Artificial intelligence (AI) is reshaping higher education, yet current debates often feel tangled, mixing concerns about pedagogy, operations, curriculum, and the future of work without a shared framework. This paper offers a first attempt at a taxonomy to organize the diverse narratives of AI education and to inform discipline-based curricular discussions. We place these narratives within the enduring responsibility of higher education: the mission of knowledge. This mission includes not only the preservation and advancement of disciplinary expertise, but also the cultivation of skills and wisdom, i.e., forms of meta-knowledge that encompass judgment, ethics, and social responsibility. For the purpose of this paper's discussion, AI is defined as adaptive, data-driven systems that automate analysis, modeling, and decision-making, highlighting its dual role as enabler and disruptor across disciplines. We argue that the most consequential challenges lie at the level of curriculum and disciplinary purpose, where AI accelerates inquiry but also unsettles expertise and identity. We show how disciplines evolve through the interplay of research, curriculum, pedagogy, and faculty expertise, and why curricular reform is the central lever for meaningful change. Pedagogical innovation offers a strategic and accessible entry point, providing actionable steps that help faculty and students build the expertise needed to engage in deeper curricular rethinking and disciplinary renewal. Within this framing, we suggest that meaningful reform can move forward through structured faculty journeys: from AI literacy to pedagogy, curriculum design, and research integration. The key is to align these journeys with the mission of knowledge, turning the disruptive pressures of AI into opportunities for disciplines to sustain expertise, advance inquiry, and serve society.

翻译：人工智能（AI）正在重塑高等教育，然而当前的讨论常常陷入混乱，将教学法、运营管理、课程设置及未来工作等议题混杂在一起，缺乏统一的框架。本文首次尝试提出一种分类体系，以梳理人工智能教育的多元叙事，并为基于学科的课程讨论提供参考。我们将这些叙事置于高等教育持久的责任之中：即知识使命。这一使命不仅包括学科专业知识的保存与发展，还涵盖技能与智慧的培养——即包含判断力、伦理与社会责任在内的元知识形态。为便于本文讨论，人工智能被定义为能够自动化分析、建模与决策的自适应数据驱动系统，强调其作为跨学科赋能者与颠覆者的双重角色。我们认为最具深远影响的挑战存在于课程与学科目标的层面，人工智能在此既加速了学术探索，也动摇了专业知识体系与学科认同。我们展示了学科如何通过研究、课程、教学法与教师专业能力的相互作用而演进，并阐释了课程改革为何是实现实质性变革的核心杠杆。教学创新提供了一个战略性且可操作的切入点，通过具体可行的步骤帮助师生构建必要的能力，从而参与更深层次的课程重构与学科革新。在此框架下，我们提出实质性改革可通过结构化的教师发展路径推进：从人工智能素养到教学实践、课程设计乃至研究融合。关键在于使这些发展路径与知识使命相契合，将人工智能的颠覆性压力转化为学科维持专业优势、推进学术探索并服务社会的机遇。