当 GPT 这样的生成式人工智能进入全球课堂，现代教育的地基正被悄然动摇：知识不再稀缺，作业可以在没有真正理解的情况下完成，传统评估难以把「学习」与单纯的「产出」区分开。在这个语境里，本书不追问「如何更高效地学」，而追问一个更根本的问题——学习究竟是如何发生的？
本书提出「学习发生学」，作为理解 GPT 时代学习的新基础框架。越过「学习即知识获取」的主流看法，作者把学习重新定义为一个在主体—互动—客体（SIO）结构之内的意义生成过程：学习不是信息或技能的累积，而是结构经由张力、互动、节律与反思的涌现。基于这套本体论，本书推进三重教育思想的挪移——从孤立的学习者到整合的 SIO 系统；从结果驱动的表现到由创造、自由、幸福统辖的意义动力学；从以传递为基础的讲授到「结构被主动生产」的生成式学习环境。
书中引入一个可操作的生成式课堂模型，展示 GPT 如何不作为学习的替代品，而作为一个催化剂——揭示、加速并追踪学习过程本身。本书不提供技巧或工具，而是为「智能不再是人类专属」的时代重构学习的哲学地基。它主张：教育的未来不在于与人工智能竞争，而在于夺回机器无法替代之物——意义、结构与自我的生成。作为「学习发生学」系列的奠基之作，本书为一切想把学习重新想象为「个体、机构乃至文明的生成性力量」的教育者、研究者与思想者，开出一条新路。
As generative artificial intelligence such as GPT enters classrooms worldwide, the foundations of modern education are being quietly destabilized. Knowledge is no longer scarce, assignments can be completed without genuine understanding, and traditional assessments struggle to distinguish learning from mere output. In this context, Introduction to Learning Generativics does not ask how to learn more efficiently-it asks a far more fundamental question: how does learning actually happen? This book proposes Learning Generativics as a new foundational framework for understanding learning in the GPT era.
Moving beyond the dominant view of learning as knowledge acquisition, the authors redefine learning as a process of meaning generation within a Subject-Interaction-Object (SIO) structure. Learning, in this view, is not the accumulation of information or skills, but the emergence of structure through tension, interaction, rhythm, and reflection. Building on this ontology, the book advances three core shifts in educational thought: from isolated learners to integrated SIO systems; from outcome-driven performance to meaning dynamics governed by creativity, freedom, and well-being; and from transmission-based instruction to generative learning environments where structure is actively produced. A practical generative classroom model is introduced, demonstrating how GPT can function not as a substitute for learning, but as a catalyst that reveals, accelerates, and traces the learning process itself.
Rather than offering techniques or tools, Introduction to Learning Generativics reconstructs the philosophical foundations of learning for an age in which intelligence is no longer exclusively human. It argues that the future of education lies not in competing with artificial intelligence, but in reclaiming what machines cannot replace: the generation of meaning, structure, and self. This book serves as the foundational volume of the Learning Generativics series, opening a new path for educators, researchers, and thinkers seeking to reimagine learning as a generative force for individuals, institutions, and civilization itself.