will provide empirical evidence for language teaching transformation in the intelligent era, driving a strategic shift in translation education from instrumental training towards creative empowerment. 2. Literature Review 2.1 Theoretical Foundations of Technology Integration The technological integration in translation pedagogy requires robust theoretical underpinnings. Hubbard’s (2009) Technology-Enhanced Learning (TEL) framework emphasizes that educational technology design must adhere to the “cognition-first” principle, meaning tools should serve deep learning goals rather than superficial efficiency. In translation studies, Pym’s (2013) “minimal error principle” advocates that technological assistance should keep semantic deviations within an acceptable threshold. This view finds validation in neurolinguistic research—fMRI scans show abnormal activation in learners’ Broca’s area (the language production center) when translation error rates exceed 15% (Perfetti, 2018). From a cognitive development perspective, ChatGPT's instant feedback mechanism perfectly aligns with Vygotsky’s Zone of Proximal Development theory. Vygotsky (1978) emphasized the scaffolding role of social interaction in cognitive development. As a digital cognitive partner, ChatGPT provides appropriate support for learners at different levels by dynamically adjusting feedback granularity (from lexical substitution suggestions to discourse reconstruction strategies). Cutting-edge neuroscience research confirms that receiving instant feedback increases neural coupling strength between the prefrontal cortex and hippocampus by 37%, significantly higher than in traditional teaching modes (Thomas, 2022). This provides neurobiological evidence for ChatGPT’s cognitive offloading function. 2.2 Realistic Challenges in Translation Teaching Current college English translation teaching is mired in multi-dimensional difficulties. A national survey of 42 universities by Li Xiaoxiang (2021) revealed that only 28% of students believed the existing teaching model effectively enhanced their translation competence. These challenges can be summarized across three dimensions: Instructional Structure Level: Traditional classrooms suffer from a vicious cycle of “unidirectional lecturing - passive practice - delayed feedback.” Wang Huashu (2021) sharply observes in An Introduction to Translation Technology the “three deficiencies phenomenon”: deep teacher-student interaction accounts for less than 15% of class time, practical training constitutes below 30%, and instant feedback coverage is less than 20%. This structural defect perpetuates cognitive gaps. Student Competence Level: Dual weaknesses in native and second language proficiency create expressive dilemmas. Non-English majors commonly exhibit “grammatical correctness but pragmatic failure,” particularly showing systematic bias in handling culture-loaded terms. For instance, translating “亡羊补牢” (mending the pen after sheep are lost) literally as “mend the fold after sheep are lost,” ignoring the English equivalent proverb “lock the stable door after the horse is stolen” (Nida, 2020). Resource Provision Level: Large class sizes and faculty shortages lead to guidance deficits. The average turnaround time for translation assignment feedback is 72 hours, causing students to miss the optimal correction window. Simultaneously, standardized textbooks cover less than 45% of necessary cultural content, failing to meet authentic translation scenario demands (Zhang Zhenyu, 2023). 2.3 Educational Potential of Intelligent Technology Generative AI exhibits dual characteristics in translation teaching applications. Kohnke’s (2023) empirical study in the SSCI journal Computer Assisted Language Learning showed ChatGPT responds to vocabulary translation tasks in less than 0.8 seconds with 91.5% accuracy in grammar correction. A 2023 meta-analysis in the Nature partner journal npj Science of Learning, synthesizing 51 global studies, confirmed an effect size (ES) gain of 0.867 for learning outcomes and 0.457 ES for higher-order thinking skills in AI-assisted groups (Williams & Hessen, 2023). Specifically in teaching scenarios, ChatGPT demonstrates four core values: * Personalized Scaffolding: Dynamically adjusts output based on learner level (e.g., providing literal translations for beginners, cultural annotations for advanced learners). * Process Visualization: Shows translation decision chains via version comparison features. * Cultural Mediator: Parses connotations of culture-specific items (e.g., contrasting the value differences of “individualism” in Eastern and Western contexts). * Metacognitive Coach: Guides reflection through probing questions (e.g., “Why choose domestication over foreignization strategy?”). However, its limitations warrant equal caution. In handling deep cultural structures, ChatGPT exhibits cultural discounting, such as translating the Chinese philosophical concept “无为” as “inaction” rather than the more accurate “effortless action” (Wu, 2022). More seriously, its statistical learning nature leads to hallucinatory outputs in low-resource language pairs, such as mistranslating the Yunnan dialect “板扎” (excellent) as “board tight” (Chen, 2023). 3. Research Design 3.1 Methodological Framework This study employed an explanatory sequential mixed-methods design, implemented in three phases: Quantitative Experimental Phase: A quasi-experimental design was used. 60 non-English major juniors were randomly assigned to an experimental group (ChatGPT-assisted, n = 30) and a control group (traditional teaching, n = 30). Informed consent was obtained from all student participants involved in the study. Participants were informed about the research purpose, procedures, potential risks and benefits, and their right to withdraw at any time without penalty. A pre-test independent samples t-test showed no significant difference in translation ability (t = 0.32, p > 0.05). The experimental group received an 8-week systematic intervention, completing 2 multi-genre translation exercises weekly (political-economic 30%, scientific-technical 30%, literary 40%). A “Dual-loop Learning Model” was implemented: the inner loop was a learning cycle of “Preview-Translate-AI Feedback-Reflection”; the outer loop was a teaching cycle of “Task Design-AI Analysis-Intensive Lecture Deepening.” Qualitative Inquiry Phase: 12 students from the experimental group underwent semi-structured interviews (45–60 minutes each), focusing on four dimensions: technology acceptance, cognitive strategy shifts, cultural understanding pathways, and ethical risk perception. Interview data underwent three-level coding using NVivo 12, yielding 7 core categories. Model Construction Phase: Based on empirical findings and integrating social constructivism and connectivism theories, a “Tri-Dimensional Integration Model” was proposed and refined through two rounds of the Delphi expert method. 3.2 Measurement Tool Validation To ensure assessment validity, multi-dimensional standardized tools were used: Translation Accuracy Assessment: Used the Sinicized version of the China Standards of English (CSE) scale, comprising semantic conveyance (40%), textual coherence (30%), cultural adaptation (20%), and technical norms (10%). Two blind reviewers scored independently, achieving an Intraclass Correlation Coefficient (ICC) of 0.89. Cognitive Load Measurement: Used the NASA-TLX scale (Cronbach’s α = 0.91). Learning Motivation Assessment: Based on Keller’s ARCS model questionnaire (Kaiser-Meyer-Olkin measure of sampling adequacy = 0.87, suitable for factor analysis). 3.3 Intervention Implementation The experimental group used a tiered intervention strategy: Technology Embedding Layer: Built a dynamic corpus system integrating ChatGPT and Snowman CAT toolchains, enabling side-by-side comparison of translations from 8 engines (e.g., DeepSeek, Google MT). An error heatmap system was developed to automatically flag three types of errors: grammatical errors (red alert), cultural missteps (yellow alert), logical breaks (blue alert). Cognitive Reconstruction Layer: Implemented metacognitive monitoring training. Students documented key decision-making thought processes via “Human-AI Collaboration Logs.” For example, when translating “画蛇添足” (draw legs on a snake), they had to explain: “Rejected AI’s literal ‘draw legs on a snake’ and chose the English proverb ‘gild the lily’ as it better aligns with target culture cognition.” Ethical Co-construction Layer: Established a Cultural Security Protocol mandating dual review for politically or religiously sensitive terms. For instance, “人类命运共同体” was required to be translated as “a community with a shared future for mankind” not “global community,” ensuring ideological accuracy. 4. Research Findings 4.1 Quantitative Analysis Results Pre-post-test comparisons of translation ability showed the experimental group achieved significant improvement in accuracy (p < 0.01), as shown in Table 1 : Table 1 Pre-post-test comparisons Group Pre-test (M ± SD) Post-test (M ± SD) Improvement Rate t-value Experimental 12.3 ± 1.8 15.6 ± 1.2 27% 8.37** Control 12.1 ± 2.1 13.9 ± 1.7 15% 4.12* *Note: **p < 0.01, *p < 0.05 In-depth analysis revealed a 41% improvement in complex sentence processing efficiency within the experimental group (6.2min → 3.7min), attributed to ChatGPT’s syntax visualization feature—parsing complex clauses into tree diagrams to reduce working memory load. Cognitive load measurement showed the experimental group’s NASA-TLX total score was significantly lower than the control group (t = 5.32, p < 0.001), with the mental demand dimension showing the largest difference (d = 0.91). For learning motivation, the confidence dimension of the ARCS scale increased by 0.91 ES, indicating technology significantly boosted self-efficacy. 