In the rapidly evolving landscape of higher education, the development and publication of specialized academic textbooks have transcended traditional editorial tasks, morphing into complex, engineering-grade projects. This transformation is particularly evident in the field of Artificial Intelligence (AI), where the convergence of cutting-edge technology, dynamic industry demands, and pedagogical innovation has created an unprecedented challenge for publishers. The process is no longer merely about compiling knowledge; it is a meticulously orchestrated endeavor that parallels the rigor and complexity of software project management. From exhaustive market and academic research to multi-tiered content design, author team assembly, iterative peer reviews, and the integration of digital resources, every phase demands precision, foresight, and a deep understanding of both the technological frontier and the educational ecosystem.
The impetus for this systemic overhaul in textbook publishing stems from a confluence of national strategic directives and global technological shifts. In China, the launch of the “New Engineering” initiative by the Ministry of Education in 2017 marked a pivotal moment. This initiative was designed to modernize engineering education, making it responsive to the needs of an economy increasingly driven by AI, cloud computing, and big data. It called for the creation of interdisciplinary, practice-oriented curricula that could bridge the gap between academia and industry. Simultaneously, the State Council’s “Next Generation Artificial Intelligence Development Plan,” issued in July 2017, elevated AI to a national strategic priority. This plan not only outlined technological goals but also emphasized the critical need for a robust talent pipeline and ethical frameworks to support a thriving AI ecosystem. The urgency was further amplified in 2020 when the Ministry of Education, alongside other key ministries, released guidelines specifically aimed at accelerating the cultivation of AI graduate students in “Double First-Class” universities, highlighting the nation’s shortfall in foundational AI theory and core technologies.
The global COVID-19 pandemic acted as a powerful catalyst, serving as a real-world “stress test” for intelligent technologies. The crisis demonstrated AI’s tangible value in areas like contact tracing, non-contact delivery services, smart healthcare, and automated manufacturing. It became clear that no industry could remain untouched; digital and intelligent transformation became an imperative, not an option. This rapid, forced adoption created a surge in demand for professionals skilled in AI, intensifying the pressure on educational institutions to deliver relevant, high-quality training. The publishing industry, as a key enabler of education, found itself at the forefront of this challenge, tasked with creating learning materials that could keep pace with the breakneck speed of technological change.
Recognizing the scale and complexity of this mission, People’s Posts and Telecommunications Press embarked on an ambitious, project-based initiative to create a comprehensive series of AI textbooks. This was not a solitary effort but a grand collaboration, orchestrated under the guidance of authoritative bodies like the Ministry of Education’s Teaching Steering Committees for Computer Science and Software Engineering. The project brought together a formidable alliance of industry giants—Huawei, Arm China, iFlytek, Tencent Cloud, and Anheng Information—and a consortium of top-tier academic institutions including Tsinghua University, Peking University, Beihang University, Beijing University of Posts and Telecommunications, Harbin Institute of Technology, and Nanjing University. The first, and arguably most critical, phase was a sweeping, systematic research effort. The team conducted in-depth analyses of over fifty domestic universities, numerous enterprises, and benchmarked against leading international research institutions and universities. This wasn’t superficial market research; it was a deep dive into the intricate interplay between industry’s evolving talent requirements and the current state of academic training.
The findings from this research phase were instrumental in defining the series’ overarching architecture. The project team adopted a “holistic planning, tiered design” model. This meant acknowledging that AI education is not monolithic. A one-size-fits-all textbook would be ineffective, even counterproductive. Instead, they mapped out a vertical spectrum of educational levels, from vocational colleges and applied undergraduate programs to engineering-focused bachelor’s degrees, elite “Double First-Class” universities, and finally, graduate studies. Each tier has distinct learning objectives, student preparedness, and faculty resources, necessitating tailored content. Furthermore, they recognized the horizontal dimension: AI is inherently interdisciplinary. A computer science student needs a different emphasis than a student from telecommunications, electronics, or even a non-technical field looking to apply AI. Therefore, the curriculum and corresponding textbooks were designed to be modular and adaptable, covering core domains like AI fundamentals, machine learning and deep learning, computer vision, natural language processing, AI applications, and robotics, but with varying depths and practical focuses depending on the target audience.
The second pillar of their strategy, “cross-border integration and differentiated design,” directly addresses the chaotic, rapidly evolving nature of AI as a field. Since 2019, when the first 35 universities were approved to offer dedicated AI undergraduate programs, the number has exploded. By 2020, nearly 200 new undergraduate and over 170 vocational programs had been established. This explosive growth, while indicative of high demand, also presented a significant challenge: how to maintain quality and relevance across such a diverse educational landscape? The answer lay in embracing, rather than fighting, the diversity. The textbook series was designed to be a flexible toolkit. For institutions with strong theoretical foundations and research ambitions, the materials would lean heavily into algorithms, mathematical theory, and system design. For vocational colleges or applied programs, the focus would shift decisively towards practical skills, hands-on projects, and immediate industry applicability. The content for a robotics program would emphasize embedded systems and control theory, while a telecommunications-focused AI course would integrate 5G, Narrowband IoT, and network security. This granular, context-sensitive approach ensures that the textbooks are not just informative but truly useful, meeting educators and students where they are.
