高等教育数字化转型与生成式人工智能的深度应用，正推动人才培养质量的重心从技术供给侧向学生需求侧深刻转移。在此背景下，教育获得感作为衡量数字教学成效的核心主观指标，不仅是技术赋能价值的直接映射，更是驱动高阶学习行为与学业表现提升的关键心理势能。现有研究多聚焦于人工智能的工具效能与宏观治理逻辑，对学习者在人机协同中内隐的认知重塑及其向外显学业产出转化的机制探讨尚显不足。为突破技术决定论的单向视域，深度解析智能时代的教学反馈闭环，基于社会认知理论与人—环境匹配理论，构建了“环境供给—行为改变—认知重塑—结果输出”的整合性分析框架。该框架系统推演了人工智能使用行为对教育获得感的重塑路径，阐明了教育获得感在提升学习者自我效能感与深度学习投入中的驱动效应，并揭示了其在技术应用与客观学业表现之间的关键中介纽带地位。上述理论探讨与机制推演不仅重构了数字化时代教育获得感的内涵维度，更为应用型高校创新人机协同课程设计、实施精准化分类干预以及构建多元教育质量监测体系提供了科学的理论遵循与实践参考。

The digital transformation of higher education and the extensive application of generative artificial intelligence are driving a profound shift in the focus of talent development from the supply side of technology to the demand side of students. Against this backdrop, the sense of educational fulfilment—as a core subjective indicator for measuring the effectiveness of digital teaching—not only directly reflects the value of technological empowerment but also serves as a key psychological driver for enhancing higher-order learning behaviours and academic performance. Existing research has largely focused on the instrumental efficacy of AI and macro-level governance logic, with insufficient exploration of the mechanisms underlying learners’ implicit cognitive restructuring within human-machine collaboration and its translation into explicit academic outcomes. To transcend the unidirectional perspective of technological determinism and deeply analyse the teaching feedback loop in the intelligent era, this study constructs an integrated analytical framework of “environmental provision—behavioural change—cognitive restructuring—outcome output” based on social cognitive theory and the person-environment fit theory. This framework systematically deduces the pathways through which AI usage behaviour reshapes educational fulfilment, elucidates the driving effect of educational fulfilment in enhancing learners’ self-efficacy and engagement in deep learning, and reveals its pivotal mediating role between technology application and objective academic performance. The aforementioned theoretical exploration and mechanistic analysis not only reconstruct the conceptual dimensions of educational fulfilment in the digital age but also provide scientific theoretical guidance and practical references for applied universities to innovate human-machine collaborative course design, implement precise categorised interventions, and establish a diversified educational quality monitoring system.