4.2 Qualitative Research Findings Grounded theory analysis of interview data yielded four core themes: Cognitive Value of Instant Feedback: Cited as the primary benefit by 62% of respondents. Student S09 described: “Before, corrections took days. Now ChatGPT points out three subject-verb agreement errors in 20 seconds and explains English hypotaxis. This immediacy creates a positive learning cycle.” Educational neuroscience research shows such timely reinforcement can increase long-term memory encoding efficiency by 40% (Duke, 2022). Bidirectional Deepening of Cultural Understanding: Evident in 34% of interview transcripts. Student S07 illustrated: “Translating ‘红白喜事’ (red and white events), ChatGPT not only gave the literal translation but also contrasted Chinese and Western funeral customs, suggesting ‘weddings and funerals’ in cross-cultural contexts.” This comparison moved students beyond language conversion into exploring deep cultural structures. Technology Dependence Risk: Mentioned in 25% of statements. Student S04 admitted: “Once I forgot to use ChatGPT, I hesitated to confirm the translation of ‘carry coal to Newcastle.’ This tool anxiety is alarming.” More seriously, AI exhibited ideological bias in specific domains, such as translating “共同富裕” as “common prosperity” instead of the internationally accepted “shared prosperity” (Student S11). Typical Mistranslation Cases Revealing Technical Limitations: “ChatGPT translated the diplomatic term ‘韬光养晦’ as ‘hide brightness, nourish obscurity,’ completely losing its connotation of ‘strategic restraint’”(Student S07). “AI suggested translating ‘说曹操曹操到’ as ‘speak of Cao Cao and he appears,’ but didn’t note the English proverb ‘speak of the devil’ is more idiomatic” (Student S08). 5. Theoretical Model and Teaching Framework: Paradigm Reconstruction for Human-AI Collaboration 5.1 Tri-Dimensional Integration Model: Dynamic Balance of Technology-Cognition-Ethics Based on empirical data and theoretical foundations, this study proposes the “Technology-Cognition-Ethics” Tri-Dimensional Integration Model (Fig. 1 ), providing a systematic framework for ChatGPT application in translation teaching: • 5.1.1 Technology Embedding Layer: Optimizing Instrumental Rationality Focuses on using intelligent tools to enhance translation precision and efficiency. A core component is the multi-engine comparison system, integrating outputs from eight mainstream engines (e.g., ChatGPT, DeepSeek, Google, MT) and automatically tagging key differences. For instance, translating “青山绿水” (green mountains and clear waters) would display literal (“blue mountains and green rivers”), free (“lush mountains and clear waters”), and culturally substituted (“scenic landscape”) versions, guiding students to analyze each version’s cultural adaptability and contextual suitability. Another key tool is the error heatmap generator, using AI to identify and categorize three core issues: grammatical errors (red alert), cultural missteps (yellow alert), and logical breaks (blue alert), creating personalized error distribution maps. This visual diagnostic efficiently reveals student weaknesses. For example, if a student fails to discern the subtle ideological difference between “common prosperity” and “shared prosperity” in political text translation, the system triggers a yellow alert (cultural misstep), providing immediate, targeted feedback to improve translation sensitivity and accuracy. • 5.1.2 Cognitive Reconstruction Layer: Strategies for Higher-Order Competence Development This pedagogical approach cultivates advanced competencies through a four-stage progressive task chain. It begins with Deconstruction, where ChatGPT generates three translation variants (literal, free, creative) of a source text. Next, the Critique phase organizes group debates analyzing cultural adaptability—for instance, examining why “江湖” might be rendered as “martial world” in a wuxia context versus “underworld” in a social context. The Reconstruction stage then guides students to synthesize optimal elements into new translations. Finally, Transcendence requires creating bilingual cultural annotation guides that elucidate equivalence mechanisms, such as explaining how “说曹操曹操到” parallels “speak of the devil”. By transforming AI output into a whetstone for critical thinking, this chain propels learners from passive recipients to active constructors of cultural meaning. • 5.1.3 Metacognitive Logging and Ethical Risk Prevention The Cognitive Reconstruction Layer uses metacognitive logs to prompt students to record the rationale behind translation decisions (e.g., “Rejected literal ‘hide brightness’ for ‘韬光养晦’ as it loses the ‘strategic restraint’ political connotation”), fostering their evolution from technology users to strategy designers. In the Ethical Co-construction Layer, a Cultural Security Protocol enforces an “AI initial screening + teacher dual review” mechanism for sensitive terms (e.g., mandating “一带一路” as “Belt and Road Initiative” with notes on ideological differences from the “Marshall Plan”). A dependency blocking mechanism is also established, conducting weekly “AI-free days” for traditional translation training to curb technology dependence (Fan Daqi & Sun Lin, 2023; Li Zhengtao, 2023). This system builds a human-AI collaborative risk prevention loop through dual-track cognitive monitoring and ethical review. 5.2 PREP Closed-Loop Teaching Framework: Incubator for Translation Competence in the AI Era To operationalize the theoretical model, the “PREP Four-Stage Cycle” teaching framework was constructed (Table 2 ): Table 2 PREP Framework Stage Core Task Technology-Enabled Strategy Preview: Activate background schema Activate Background Schema ChatGPT generates topic knowledge graphs (e.g., semantic network for “quantum entanglement-superconducting chip-topological quantum” before translating “quantum computing”). Render: Collaborative Translation Stepwise Translation Optimization 1. AI generates base translation (70%) → 2. Student optimizes language & cultural fit (85%) → 3. Teacher calibrates key concepts (92%). Evaluate: Multidimensional Assessment Process-Oriented Evaluation Machine scoring (40%, BLEU + term consistency) + Peer review (30%, 3 strengths + 1 suggestion) + Teacher evaluation (30%, depth of cultural transfer) Ponder: Metacognitive Reflection Making Decision Logic Explicit Write Human-AI Collaboration Logs answering core questions: “Did AI suggestions improve cultural conveyance? Was probabilistic output blindly accepted?” • 5.2.1 Innovative Practice Cases Cases focused on culture-loaded term handling and translation verification mechanisms, achieving deep translation training through human-AI collaboration. Culture-Loaded Term Collaboration: Translating “红白喜事”, the student initially received ChatGPT’s literal “red and white events”, triggering a cultural warning (yellow alert) indicating cognitive conflict. Guided to search the cultural database, the student discovered the lack of a “喜丧” (happy funeral) concept in Western culture, ultimately adopting “weddings and funerals” with an annotation: “In traditional Chinese culture, white symbolizes death, but ‘白事’ (white event/funeral) can also be seen as a ritualistic celebration of life completion,” preserving core meaning while achieving cultural adaptation. Back-Translation Verification: Students back-translate AI-generated translations into the source language for comparison. For example, the AI translation of “亡羊补牢” (mend the pen after sheep are lost) was “mend the fold after sheep are lost”. Back-translation yielded “lock the stable door after the horse is stolen,” revealing a clear semantic deviation from the original English proverb. This reverse verification mechanism visually exposes distortions in cultural image transfer, strengthening students' awareness of metaphor migration sensitivity. 5.3 Risk Control Matrix: Systematic Mitigation of Challenges Addressing the three major risks identified, a graded response strategy matrix was constructed (Table 3 ): Table 3 AI Risk Response Matrix for Translation Teaching Risk Type Probability Severity Solution Practice Case Cultural Mistranslation 38% High Cultural Glossary DB + Dual Review Add entry “龙→loong” Technology Dependence 27% Medium “AI-Free Days” + Metacognitive Training Weekly traditional translation session Academic Integrity Crisis 23% Very High Process Tracing + Stylometric Analysis Detect AI features (excessive fluency/low personal style) 6. Educational Practice Recommendations: Towards a Human-AI Symbiotic Translation Education Ecosystem 6.1 Restructuring the Curriculum System Responding to the deep penetration of intelligent technology, this study proposes a “Three-Three” modular curriculum restructuring: Foundation Layer (30%): Focuses on AI-assisted technical skills training, emphasizing Prompt Engineering (e.g., optimizing collaboration via precise instructions like “Use domestication strategy for the following text, retain metaphors but use English idioms”). Practice shows this can improve BLEU scores by over 15% (Doherty, 2023). Culture Layer (30%): Focuses on deep contrastive analysis of Chinese and Western thought patterns, systematically cultivating cultural transference decision-making competence by analyzing ethical connotation differences in culture-loaded terms (e.g., contrasting the value orientation conflict between Chinese “雪中送炭” [sending charcoal in snowy weather] and English “help a lame dog over a stile”) (Hu Kaibao, 2023). Creation Layer (40%): Breaks through AI’s creative limitations. Students analyze multiple classic text translations (e.g., comparing Legge and Lau translations of Dao De Jing) to grasp linguistic essence, and create bilingual cultural annotation guides (Zhou Zhongliang, 2023). School-based materials should feature task-driven content chains. A typical case requires students to use ChatGPT to translate The Analects phrase “君子和而不同” (The gentleman harmonizes without being uniform), systematically compare philosophical conveyance differences in Arthur Waley and Ku Hung-ming’s translations, and write a 200-word analysis justifying the optimal translation’s cultural fit (Wang Huashu, 2022). Such designs shift students from passive technology recipients to active constructors of cultural meaning. 