The Development Path of Human Intelligence in Translation Education Under the Generative Learning Theory

doi:10.3969/j.issn.1674-8921.2025.06.014

Abstract

Abstract:

The profound advancement of artificial intelligence necessitates a redefinition of translation education. Against this backdrop of technological empowerment, how to solidify Human Intelligence (HI) has emerged as a central issue. Grounded in generative learning theory, this study constructs a theoretical framework for the development of human intelligence within translation education. It proposes systematically enhancing learners’ irreplaceability in key dimensions, such as cultural interpretation, creative translation, and ethical judgment, through three core mechanisms: cognitive activation, cultural schema generation, and metacognitive monitoring. Based on the educational philosophy of “human-led, machine-assisted”, this study recommends establishing a HI development assessment system and optimizing curriculum design for human-machine collaboration to promote a paradigm shift in translation education for the age of artificial intelligence. This study offers theoretical references and practical pathways for reshaping the translation education system with HI at its core.

Key words: translation education, artificial intelligence, human intelligence, Generative Learning Theory

CLC Number:

H315.9

WU Zhihui. The Development Path of Human Intelligence in Translation Education Under the Generative Learning Theory[J]. Contemporary Foreign Languages Studies, 2025, 25(6): 124-133.

Figures/Tables 5

References 21

[1]	Fiorella, L. & R. E. Mayer. 2016. Eight ways to promote generative learning[J]. Educational Psychology Review 28(4): 717-741. doi: 10.1007/s10648-015-9348-9 URL
[2]	Gardner, H. 1983. Frames of Mind: The Theory of Multiple Intelligences[M]. New York: Basic Books.
[3]	Hervais-Adelman, A., B. Moser-Mercer & N. Golestani. 2015. Brain functional plasticity associated with the emergence of expertise in extreme language control[J]. Neuro Image 114:264-274.
[4]	Jakobsen, A. L. 2003. Effects of think aloud on translation speed, revision, and segmentation[A]. In F.Alves(ed.). Triangulating Translation: Perspectives in Process Oriented Research[M]. Amsterdam: John Benjamins.69-95.
[5]	Lawson, A. P. & R. E. Mayer. 2021. Benefits of writing an explanation during pauses in multimedia lessons[J]. Educational Psychology Review 33(4):1-27. doi: 10.1007/s10648-021-09594-w pmid: 33776377
[6]	Lave, J. & E. Wenger. 1991. Situated Learning: Legitimate Peripheral Participation[M]. Cambridge: Cambridge University Press.
[7]	O’Brien, S. 2020. Translation technology and disaster management[A]. In M. O’Hagan(ed.). The Routledge Handbook of Translation and Technology[M]. London: Routledge. 304-319.
[8]	Risko, E. F. & S. J. Gilbert. 2016. Cognitive offloading[J]. Trends in Cognitive Sciences 20(9):676-688. doi: S1364-6613(16)30098-5 pmid: 27542527
[9]	Rowland, C. A. 2014. The effect of testing versus restudy on retention: A meta-analytic review of the testing effect[J]. Psychological Bulletin 140(6):1432-1463. doi: 10.1037/a0037559 pmid: 25150680
[10]	Pi, Z., Y. Zhang, W. Zhou, et al. 2021. Learning by explaining to oneself and a peer enhances learners’ theta and alphaoscillations while watching video lectures[J]. British Journal of Educational Technology 52(2):659-679. doi: 10.1111/bjet.v52.2 URL
[11]	Sternberg, R. J. 1985. Beyond IQ: A Triarchic Theory of Human Intelligence[M]. New York: Cambridge University Press.
[12]	Van Kesteren, M. T., M. Rijpkema, D. J. Ruiter, et al. 2012. Retrieval of associative information congruent with prior knowledge is related to increased medial prefrontal activity and connectivity[J]. Journal of Neuroscience 30(47):15888-15894. doi: 10.1523/JNEUROSCI.2674-10.2010 URL
[13]	Vygotsky, L. S. 1978. Mind in Society: The Development of Higher Psychological Processes[M]. Cambridge, MA: Harvard University Press.
[14]	Wittrock, M. C. 1974. Learning as a generative process[J]. Educational Psychologist 11(2):87-95. doi: 10.1080/00461527409529129 URL
[15]	Yu, Yuxiu. 2024. Application of translation technology based on AI in translation teaching[J/OL]. [2025-07-22]. Systems and Soft Computing (6). https://doi.org/10.1016/j.sasc.2024.200072.
[16]	胡加圣、戚亚娟. 2023. ChatGPT时代的中国外语教育:求变与应变[J]. 外语电化教学(1):3-6,105.
[17]	胡开宝、田绪军. 2020. 语言智能背景下的MTI人才培养:挑战、对策与前景[J]. 外语界(2):59-64.
[18]	姜华、王春秀、杨暑东. 2023. 生成式AI在教育领域的应用潜能、风险挑战及应对策略[J]. 现代教育管理(7):66-74. doi: 10.16697/j.1674-5485.2023.07.007
[19]	王华树、谢斐. 2024. 大语言模型技术驱动下翻译教育实践模式创新研究[J]. 中国翻译 45(2):70-78.
[20]	王雪、杨文亚、卢鑫, 等. 2021. 生成性学习策略促进VR环境下学习发生的机制研究[J]. 远程教育杂志 39(3):65-74.
[21]	祝智庭、戴岭、胡姣. 2023. 高意识生成式学习:AIGC技术赋能的学习范式创新[J]. 电化教育研究(6):5-14.

智能维度	人类智能表现	AI技术缺陷
文化认知	文化图式动态更新与重构	训练数据依赖
情景判断	实时评估并优化交际策略	固定算法驱动
伦理协同	在多元价值间进行调适	单模态信息处理

作用机制	对抗异化类型	人类智能(HI)表现	HI发展维度	生成式学习任务
认知激活	思维碎片化	主动调用先验知识,进行整体意义建构与推理。	情景判断	人机译文对比分析等(选择)
文化图式生成	文化钝感	解构源语文化框架,动态建构适应目标语境的新认知框架。	文化认知	文化注释、跨文化案例修订等(组织)
元认知监控	元认知萎缩	对翻译过程与策略进行实时监控、反思、评估与负责任决策。	伦理协同	翻译反思日志、有声思维等(整合)