The third, and perhaps most revolutionary, aspect of this publishing project is its approach to “innovative resource construction.” In the New Engineering paradigm, a textbook is no longer just a physical or digital book. It is the nucleus of a rich, interactive, multimedia learning ecosystem. Recognizing the limitations of traditional resources—often criticized for being static, poorly digitized, or hastily assembled—the project team committed to building a comprehensive suite of “online-offline integrated, pre-class and post-class covered” materials. This goes far beyond simple PowerPoint slides. The resource package is stratified to meet different needs. For basic teaching support, instructors and students gain instant access, via QR codes, to syllabi, presentation decks, case study packages, and code repositories. For enhanced, auxiliary learning, there are micro-lectures, MOOCs, and live-streamed sessions, often supplemented by dedicated online community groups for Q&A and discussion. For professional development and advanced training, the project offers customized, high-touch services like offline and online teacher training camps and specialized combat-ready online courses. This multi-layered, on-demand resource model transforms the textbook from a passive information source into an active, dynamic learning platform, empowering educators to teach more effectively and enabling students to learn in the way that suits them best.
The final, and foundational, principle underpinning the entire endeavor is an unwavering commitment to “quality as the core,” enforced through meticulous, engineering-style process control. In an era defined by information overload and fleeting trends, the value of a textbook lies in its accuracy, authority, and enduring relevance. To achieve this, the traditional publishing workflow was deconstructed and rebuilt with the precision of a software development lifecycle. The process was expanded into a detailed sequence of engineering-grade checkpoints: research and analysis, strategic planning, overall design, author engagement, blind peer review, content production, interim evaluations, final manuscript approval, resource development, pre-launch webinars, product release, faculty training, and continuous post-publication optimization. Crucially, quality control is not the sole responsibility of the publisher’s in-house editors. It is a collaborative effort that actively involves policy advisors, industry technology experts, award-winning university professors, research institute scholars, and even early adopter “beta” users. This creates a robust, multi-perspective quality assurance network.
One particularly innovative quality control mechanism is the “blind review and screening” process. After an initial screening, draft outlines and manuscripts are placed into a “review pool.” A panel of domain experts then conducts anonymous, online evaluations. The identities of both the authors and the reviewers are concealed, creating a shield against bias and fostering brutally honest, objective feedback. This system, refined through several publishing cycles, has proven to be a powerful tool for elevating content quality and ensuring that only the most rigorous, relevant, and well-structured materials reach the final publication stage. It is a testament to the project’s commitment to excellence, turning quality from an abstract goal into a measurable, process-driven outcome.
The journey of AI, from the hype surrounding events like the AlphaGo matches to its current, deeply embedded role in global society, has been tumultuous. It has weathered cycles of inflated expectations, technological bottlenecks, and serious ethical debates. Yet, the events of 2020, particularly the global pandemic, proved to be a defining moment. AI moved from the realm of theoretical promise to practical, life-saving application. It became “ubiquitous intelligence,” woven into the fabric of healthcare, logistics, data management, and economic recovery. This “ubiquity” is now reflected in the educational sphere, with more universities than ever racing to establish AI programs. This creates an immense responsibility for publishers. The textbooks they produce are not mere commercial products; they are the foundational tools shaping the next generation of innovators who will define the future of technology and society.
The team at People’s Posts and Telecommunications Press, led by strategists like Zhi Min Zhu, Xiao Yu Li, and academic expert Zhen Yu Wu, understood this responsibility. Their approach is a model of modern, user-centered, supply-side reform in educational publishing. They are not simply reacting to market demands; they are proactively shaping the educational landscape. Their guiding principle—“holistic planning, authoritative expert guidance, core team construction, enterprise support, and ecological resource co-construction”—is a blueprint for success in the digital age. By treating textbook publishing as a complex, iterative engineering project, they have created a dynamic, high-quality, and scalable system. This system doesn’t just publish books; it builds ecosystems that empower educators, engage students, and ultimately, fuel the engine of national innovation. It is a bold, necessary evolution that ensures educational publishing remains not just relevant, but indispensable, in the age of artificial intelligence.
By Zhi Min Zhu, Xiao Yu Li from People’s Posts and Telecommunications Press Co., Ltd., Beijing, and Zhen Yu Wu from Nanjing University, Jiangsu Nanjing. Published in China Media Technology, 2021(02):87-89. DOI: 10.19483/j.cnki.11-4653/n.2021.02.025