6.2 Strategic Transformation of Teacher Roles The AI era demands teachers transition from knowledge transmitters to human-AI collaborative designers. This study proposes a three-tier AI Teaching Competency certification system: L1 Tool Operator: Requires proficiency in prompt engineering and data interpretation (e.g., dynamically adjusting prompts to achieve 92% term consistency in political text translation) (Fan Daqi & Sun Lin, 2023). L2 Curriculum Designer: Must develop human-AI collaborative lesson plans. A representative example is the course “AI-Assisted Translation of Government Work Reports,” integrating glossary building, cultural metaphor conversion, and ideological review modules (Jiao Jianli, 2023). L3 Ethical Steward: Must guide students in identifying ideological risks of technology use (e.g., analyzing the political connotations of “common prosperity,” “shared prosperity,” and “collective affluence”) to foster a critical technological perspective (Li Zhengtao, 2023). The key path for competency renewal involves regular Human-AI Collaborative Teaching Workshops, operating on a “Teacher provides objectives & cultural insights → ChatGPT generates base materials → Jointly develop tiered tasks” model. For a “carbon neutrality” theme: primary tasks focus on precise term matching (e.g., “碳达峰” to “peak carbon emissions”); advanced tasks require cross-civilization metaphor conversion (e.g., reconstructing the agrarian imagery in “绿色转型” [green transition] for an industrial civilization context) (Williams & Hessen, 2023). This process drives teachers’ evolution from technology users to educational value re-shaper. 7. Conclusion: Dialectical Unity of Technological Empowerment and Humanistic Spirit This study systematically reveals the dual effects of ChatGPT in college English translation teaching through empirical research. The practice of the Tri-Dimensional Integration Model (Technology-Cognition-Ethics) and the PREP Closed-Loop Framework (Preview-Render-Evaluate-Ponder) confirms: ChatGPT’s instant feedback mechanism (response latency < 1.2 seconds) and cognitive offloading function (working memory load reduced by 37%) increased translation accuracy by 27% (*p*<0.01), complex sentence processing efficiency by 41%, and learning motivation confidence by ES = 0.91. These data highlight AI’s significant value in optimizing teaching processes and reducing cognitive load (Zhou Zhongliang, 2023). However, technological empowerment consistently accompanies humanistic challenges. The exposed 38% cultural mistranslation rate (e.g., literal “hide brightness” for “韬光养晦” losing “strategic restraint” connotation), 27% of students showing technology dependence (inability to verify proverb translations independently without AI), and ideological deviations in political terms (e.g., mistranslating “共同富裕” as “common prosperity” instead of “shared prosperity”) jointly warn us to uphold the core principle of “Human-led, AI-empowered” (Fan Daqi & Sun Lin, 2023). While machines can convert words precisely, the irreplaceable value of human teachers lies in interpreting the “untranslatable” depths of culture—in the semantic chasm between “龙” (loong) and “dragon”, in the ethical coding of “红白喜事” (red and white events)—guarding the spiritual DNA behind language (Li Xiaoxiang, 2021). Looking forward, translation education requires a triple deep transformation: (1) Goal Restructuring: Shift from language conversion to cultural transference, cultivating students’ ability to infuse AI translations with cultural annotations (e.g., parsing the dual semantics of “江湖” in martial arts and social contexts). (2) Competence Evolution: Move beyond tool application, strengthening ethical risk insight through “Technology Critique Workshops” analyzing AI’s systemic biases (Williams & Hessen, 2023). (3) Paradigm Innovation: Explore brain science-inspired instructional design, using fMRI technology to optimize the match between AI feedback and cognitive load (Thomas, 2022). The study also reveals three unresolved challenges: localization bottlenecks in low-resource language support (mistranslation rates exceeding 52% for “Belt and Road” languages), a 19% decline in metaphorical creativity (*p*=0.03) due to long-term AI use, and the hidden peril of educational equity exacerbated by the digital divide (benefits for underdeveloped regions only 63% of those in developed areas). These challenges demand we constantly calibrate technology’s course with the humanistic spirit—when the light of code illuminates the classroom, educators must still guard the lamp named “Humanistic Spirit”, for true communication always occurs between souls, not servers. Declarations Ethical approval The study involving human participants was reviewed and approved by the Academic Ethics Committee of Jilin International Studies University, and their participation has been officially approved by the university. All human-related procedures were performed in accordance with relevant guidelines and regulations. Consent to participate Informed consent was obtained from all individual participants included in the study. Prior to the commencement of the research, all participants were fully informed about the study's purpose, procedures, potential risks and benefits, confidentiality measures, and their right to withdraw at any time without penalty. Consent to publish Consent for publication was obtained from all individual participants included in the study. Participants consented to the publication of the research findings in an anonymized format, ensuring their personal data remain confidential. Competing interests The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Author contribution I am the sole principal investigator of this research, and the entirety of the work has been conducted by myself. Declaration of funding Although this study is affiliated with several research projects, these projects have not received substantial financial support, though some funding may be provided after the project's completion. Projects : Ministry of Education Vocational College Education and Teaching Reform Project: “Research on Governance Policies of Online Language Use in University English Teaching under the Background of Artificial Intelligence” (2025JGYB045);2025 “14th Five-Year Plan” Special Project on “Reading and Teacher Development” by the Tao Xingzhi Research Association of China: “Application of Multimodal Generative AI in College English Creative Writing Teaching” (202513164JN);2024 Jilin Province Higher Education Research Project: “Exploration and Practice of ChatGPT in College English Translation Teaching”(JGJX24D1074). References Brown, T., et al. (2020). Language models are few-shot learners. NeurIPS , 33, 1877–1901. Doherty, S. (2023). ChatGPT in translation education. The Interpreter and Translator Trainer , 17(2), 1–20. Fan, Daqi, & Sun, Lin. (2023). Practical paths for political and ideological awareness of translators under the perspective of building a discourse system with Chinese characteristics for international communication. Journal of Beijing International Studies University , 45(1), 80–90. Feng, Zhiwei, & Zhang, Dengke. (2023). GPT and language studies. Computer-Assisted Foreign Language Education , (2), 3–11+105. Guo, Wenjie. (2024). Challenges, strategies, and implications of ChatGPT in English translation courses. Journal of Guizhou Normal College , (3). Hew, K. F., Huang, W., Du, J., et al. (2023). Using chatbots to support student goal setting and social presence in fully online activities. Journal of Computing in Higher Education , 35(1), 40–68. Hu, Kaibao. (2023). ChatGPT and innovation in translation education. Shanghai Journal of Translators , (2), 1–6. Hubbard, D. W. (2009). The failure of management: Why it’s broken and how to fix it . John Wiley & Sons, Inc., Hoboken. Ioannidis, C., Pym, D. J., & Williams, J. M. (2013). Sustainability in information stewardship: Time preferences, externalities, and social co-ordination. The Twelfth Workshop on the Economics of Information Security . Jeon, J. (2021). Chatbot-assisted dynamic assessment (CA-DA) for L2 vocabulary learning and diagnosis. Computer Assisted Language Learning , 36(3), 1–24. Jiao, Jianli. (2023). Dialogic AI reshaping the educational ecosystem. Educational Research , (6), 88–95. Kenney, M. (2022). Deep learning for cognitive translation studies. Translation Spaces , 11(1), 1–25. Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal , 54(2), 1–14. Laviosa, S. (2021). Corpora in translator education . Routledge. Li, Xiaoxiang. (2021). Theoretical and practical research on the “Three-Wide Education” of foreign language curriculum . Southeast University Press. Li, Zhengtang. (2023). Facing ChatGPT: How can teachers survive in crisis? Research in Educational Development , 43(10), 1–9. Lu, Yu, et al. (2023). Educational applications and prospects of generative AI: A case study of the ChatGPT system. Distance Education in China , (4), 24–31+51. Mijwil, M. M., Hiran, K. K., Doshi, R., et al. (2023). ChatGPT and the future of academic integrity. Al-Salam Journal for Engineering and Technology , 2(2), 116–127. Moorkens, J. (2020). Translation quality assessment. Machine Translation , 34(3), 1–28. Niu, Min. (2024). Innovation of practical training models in the “Business English Translation” course for vocational colleges. Vocational Education Forum , (2), 45–52. O’Brien, S. (2021). Human-AI collaboration in translation. Frontiers in AI , 4, 1–10. Pym, A. (2023). ChatGPT and translation pedagogy. Journal of Specialised Translation , 39, 15–32. Thomas, C. A. (2022). Guiding the study of lived experience through an autoethnographic approach. In D. Clover, K. Sanford & W. S. Allen (Eds.), Academic project designs and methods: From professional development to critical and creative practice (pp. 231–237). University of Victoria. Vieira, L. (2020). Post-editing in translation education. Translation and Interpreting Studies , 15(1), 1–20. Vygotsky, L. (1978). Mind in society: The development of higher psychological processes . Harvard University Press, Cambridge, Massachusetts. Wang, Huashu. (2021). Research on translation education technology in the era of artificial intelligence: Issues and countermeasures. Chinese Translators Journal , (3), 84–88. Wang, Tian’en. (2023). The characteristics, educational significance, and problem response of ChatGPT. Ideological and Theoretical Education , (4), 19–25. Wang, Youmei, et al. (2023). Ethical risks and avoidance approaches in the educational application of ChatGPT. Open Education Research , 29(2), 26–35. Williams, R., & Hessen, D. J. (2023). The impact of generative AI on higher-order thinking. npj Science of Learning , 8(1), 1–9. Wu, Y., et al. (2020). Duolingo’s AI-driven language learning. AIED Proceedings , 112–124. Xu, Huifu. (2023). The crisis of higher education in the era of weak artificial intelligence. Open Education Research , 31(4), 15–22. Zhang, M. (2023). Cultural intelligence in AI translation. Cross-Cultural Communication , 19(1), 1–15. Zhao, L. (2021). ChatGPT-assisted EFL teaching in China. Computer Assisted Language Learning , 38(4), 1–23. Zhou, Zhongliang. (2023). The application of ChatGPT in translation teaching: Changes, challenges, and responses. Foreign Language Teaching and Research , (5), 22–30. