在生成式人工智能(GenAI)快速融入翻译能力培养的背景下,如何优化反馈机制以促进学习投入,已成为亟须解决的问题。本研究采用混合研究方法,系统考察反馈模式(GenAI单一反馈 vs. GenAI-教师协同反馈)与反馈复杂度如何影响学习者在机器翻译译后编辑(MTPE)学习中的情感、认知、行为投入及再修订质量。24名高年级本科生完成包含典型修辞手法的文学MTPE任务,数据来自其修订结果、问卷测量与半结构化访谈。结果显示:(1)协同反馈显著提升三维投入与修订质量;(2)学习者偏好简洁且情境契合的反馈,反馈复杂度需依任务与个体差异动态调整;(3)情感与认知投入的协同作用有助于推动有效修订。研究证实协同反馈在可信度、可理解性与可操作性上优于单一反馈,更能激发学习投入。这一结果为优化人工智能时代的MTPE教学反馈机制提供了实证支持,并为人机协同的智能化教育干预提供了参考。

The integration of GenAI into translation training is calling for the optimization of feedback mechanisms to enhance learner engagement. This study investigates the influence of feedback modality (GenAI-only feedback vs. GenAI-instructor collaborative feedback) and feedback complexity on learners’ emotional, cognitive, and behavioral engagement, as well as on the quality of subsequent revisions. Twenty-four senior undergraduate students completed a literary MTPE task incorporating typical rhetorical devices. Data were obtained from their revised outputs, questionnaire responses, and semi-structured interviews. Our findings reveal that: (1) collaborative feedback significantly enhanced three dimensions of engagement and revision quality; (2) learners favored concise and contextually relevant feedback, indicating that feedback complexity should be dynamically tailored to task characteristics and individual learner profiles; and (3) the interaction between emotional and cognitive engagement facilitated more effective revisions. Overall, collaborative feedback demonstrated superior credibility, comprehensibility, and actionability compared to GenAI-only feedback, thereby fostering learner engagement more effectively. This research offers empirical evidence for refining feedback mechanisms in MTPE instruction in the era of AI and provides practical insights for the design of intelligent, human—machine collaborative educational strategies.