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8729921","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595036158,"identity":"1e67e79f-9cec-422a-8d5e-0d9c736f1221","order_by":0,"name":"Liang Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYJCCAw8YJIAk84EDH34QqyUBrIUt8eDMHmKtSQBr5DE+zMFGhGr52c0PDyTUWOTx3cj5cJiBh0GeX+wAfi0Gd44ZHEg4JlEseSN3w+ECCwbDmbMTCGiRSDA4kNggkbgBpGUGD0OCwW0CWuRnpH+Aasl5cJiHjQgtDDdyYLbkMBCnxeBGTgHIL4kzzzwzAAayBGG/AB22+cOHmrrEvuPJjz98+GEjzy9NyGFwIABWKUGschDgP0CK6lEwCkbBKBhJAAD7gFBCoL493wAAAABJRU5ErkJggg==","orcid":"","institution":"jilin international studies university","correspondingAuthor":true,"prefix":"","firstName":"Liang","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2026-01-29 10:01:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8729921/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8729921/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103236943,"identity":"2702e35d-1700-41a2-813c-b792ccb3d2cb","added_by":"auto","created_at":"2026-02-23 13:22:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136609,"visible":true,"origin":"","legend":"\u003cp\u003eTri-Dimensional Integration Model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8729921/v1/6ecc98f6127ac7ec0b34e76a.png"},{"id":104549680,"identity":"188e0a53-f4dd-4601-ab62-5b1d0f511585","added_by":"auto","created_at":"2026-03-13 07:56:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":998696,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8729921/v1/b2c5f643-f27d-4051-bb45-ff8098fedbd1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effectiveness, Risks, and Pedagogical Reconstruction in College English Translation Teaching via a ChatGPT-Based Tri-Dimensional Integration Model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe iterative development of artificial intelligence (AI) technology is profoundly reshaping the educational landscape. According to the latest statistics from the Ministry of Education\u0026rsquo;s \u0026ldquo;Education Informatization 2.0 Action Plan,\u0026rdquo; the penetration rate of intelligent educational tools in universities reached 78.6% by the third quarter of 2023, with language learning applications experiencing annual growth exceeding 42%. Amidst this technological revolution, OpenAI\u0026rsquo;s ChatGPT, with its 175-billion-parameter scale and cross-lingual deep generative capabilities, offers a disruptive solution to traditional translation pedagogy. Current college English translation teaching faces three structural dilemmas: firstly, a resource bottleneck, where the student-to-teacher ratio remains high at approximately 1:120, severely compromising personalized feedback mechanisms; secondly, cognitive overload, with complex sentence parsing consuming over 60% of classroom time; and thirdly, cultural transmission barriers, with research indicating that about 42% of non-English majors commit systematic mistranslations due to deficient cultural schemata (Li Xiaoxiang, 2021). These deep-seated contradictions urgently require paradigm shifts enabled by intelligent technology.\u003c/p\u003e \u003cp\u003eChatGPT, as a prime example of generative AI (AIGC), excels in contextualized knowledge construction through its Transformer architecture. Wang Tian\u0026rsquo;en (2023) points out in his monograph Artificial Intelligence and Educational Transformation that ChatGPT\u0026rsquo;s cognitive mechanism comprises three key layers: the foundational layer builds a language probability model through training on 175\u0026nbsp;billion tokens; the reinforcement learning layer aligns values through Reinforcement Learning from Human Feedback (RLHF); and the application layer iterates knowledge through continuous dialogue. This architecture endows it with four pedagogical values in translation teaching: providing instant feedback with sub-second response latency, enabling precisely stratified personalized guidance, reducing working memory load, and fostering cross-cultural metacognitive development. However, its application also carries potential risks such as the cultural discount effect, technology dependence syndrome, and academic integrity crises.\u003c/p\u003e \u003cp\u003eThis study empirically explores the efficacy boundaries of ChatGPT in college English translation teaching using a mixed-methods approach, aiming to address three core questions: Firstly, can AI assistance substantially enhance translation accuracy and cognitive efficiency? Secondly, what cognitive and ethical risks arise during technological integration? Thirdly, how can a novel educational paradigm of human-AI collaboration be constructed? The findings will provide empirical evidence for language teaching transformation in the intelligent era, driving a strategic shift in translation education from instrumental training towards creative empowerment.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical Foundations of Technology Integration\u003c/h2\u003e \u003cp\u003eThe technological integration in translation pedagogy requires robust theoretical underpinnings. Hubbard\u0026rsquo;s (2009) Technology-Enhanced Learning (TEL) framework emphasizes that educational technology design must adhere to the \u0026ldquo;cognition-first\u0026rdquo; principle, meaning tools should serve deep learning goals rather than superficial efficiency. In translation studies, Pym\u0026rsquo;s (2013) \u0026ldquo;minimal error principle\u0026rdquo; advocates that technological assistance should keep semantic deviations within an acceptable threshold. This view finds validation in neurolinguistic research\u0026mdash;fMRI scans show abnormal activation in learners\u0026rsquo; Broca\u0026rsquo;s area (the language production center) when translation error rates exceed 15% (Perfetti, 2018).\u003c/p\u003e \u003cp\u003eFrom a cognitive development perspective, ChatGPT's instant feedback mechanism perfectly aligns with Vygotsky\u0026rsquo;s Zone of Proximal Development theory. Vygotsky (1978) emphasized the scaffolding role of social interaction in cognitive development. As a digital cognitive partner, ChatGPT provides appropriate support for learners at different levels by dynamically adjusting feedback granularity (from lexical substitution suggestions to discourse reconstruction strategies). Cutting-edge neuroscience research confirms that receiving instant feedback increases neural coupling strength between the prefrontal cortex and hippocampus by 37%, significantly higher than in traditional teaching modes (Thomas, 2022). This provides neurobiological evidence for ChatGPT\u0026rsquo;s cognitive offloading function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Realistic Challenges in Translation Teaching\u003c/h2\u003e \u003cp\u003eCurrent college English translation teaching is mired in multi-dimensional difficulties. A national survey of 42 universities by Li Xiaoxiang (2021) revealed that only 28% of students believed the existing teaching model effectively enhanced their translation competence. These challenges can be summarized across three dimensions:\u003c/p\u003e \u003cp\u003eInstructional Structure Level: Traditional classrooms suffer from a vicious cycle of \u0026ldquo;unidirectional lecturing - passive practice - delayed feedback.\u0026rdquo; Wang Huashu (2021) sharply observes in An Introduction to Translation Technology the \u0026ldquo;three deficiencies phenomenon\u0026rdquo;: deep teacher-student interaction accounts for less than 15% of class time, practical training constitutes below 30%, and instant feedback coverage is less than 20%. This structural defect perpetuates cognitive gaps.\u003c/p\u003e \u003cp\u003eStudent Competence Level: Dual weaknesses in native and second language proficiency create expressive dilemmas. Non-English majors commonly exhibit \u0026ldquo;grammatical correctness but pragmatic failure,\u0026rdquo; particularly showing systematic bias in handling culture-loaded terms. For instance, translating \u0026ldquo;亡羊补牢\u0026rdquo; (mending the pen after sheep are lost) literally as \u0026ldquo;mend the fold after sheep are lost,\u0026rdquo; ignoring the English equivalent proverb \u0026ldquo;lock the stable door after the horse is stolen\u0026rdquo; (Nida, 2020).\u003c/p\u003e \u003cp\u003eResource Provision Level: Large class sizes and faculty shortages lead to guidance deficits. The average turnaround time for translation assignment feedback is 72 hours, causing students to miss the optimal correction window. Simultaneously, standardized textbooks cover less than 45% of necessary cultural content, failing to meet authentic translation scenario demands (Zhang Zhenyu, 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Educational Potential of Intelligent Technology\u003c/h2\u003e \u003cp\u003eGenerative AI exhibits dual characteristics in translation teaching applications. Kohnke\u0026rsquo;s (2023) empirical study in the SSCI journal Computer Assisted Language Learning showed ChatGPT responds to vocabulary translation tasks in less than 0.8 seconds with 91.5% accuracy in grammar correction. A 2023 meta-analysis in the Nature partner journal npj Science of Learning, synthesizing 51 global studies, confirmed an effect size (ES) gain of 0.867 for learning outcomes and 0.457 ES for higher-order thinking skills in AI-assisted groups (Williams \u0026amp; Hessen, 2023). Specifically in teaching scenarios, ChatGPT demonstrates four core values:\u003c/p\u003e \u003cp\u003e* Personalized Scaffolding: Dynamically adjusts output based on learner level (e.g., providing literal translations for beginners, cultural annotations for advanced learners).\u003c/p\u003e \u003cp\u003e* Process Visualization: Shows translation decision chains via version comparison features.\u003c/p\u003e \u003cp\u003e* Cultural Mediator: Parses connotations of culture-specific items (e.g., contrasting the value differences of \u0026ldquo;individualism\u0026rdquo; in Eastern and Western contexts).\u003c/p\u003e \u003cp\u003e* Metacognitive Coach: Guides reflection through probing questions (e.g., \u0026ldquo;Why choose domestication over foreignization strategy?\u0026rdquo;).\u003c/p\u003e \u003cp\u003eHowever, its limitations warrant equal caution. In handling deep cultural structures, ChatGPT exhibits cultural discounting, such as translating the Chinese philosophical concept \u0026ldquo;无为\u0026rdquo; as \u0026ldquo;inaction\u0026rdquo; rather than the more accurate \u0026ldquo;effortless action\u0026rdquo; (Wu, 2022). More seriously, its statistical learning nature leads to hallucinatory outputs in low-resource language pairs, such as mistranslating the Yunnan dialect \u0026ldquo;板扎\u0026rdquo; (excellent) as \u0026ldquo;board tight\u0026rdquo; (Chen, 2023).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Design","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Methodological Framework\u003c/h2\u003e \u003cp\u003eThis study employed an explanatory sequential mixed-methods design, implemented in three phases:\u003c/p\u003e \u003cp\u003eQuantitative Experimental Phase: A quasi-experimental design was used. 60 non-English major juniors were randomly assigned to an experimental group (ChatGPT-assisted, n\u0026thinsp;=\u0026thinsp;30) and a control group (traditional teaching, n\u0026thinsp;=\u0026thinsp;30). Informed consent was obtained from all student participants involved in the study. Participants were informed about the research purpose, procedures, potential risks and benefits, and their right to withdraw at any time without penalty. A pre-test independent samples t-test showed no significant difference in translation ability (t\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The experimental group received an 8-week systematic intervention, completing 2 multi-genre translation exercises weekly (political-economic 30%, scientific-technical 30%, literary 40%). A \u0026ldquo;Dual-loop Learning Model\u0026rdquo; was implemented: the inner loop was a learning cycle of \u0026ldquo;Preview-Translate-AI Feedback-Reflection\u0026rdquo;; the outer loop was a teaching cycle of \u0026ldquo;Task Design-AI Analysis-Intensive Lecture Deepening.\u0026rdquo;\u003c/p\u003e \u003cp\u003eQualitative Inquiry Phase: 12 students from the experimental group underwent semi-structured interviews (45\u0026ndash;60 minutes each), focusing on four dimensions: technology acceptance, cognitive strategy shifts, cultural understanding pathways, and ethical risk perception. Interview data underwent three-level coding using NVivo 12, yielding 7 core categories.\u003c/p\u003e \u003cp\u003eModel Construction Phase: Based on empirical findings and integrating social constructivism and connectivism theories, a \u0026ldquo;Tri-Dimensional Integration Model\u0026rdquo; was proposed and refined through two rounds of the Delphi expert method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Measurement Tool Validation\u003c/h2\u003e \u003cp\u003eTo ensure assessment validity, multi-dimensional standardized tools were used:\u003c/p\u003e \u003cp\u003eTranslation Accuracy Assessment: Used the Sinicized version of the China Standards of English (CSE) scale, comprising semantic conveyance (40%), textual coherence (30%), cultural adaptation (20%), and technical norms (10%). Two blind reviewers scored independently, achieving an Intraclass Correlation Coefficient (ICC) of 0.89. Cognitive Load Measurement: Used the NASA-TLX scale (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.91). Learning Motivation Assessment: Based on Keller\u0026rsquo;s ARCS model questionnaire (Kaiser-Meyer-Olkin measure of sampling adequacy\u0026thinsp;=\u0026thinsp;0.87, suitable for factor analysis).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Intervention Implementation\u003c/h2\u003e \u003cp\u003eThe experimental group used a tiered intervention strategy:\u003c/p\u003e \u003cp\u003eTechnology Embedding Layer: Built a dynamic corpus system integrating ChatGPT and Snowman CAT toolchains, enabling side-by-side comparison of translations from 8 engines (e.g., DeepSeek, Google MT). An error heatmap system was developed to automatically flag three types of errors: grammatical errors (red alert), cultural missteps (yellow alert), logical breaks (blue alert).\u003c/p\u003e \u003cp\u003eCognitive Reconstruction Layer: Implemented metacognitive monitoring training. Students documented key decision-making thought processes via \u0026ldquo;Human-AI Collaboration Logs.\u0026rdquo; For example, when translating \u0026ldquo;画蛇添足\u0026rdquo; (draw legs on a snake), they had to explain: \u0026ldquo;Rejected AI\u0026rsquo;s literal \u0026lsquo;draw legs on a snake\u0026rsquo; and chose the English proverb \u0026lsquo;gild the lily\u0026rsquo; as it better aligns with target culture cognition.\u0026rdquo;\u003c/p\u003e \u003cp\u003eEthical Co-construction Layer: Established a Cultural Security Protocol mandating dual review for politically or religiously sensitive terms. For instance, \u0026ldquo;人类命运共同体\u0026rdquo; was required to be translated as \u0026ldquo;a community with a shared future for mankind\u0026rdquo; not \u0026ldquo;global community,\u0026rdquo; ensuring ideological accuracy.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Research Findings","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Quantitative Analysis Results\u003c/h2\u003e \u003cp\u003ePre-post-test comparisons of translation ability showed the experimental group achieved significant improvement in accuracy (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePre-post-test comparisons\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-test (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-test (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImprovement Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperimental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.37**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.12*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Note: **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003eIn-depth analysis revealed a 41% improvement in complex sentence processing efficiency within the experimental group (6.2min \u0026rarr; 3.7min), attributed to ChatGPT\u0026rsquo;s syntax visualization feature\u0026mdash;parsing complex clauses into tree diagrams to reduce working memory load. Cognitive load measurement showed the experimental group\u0026rsquo;s NASA-TLX total score was significantly lower than the control group (t\u0026thinsp;=\u0026thinsp;5.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the mental demand dimension showing the largest difference (d\u0026thinsp;=\u0026thinsp;0.91). For learning motivation, the confidence dimension of the ARCS scale increased by 0.91 ES, indicating technology significantly boosted self-efficacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Qualitative Research Findings\u003c/h2\u003e \u003cp\u003eGrounded theory analysis of interview data yielded four core themes:\u003c/p\u003e \u003cp\u003eCognitive Value of Instant Feedback: Cited as the primary benefit by 62% of respondents. Student S09 described: \u0026ldquo;Before, corrections took days. Now ChatGPT points out three subject-verb agreement errors in 20 seconds and explains English hypotaxis. This immediacy creates a positive learning cycle.\u0026rdquo; Educational neuroscience research shows such timely reinforcement can increase long-term memory encoding efficiency by 40% (Duke, 2022).\u003c/p\u003e \u003cp\u003eBidirectional Deepening of Cultural Understanding: Evident in 34% of interview transcripts. Student S07 illustrated: \u0026ldquo;Translating \u0026lsquo;红白喜事\u0026rsquo; (red and white events), ChatGPT not only gave the literal translation but also contrasted Chinese and Western funeral customs, suggesting \u0026lsquo;weddings and funerals\u0026rsquo; in cross-cultural contexts.\u0026rdquo; This comparison moved students beyond language conversion into exploring deep cultural structures.\u003c/p\u003e \u003cp\u003eTechnology Dependence Risk: Mentioned in 25% of statements. Student S04 admitted: \u0026ldquo;Once I forgot to use ChatGPT, I hesitated to confirm the translation of \u0026lsquo;carry coal to Newcastle.\u0026rsquo; This tool anxiety is alarming.\u0026rdquo; More seriously, AI exhibited ideological bias in specific domains, such as translating \u0026ldquo;共同富裕\u0026rdquo; as \u0026ldquo;common prosperity\u0026rdquo; instead of the internationally accepted \u0026ldquo;shared prosperity\u0026rdquo; (Student S11).\u003c/p\u003e \u003cp\u003eTypical Mistranslation Cases Revealing Technical Limitations:\u003c/p\u003e \u003cp\u003e\u0026ldquo;ChatGPT translated the diplomatic term \u0026lsquo;韬光养晦\u0026rsquo; as \u0026lsquo;hide brightness, nourish obscurity,\u0026rsquo; completely losing its connotation of \u0026lsquo;strategic restraint\u0026rsquo;\u0026rdquo;(Student S07).\u003c/p\u003e \u003cp\u003e\u0026ldquo;AI suggested translating \u0026lsquo;说曹操曹操到\u0026rsquo; as \u0026lsquo;speak of Cao Cao and he appears,\u0026rsquo; but didn\u0026rsquo;t note the English proverb \u0026lsquo;speak of the devil\u0026rsquo; is more idiomatic\u0026rdquo; (Student S08).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Theoretical Model and Teaching Framework: Paradigm Reconstruction for Human-AI Collaboration","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Tri-Dimensional Integration Model: Dynamic Balance of Technology-Cognition-Ethics\u003c/h2\u003e \u003cp\u003eBased on empirical data and theoretical foundations, this study proposes the \u0026ldquo;Technology-Cognition-Ethics\u0026rdquo; Tri-Dimensional Integration Model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), providing a systematic framework for ChatGPT application in translation teaching:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u0026bull; 5.1.1 Technology Embedding Layer: Optimizing Instrumental Rationality\u003c/p\u003e \u003cp\u003eFocuses on using intelligent tools to enhance translation precision and efficiency. A core component is the multi-engine comparison system, integrating outputs from eight mainstream engines (e.g., ChatGPT, DeepSeek, Google, MT) and automatically tagging key differences. For instance, translating \u0026ldquo;青山绿水\u0026rdquo; (green mountains and clear waters) would display literal (\u0026ldquo;blue mountains and green rivers\u0026rdquo;), free (\u0026ldquo;lush mountains and clear waters\u0026rdquo;), and culturally substituted (\u0026ldquo;scenic landscape\u0026rdquo;) versions, guiding students to analyze each version\u0026rsquo;s cultural adaptability and contextual suitability. Another key tool is the error heatmap generator, using AI to identify and categorize three core issues: grammatical errors (red alert), cultural missteps (yellow alert), and logical breaks (blue alert), creating personalized error distribution maps. This visual diagnostic efficiently reveals student weaknesses. For example, if a student fails to discern the subtle ideological difference between \u0026ldquo;common prosperity\u0026rdquo; and \u0026ldquo;shared prosperity\u0026rdquo; in political text translation, the system triggers a yellow alert (cultural misstep), providing immediate, targeted feedback to improve translation sensitivity and accuracy.\u003c/p\u003e \u003cp\u003e\u0026bull; 5.1.2 Cognitive Reconstruction Layer: Strategies for Higher-Order Competence Development\u003c/p\u003e \u003cp\u003eThis pedagogical approach cultivates advanced competencies through a four-stage progressive task chain. It begins with Deconstruction, where ChatGPT generates three translation variants (literal, free, creative) of a source text. Next, the Critique phase organizes group debates analyzing cultural adaptability\u0026mdash;for instance, examining why \u0026ldquo;江湖\u0026rdquo; might be rendered as \u0026ldquo;martial world\u0026rdquo; in a wuxia context versus \u0026ldquo;underworld\u0026rdquo; in a social context. The Reconstruction stage then guides students to synthesize optimal elements into new translations. Finally, Transcendence requires creating bilingual cultural annotation guides that elucidate equivalence mechanisms, such as explaining how \u0026ldquo;说曹操曹操到\u0026rdquo; parallels \u0026ldquo;speak of the devil\u0026rdquo;. By transforming AI output into a whetstone for critical thinking, this chain propels learners from passive recipients to active constructors of cultural meaning.\u003c/p\u003e \u003cp\u003e\u0026bull; 5.1.3 Metacognitive Logging and Ethical Risk Prevention\u003c/p\u003e \u003cp\u003eThe Cognitive Reconstruction Layer uses metacognitive logs to prompt students to record the rationale behind translation decisions (e.g., \u0026ldquo;Rejected literal \u0026lsquo;hide brightness\u0026rsquo; for \u0026lsquo;韬光养晦\u0026rsquo; as it loses the \u0026lsquo;strategic restraint\u0026rsquo; political connotation\u0026rdquo;), fostering their evolution from technology users to strategy designers. In the Ethical Co-construction Layer, a Cultural Security Protocol enforces an \u0026ldquo;AI initial screening\u0026thinsp;+\u0026thinsp;teacher dual review\u0026rdquo; mechanism for sensitive terms (e.g., mandating \u0026ldquo;一带一路\u0026rdquo; as \u0026ldquo;Belt and Road Initiative\u0026rdquo; with notes on ideological differences from the \u0026ldquo;Marshall Plan\u0026rdquo;). A dependency blocking mechanism is also established, conducting weekly \u0026ldquo;AI-free days\u0026rdquo; for traditional translation training to curb technology dependence (Fan Daqi \u0026amp; Sun Lin, 2023; Li Zhengtao, 2023). This system builds a human-AI collaborative risk prevention loop through dual-track cognitive monitoring and ethical review.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.2 PREP Closed-Loop Teaching Framework: Incubator for Translation Competence in the AI Era\u003c/h2\u003e \u003cp\u003eTo operationalize the theoretical model, the \u0026ldquo;PREP Four-Stage Cycle\u0026rdquo; teaching framework was constructed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePREP Framework\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCore Task\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTechnology-Enabled Strategy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreview: Activate background schema\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActivate Background Schema\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChatGPT generates topic knowledge graphs (e.g., semantic network for \u0026ldquo;quantum entanglement-superconducting chip-topological quantum\u0026rdquo; before translating \u0026ldquo;quantum computing\u0026rdquo;).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRender: Collaborative Translation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStepwise Translation Optimization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. AI generates base translation (70%) \u0026rarr;\u003c/p\u003e \u003cp\u003e2. Student optimizes language \u0026amp; cultural fit (85%) \u0026rarr;\u003c/p\u003e \u003cp\u003e3. Teacher calibrates key concepts (92%).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluate: Multidimensional Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProcess-Oriented Evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMachine scoring (40%, BLEU\u0026thinsp;+\u0026thinsp;term consistency) +\u003c/p\u003e \u003cp\u003ePeer review (30%, 3 strengths\u0026thinsp;+\u0026thinsp;1 suggestion) +\u003c/p\u003e \u003cp\u003eTeacher evaluation (30%, depth of cultural transfer)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePonder: Metacognitive Reflection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaking Decision Logic Explicit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWrite Human-AI Collaboration Logs answering core questions: \u0026ldquo;Did AI suggestions improve cultural conveyance? Was probabilistic output blindly accepted?\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026bull; 5.2.1 Innovative Practice Cases\u003c/p\u003e \u003cp\u003eCases focused on culture-loaded term handling and translation verification mechanisms, achieving deep translation training through human-AI collaboration.\u003c/p\u003e \u003cp\u003eCulture-Loaded Term Collaboration: Translating \u0026ldquo;红白喜事\u0026rdquo;, the student initially received ChatGPT\u0026rsquo;s literal \u0026ldquo;red and white events\u0026rdquo;, triggering a cultural warning (yellow alert) indicating cognitive conflict. Guided to search the cultural database, the student discovered the lack of a \u0026ldquo;喜丧\u0026rdquo; (happy funeral) concept in Western culture, ultimately adopting \u0026ldquo;weddings and funerals\u0026rdquo; with an annotation: \u0026ldquo;In traditional Chinese culture, white symbolizes death, but \u0026lsquo;白事\u0026rsquo; (white event/funeral) can also be seen as a ritualistic celebration of life completion,\u0026rdquo; preserving core meaning while achieving cultural adaptation.\u003c/p\u003e \u003cp\u003eBack-Translation Verification: Students back-translate AI-generated translations into the source language for comparison. For example, the AI translation of \u0026ldquo;亡羊补牢\u0026rdquo; (mend the pen after sheep are lost) was \u0026ldquo;mend the fold after sheep are lost\u0026rdquo;. Back-translation yielded \u0026ldquo;lock the stable door after the horse is stolen,\u0026rdquo; revealing a clear semantic deviation from the original English proverb. This reverse verification mechanism visually exposes distortions in cultural image transfer, strengthening students' awareness of metaphor migration sensitivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Risk Control Matrix: Systematic Mitigation of Challenges\u003c/h2\u003e \u003cp\u003eAddressing the three major risks identified, a graded response strategy matrix was constructed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAI Risk Response Matrix for Translation Teaching\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeverity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePractice Case\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultural Mistranslation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCultural Glossary DB\u0026thinsp;+\u0026thinsp;Dual Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdd entry \u0026ldquo;龙\u0026rarr;loong\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology Dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ldquo;AI-Free Days\u0026rdquo; + Metacognitive Training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeekly traditional translation session\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Integrity Crisis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProcess Tracing\u0026thinsp;+\u0026thinsp;Stylometric Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDetect AI features (excessive fluency/low personal style)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Educational Practice Recommendations: Towards a Human-AI Symbiotic Translation Education Ecosystem","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Restructuring the Curriculum System\u003c/h2\u003e \u003cp\u003eResponding to the deep penetration of intelligent technology, this study proposes a \u0026ldquo;Three-Three\u0026rdquo; modular curriculum restructuring:\u003c/p\u003e \u003cp\u003eFoundation Layer (30%): Focuses on AI-assisted technical skills training, emphasizing Prompt Engineering (e.g., optimizing collaboration via precise instructions like \u0026ldquo;Use domestication strategy for the following text, retain metaphors but use English idioms\u0026rdquo;). Practice shows this can improve BLEU scores by over 15% (Doherty, 2023).\u003c/p\u003e \u003cp\u003eCulture Layer (30%): Focuses on deep contrastive analysis of Chinese and Western thought patterns, systematically cultivating cultural transference decision-making competence by analyzing ethical connotation differences in culture-loaded terms (e.g., contrasting the value orientation conflict between Chinese \u0026ldquo;雪中送炭\u0026rdquo; [sending charcoal in snowy weather] and English \u0026ldquo;help a lame dog over a stile\u0026rdquo;) (Hu Kaibao, 2023).\u003c/p\u003e \u003cp\u003eCreation Layer (40%): Breaks through AI\u0026rsquo;s creative limitations. Students analyze multiple classic text translations (e.g., comparing Legge and Lau translations of Dao De Jing) to grasp linguistic essence, and create bilingual cultural annotation guides (Zhou Zhongliang, 2023).\u003c/p\u003e \u003cp\u003eSchool-based materials should feature task-driven content chains. A typical case requires students to use ChatGPT to translate The Analects phrase \u0026ldquo;君子和而不同\u0026rdquo; (The gentleman harmonizes without being uniform), systematically compare philosophical conveyance differences in Arthur Waley and Ku Hung-ming\u0026rsquo;s translations, and write a 200-word analysis justifying the optimal translation\u0026rsquo;s cultural fit (Wang Huashu, 2022). Such designs shift students from passive technology recipients to active constructors of cultural meaning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Strategic Transformation of Teacher Roles\u003c/h2\u003e \u003cp\u003eThe AI era demands teachers transition from knowledge transmitters to human-AI collaborative designers. This study proposes a three-tier AI Teaching Competency certification system:\u003c/p\u003e \u003cp\u003eL1 Tool Operator: Requires proficiency in prompt engineering and data interpretation (e.g., dynamically adjusting prompts to achieve 92% term consistency in political text translation) (Fan Daqi \u0026amp; Sun Lin, 2023).\u003c/p\u003e \u003cp\u003eL2 Curriculum Designer: Must develop human-AI collaborative lesson plans. A representative example is the course \u0026ldquo;AI-Assisted Translation of Government Work Reports,\u0026rdquo; integrating glossary building, cultural metaphor conversion, and ideological review modules (Jiao Jianli, 2023).\u003c/p\u003e \u003cp\u003eL3 Ethical Steward: Must guide students in identifying ideological risks of technology use (e.g., analyzing the political connotations of \u0026ldquo;common prosperity,\u0026rdquo; \u0026ldquo;shared prosperity,\u0026rdquo; and \u0026ldquo;collective affluence\u0026rdquo;) to foster a critical technological perspective (Li Zhengtao, 2023).\u003c/p\u003e \u003cp\u003eThe key path for competency renewal involves regular Human-AI Collaborative Teaching Workshops, operating on a \u0026ldquo;Teacher provides objectives \u0026amp; cultural insights \u0026rarr; ChatGPT generates base materials \u0026rarr; Jointly develop tiered tasks\u0026rdquo; model. For a \u0026ldquo;carbon neutrality\u0026rdquo; theme: primary tasks focus on precise term matching (e.g., \u0026ldquo;碳达峰\u0026rdquo; to \u0026ldquo;peak carbon emissions\u0026rdquo;); advanced tasks require cross-civilization metaphor conversion (e.g., reconstructing the agrarian imagery in \u0026ldquo;绿色转型\u0026rdquo; [green transition] for an industrial civilization context) (Williams \u0026amp; Hessen, 2023). This process drives teachers\u0026rsquo; evolution from technology users to educational value re-shaper.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusion: Dialectical Unity of Technological Empowerment and Humanistic Spirit","content":"\u003cp\u003eThis study systematically reveals the dual effects of ChatGPT in college English translation teaching through empirical research. The practice of the Tri-Dimensional Integration Model (Technology-Cognition-Ethics) and the PREP Closed-Loop Framework (Preview-Render-Evaluate-Ponder) confirms: ChatGPT\u0026rsquo;s instant feedback mechanism (response latency\u0026thinsp;\u0026lt;\u0026thinsp;1.2 seconds) and cognitive offloading function (working memory load reduced by 37%) increased translation accuracy by 27% (*p*\u0026lt;0.01), complex sentence processing efficiency by 41%, and learning motivation confidence by ES\u0026thinsp;=\u0026thinsp;0.91. These data highlight AI\u0026rsquo;s significant value in optimizing teaching processes and reducing cognitive load (Zhou Zhongliang, 2023).\u003c/p\u003e \u003cp\u003eHowever, technological empowerment consistently accompanies humanistic challenges. The exposed 38% cultural mistranslation rate (e.g., literal \u0026ldquo;hide brightness\u0026rdquo; for \u0026ldquo;韬光养晦\u0026rdquo; losing \u0026ldquo;strategic restraint\u0026rdquo; connotation), 27% of students showing technology dependence (inability to verify proverb translations independently without AI), and ideological deviations in political terms (e.g., mistranslating \u0026ldquo;共同富裕\u0026rdquo; as \u0026ldquo;common prosperity\u0026rdquo; instead of \u0026ldquo;shared prosperity\u0026rdquo;) jointly warn us to uphold the core principle of \u0026ldquo;Human-led, AI-empowered\u0026rdquo; (Fan Daqi \u0026amp; Sun Lin, 2023). While machines can convert words precisely, the irreplaceable value of human teachers lies in interpreting the \u0026ldquo;untranslatable\u0026rdquo; depths of culture\u0026mdash;in the semantic chasm between \u0026ldquo;龙\u0026rdquo; (loong) and \u0026ldquo;dragon\u0026rdquo;, in the ethical coding of \u0026ldquo;红白喜事\u0026rdquo; (red and white events)\u0026mdash;guarding the spiritual DNA behind language (Li Xiaoxiang, 2021).\u003c/p\u003e \u003cp\u003eLooking forward, translation education requires a triple deep transformation:\u003c/p\u003e \u003cp\u003e(1) Goal Restructuring: Shift from language conversion to cultural transference, cultivating students\u0026rsquo; ability to infuse AI translations with cultural annotations (e.g., parsing the dual semantics of \u0026ldquo;江湖\u0026rdquo; in martial arts and social contexts).\u003c/p\u003e \u003cp\u003e(2) Competence Evolution: Move beyond tool application, strengthening ethical risk insight through \u0026ldquo;Technology Critique Workshops\u0026rdquo; analyzing AI\u0026rsquo;s systemic biases (Williams \u0026amp; Hessen, 2023).\u003c/p\u003e \u003cp\u003e(3) Paradigm Innovation: Explore brain science-inspired instructional design, using fMRI technology to optimize the match between AI feedback and cognitive load (Thomas, 2022).\u003c/p\u003e \u003cp\u003eThe study also reveals three unresolved challenges: localization bottlenecks in low-resource language support (mistranslation rates exceeding 52% for \u0026ldquo;Belt and Road\u0026rdquo; languages), a 19% decline in metaphorical creativity (*p*=0.03) due to long-term AI use, and the hidden peril of educational equity exacerbated by the digital divide (benefits for underdeveloped regions only 63% of those in developed areas). These challenges demand we constantly calibrate technology\u0026rsquo;s course with the humanistic spirit\u0026mdash;when the light of code illuminates the classroom, educators must still guard the lamp named \u0026ldquo;Humanistic Spirit\u0026rdquo;, for true communication always occurs between souls, not servers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study involving human participants was reviewed and approved by the Academic Ethics Committee of Jilin International Studies University, and their participation has been officially approved by the university.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll human-related procedures were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study. Prior to the commencement of the research, all participants were fully informed about the study\u0026apos;s purpose, procedures, potential risks and benefits, confidentiality measures, and their right to withdraw at any time without penalty.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication was obtained from all individual participants included in the study. Participants consented to the publication of the research findings in an anonymized format, ensuring their personal data remain confidential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI am the sole principal investigator of this research, and the entirety of the work has been conducted by myself.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough this study is affiliated with several research projects, these projects have not received substantial financial support, though some funding may be provided after the project\u0026apos;s completion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProjects\u003c/strong\u003e: Ministry of Education Vocational College Education and Teaching Reform Project: \u0026ldquo;Research on Governance Policies of Online Language Use in University English Teaching under the Background of Artificial Intelligence\u0026rdquo; (2025JGYB045);2025 \u0026ldquo;14th Five-Year Plan\u0026rdquo; Special Project on \u0026ldquo;Reading and Teacher Development\u0026rdquo; by the Tao Xingzhi Research Association of China: \u0026ldquo;Application of Multimodal Generative AI in College English Creative Writing Teaching\u0026rdquo; (202513164JN);2024 Jilin Province Higher Education Research Project: \u0026ldquo;Exploration and Practice of ChatGPT in College English Translation Teaching\u0026rdquo;(JGJX24D1074).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBrown, T., et al. (2020). Language models are few-shot learners. \u003cem\u003eNeurIPS\u003c/em\u003e, 33, 1877\u0026ndash;1901.\u003c/li\u003e\n\u003cli\u003eDoherty, S. (2023). ChatGPT in translation education. \u003cem\u003eThe Interpreter and Translator Trainer\u003c/em\u003e, 17(2), 1\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eFan, Daqi, \u0026amp; Sun, Lin. (2023). Practical paths for political and ideological awareness of translators under the perspective of building a discourse system with Chinese characteristics for international communication. \u003cem\u003eJournal of Beijing International Studies University\u003c/em\u003e, 45(1), 80\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eFeng, Zhiwei, \u0026amp; Zhang, Dengke. (2023). GPT and language studies. \u003cem\u003eComputer-Assisted Foreign Language Education\u003c/em\u003e, (2), 3\u0026ndash;11+105.\u003c/li\u003e\n\u003cli\u003eGuo, Wenjie. (2024). Challenges, strategies, and implications of ChatGPT in English translation courses. \u003cem\u003eJournal of Guizhou Normal College\u003c/em\u003e, (3).\u003c/li\u003e\n\u003cli\u003eHew, K. F., Huang, W., Du, J., et al. (2023). Using chatbots to support student goal setting and social presence in fully online activities. \u003cem\u003eJournal of Computing in Higher Education\u003c/em\u003e, 35(1), 40\u0026ndash;68.\u003c/li\u003e\n\u003cli\u003eHu, Kaibao. (2023). ChatGPT and innovation in translation education. \u003cem\u003eShanghai Journal of Translators\u003c/em\u003e, (2), 1\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eHubbard, D. W. (2009). \u003cem\u003eThe failure of management: Why it\u0026rsquo;s broken and how to fix it\u003c/em\u003e. John Wiley \u0026amp; Sons, Inc., Hoboken.\u003c/li\u003e\n\u003cli\u003eIoannidis, C., Pym, D. J., \u0026amp; Williams, J. M. (2013). Sustainability in information stewardship: Time preferences, externalities, and social co-ordination. \u003cem\u003eThe Twelfth Workshop on the Economics of Information Security\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eJeon, J. (2021). Chatbot-assisted dynamic assessment (CA-DA) for L2 vocabulary learning and diagnosis. \u003cem\u003eComputer Assisted Language Learning\u003c/em\u003e, 36(3), 1\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eJiao, Jianli. (2023). Dialogic AI reshaping the educational ecosystem. \u003cem\u003eEducational Research\u003c/em\u003e, (6), 88\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eKenney, M. (2022). Deep learning for cognitive translation studies. \u003cem\u003eTranslation Spaces\u003c/em\u003e, 11(1), 1\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eKohnke, L., Moorhouse, B. L., \u0026amp; Zou, D. (2023). ChatGPT for language teaching and learning. \u003cem\u003eRELC Journal\u003c/em\u003e, 54(2), 1\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eLaviosa, S. (2021). \u003cem\u003eCorpora in translator education\u003c/em\u003e. Routledge.\u003c/li\u003e\n\u003cli\u003eLi, Xiaoxiang. (2021). \u003cem\u003eTheoretical and practical research on the \u0026ldquo;Three-Wide Education\u0026rdquo; of foreign language curriculum\u003c/em\u003e. Southeast University Press.\u003c/li\u003e\n\u003cli\u003eLi, Zhengtang. (2023). Facing ChatGPT: How can teachers survive in crisis? \u003cem\u003eResearch in Educational Development\u003c/em\u003e, 43(10), 1\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eLu, Yu, et al. (2023). Educational applications and prospects of generative AI: A case study of the ChatGPT system. \u003cem\u003eDistance Education in China\u003c/em\u003e, (4), 24\u0026ndash;31+51.\u003c/li\u003e\n\u003cli\u003eMijwil, M. M., Hiran, K. K., Doshi, R., et al. (2023). ChatGPT and the future of academic integrity. \u003cem\u003eAl-Salam Journal for Engineering and Technology\u003c/em\u003e, 2(2), 116\u0026ndash;127.\u003c/li\u003e\n\u003cli\u003eMoorkens, J. (2020). Translation quality assessment. \u003cem\u003eMachine Translation\u003c/em\u003e, 34(3), 1\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eNiu, Min. (2024). Innovation of practical training models in the \u0026ldquo;Business English Translation\u0026rdquo; course for vocational colleges. \u003cem\u003eVocational Education Forum\u003c/em\u003e, (2), 45\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Brien, S. (2021). Human-AI collaboration in translation. \u003cem\u003eFrontiers in AI\u003c/em\u003e, 4, 1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003ePym, A. (2023). ChatGPT and translation pedagogy. \u003cem\u003eJournal of Specialised Translation\u003c/em\u003e, 39, 15\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eThomas, C. A. (2022). Guiding the study of lived experience through an autoethnographic approach. In D. Clover, K. Sanford \u0026amp; W. S. Allen (Eds.), \u003cem\u003eAcademic project designs and methods: From professional development to critical and creative practice\u003c/em\u003e (pp. 231\u0026ndash;237). University of Victoria.\u003c/li\u003e\n\u003cli\u003eVieira, L. (2020). Post-editing in translation education. \u003cem\u003eTranslation and Interpreting Studies\u003c/em\u003e, 15(1), 1\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eVygotsky, L. (1978). \u003cem\u003eMind in society: The development of higher psychological processes\u003c/em\u003e. Harvard University Press, Cambridge, Massachusetts.\u003c/li\u003e\n\u003cli\u003eWang, Huashu. (2021). Research on translation education technology in the era of artificial intelligence: Issues and countermeasures. \u003cem\u003eChinese Translators Journal\u003c/em\u003e, (3), 84\u0026ndash;88.\u003c/li\u003e\n\u003cli\u003eWang, Tian\u0026rsquo;en. (2023). The characteristics, educational significance, and problem response of ChatGPT. \u003cem\u003eIdeological and Theoretical Education\u003c/em\u003e, (4), 19\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eWang, Youmei, et al. (2023). Ethical risks and avoidance approaches in the educational application of ChatGPT.\u003cem\u003e Open Education Research\u003c/em\u003e, 29(2), 26\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eWilliams, R., \u0026amp; Hessen, D. J. (2023). The impact of generative AI on higher-order thinking. \u003cem\u003enpj Science of Learning\u003c/em\u003e, 8(1), 1\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eWu, Y., et al. (2020). Duolingo\u0026rsquo;s AI-driven language learning. \u003cem\u003eAIED Proceedings\u003c/em\u003e, 112\u0026ndash;124.\u003c/li\u003e\n\u003cli\u003eXu, Huifu. (2023). The crisis of higher education in the era of weak artificial intelligence. \u003cem\u003eOpen Education Research\u003c/em\u003e, 31(4), 15\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eZhang, M. (2023). Cultural intelligence in AI translation. \u003cem\u003eCross-Cultural Communication\u003c/em\u003e, 19(1), 1\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eZhao, L. (2021). ChatGPT-assisted EFL teaching in China. \u003cem\u003eComputer Assisted Language Learning\u003c/em\u003e, 38(4), 1\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eZhou, Zhongliang. (2023). The application of ChatGPT in translation teaching: Changes, challenges, and responses. \u003cem\u003eForeign Language Teaching and Research\u003c/em\u003e, (5), 22\u0026ndash;30.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ChatGPT, translation pedagogy, tri-dimensional integration model, human-AI collaboration, cultural transference","lastPublishedDoi":"10.21203/rs.3.rs-8729921/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8729921/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study addresses three persistent challenges in college English translation instruction: the imbalance between student and teacher ratios, inefficiencies in analyzing complex sentence structures, and frequent cultural mistranslations. To tackle these issues, a tri-dimensional integration model\u0026mdash;comprising Technology, Cognition, and Ethics\u0026mdash;and a PREP teaching framework were developed. A mixed-method empirical study was conducted involving 60 non-English majors. Findings reveal that ChatGPT-assisted instruction significantly improves translation accuracy by 27% and complex sentence processing efficiency by 41%. However, it also introduces a 38% rate of cultural mistranslation, a 27% risk of overreliance on technology, and potential deviations in rendering political terms. To address these risks, the study advocates for multi-engine comparisons and a four-stage task chain to enhance learners\u0026rsquo; cultural decision-making competence. It also recommends building a dedicated cultural terminology database and implementing a dual-review mechanism for quality assurance. Furthermore, a \u0026ldquo;Three-Three Curriculum Model\u0026rdquo; (30% AI literacy, 30% cross-cultural analysis, 40% human-led revision) is proposed to support the transformation of teachers into operators, instructional designers, and ethical stewards. Ultimately, the study underscores a \u0026ldquo;Human-led, AI-empowered\u0026rdquo; principle: while machines can convert language on the surface, the true mission of education lies in interpreting the untranslatable depths of culture\u0026mdash;steering with technology, illuminating with humanity, and fostering a dialogue of the soul.\u003c/p\u003e","manuscriptTitle":"Effectiveness, Risks, and Pedagogical Reconstruction in College English Translation Teaching via a ChatGPT-Based Tri-Dimensional Integration Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 13:22:34","doi":"10.21203/rs.3.rs-8729921/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8d246c48-edaa-465e-bd4f-ec48409f1180","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T07:55:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 13:22:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8729921","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8729921","identity":"rs-8729921","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}