As Li (2025) points out, “the development of AI technology brings multiple challenges and impacts to teaching philosophies, content, resources, and models.” [5] GenAI fundamentally challenges every dimension of the TPACK framework, imposing new demands for our time.

GenAI has catalyzed profound changes within the core knowledge elements of TPACK. Firstly, it has enriched the substance of Technological Knowledge (TK). Whereas traditional TK focused on hardware and software operational skills, in the GenAI era, its core has shifted to the cognitive and practical abilities required for human-AI collaboration. Teachers must not only know how to use these tools but also understand their generative logic, master effective Prompt Engineering techniques, and possess the capacity for critical evaluation of AI-generated content. Secondly, Content Knowledge (CK) has become increasingly dynamic. The CK of vocational education is intimately tied to industry practice, and GenAI can capture and generate the latest industry trends and technological applications in real-time. This renders static, fixed textbook knowledge rapidly obsolete. A teacher’s CK is no longer measured by the volume of knowledge possessed, but by the ability to dynamically acquire, verify, integrate, and transmit fluid, cutting-edge disciplinary knowledge using AI. Finally, Pedagogical Knowledge (PK) is witnessing the emergence of new paradigms with the aid of GenAI. The powerful capacity of GenAI for personalized support is driving a shift from “standardized instruction” to “customized education.” This demands that teachers’ PK evolves from traditional methods like lecturing and demonstration to more complex, modern paradigms such as designing and guiding personalized learning, data-driven instruction, project-based inquiry, and virtual simulation training.

GenAI impacts not only the individual knowledge elements of TPACK but also profoundly challenges their integration. Firstly, it presents a challenge to Technological Pedagogical Knowledge (TPK): How to design effective human-AI collaborative learning activities that leverage the advantages of AI while preventing student dependency and fostering higher-order thinking has become a central dilemma for TPK. Secondly, it challenges Technological Content Knowledge (TCK): How to utilize AI to transform abstract industrial principles and complex processes into vivid, intuitive, and interactive instructional content, while ensuring its accuracy and currency, poses a direct test to the TCK of vocational teachers. Finally, it challenges Technological Pedagogical Content Knowledge (TPACK): The highest-level challenge lies in seamlessly integrating GenAI, advanced pedagogies, and dynamic subject matter content within authentic industry-integrated projects or practical training scenarios to create novel, high-value learning experiences. This requires teachers to possess an exceptional degree of integrated design and innovative capabilities.

The traditional TPACK framework addresses ethical issues in a relatively implicit manner. However, GenAI introduces a series of acute ethical risks, including data privacy, algorithmic bias, ideological influence [2] , academic integrity, and intellectual property rights. As Liu et al. (2025) state, “the powerful capabilities emerging from large models are commensurate with the ethical challenges they bring.” [2] It is therefore imperative to supplement the TPACK framework with Techno-Ethical Assessment Knowledge (TEAK) for the GenAI era. Rather than being a standalone eighth component, TEAK should be conceptualized as an essential ethical layer that permeates and informs every other domain within the framework. It represents the wisdom to critically evaluate and responsibly apply GenAI, ensuring that its use aligns with pedagogical goals and societal values. GenAI-TEAK requires teachers to possess the ability to make ethical judgments and risk assessments when using AI technologies, ensuring that their application aligns with educational ethics and pedagogical goals, thereby realizing “AI for good.”

This stage aims to eliminate teachers’ cognitive barriers regarding technology and ethics.

This stage is designed to foster the deep integration of technology with subject matter and pedagogy.

For instance, an automotive engineering instructor might use a large language model to analyze and summarize the latest research on EV battery technology to update their curriculum, while a hospitality management teacher could use AI to analyze real-time guest review data from global hotel chains to identify emerging service standards and incorporate them into skills training.

This is particularly critical in vocational education, where learning must mirror authentic workplace challenges. For example, use AI to generate complex, interdisciplinary, real-world problems to drive projects; create high-fidelity virtual training environments for students to practice complex technical procedures safely and repetitively; and leverage AI to analyze student learning process data in real-time, providing personalized feedback and guidance. **To make this actionable, a teacher could use the following prompt sequence to generate a PBL scenario: “Act as a senior project manager in a logistics company. Create a detailed project brief for a team of student trainees. The task is to design a more efficient last-mile delivery solution for a dense urban area, considering cost, environmental impact, and customer satisfaction. The brief must include realistic constraints, key performance indicators (KPIs), and potential challenges they will face.”.

This stage aims to achieve a holistic leap in TPACK competency.

A guiding question for a community in a culinary arts program might be: “How can we use AI-powered recipe generation and analysis tools to teach students about flavor profiling and menu costing, while still ensuring they master foundational cooking techniques?”

As General Secretary Xi Jinping has emphasized, “it is necessary to integrate multidisciplinary forces, strengthen research on AI-related legal, ethical, and social issues, and establish and improve the legal regulations, institutional systems, and ethical morals that ensure the healthy development of artificial intelligence” [7] . Given the ethical issues of GenAI, relevant departments should expedite the formulation of ethical norms and guiding principles for the application of AI in education, drawing clear “red lines” for its use. Concurrently, professional development standards and criteria for faculty evaluation and promotion should be optimized to include GenAI-TPACK competency as a key indicator of assessment and motivation.

Institutions must provide organizational support, such as establishing specialized bodies like a “Center for Intelligent Teaching Development” to coordinate training, research, and technical support. They must also provide necessary resources, including AI software licenses, computing power, localized technological adaptation [6] , and high-quality databases, to lower the barriers for teachers’ application of these tools.

An innovative culture is the fertile ground for growth. Institutions should actively foster an academic atmosphere that encourages exploration and tolerates failure, recognizing teachers’ efforts in AI-driven teaching innovation. At the same time, it is essential to advocate for a new knowledge perspective that is open, shared, and autonomous, promoting a new teaching culture of human-AI collaboration and teacher-student co-creation. Embracing the philosophy that “the wisdom of the smart classroom originates from the intelligent synergy achieved by the teacher based on a humanistic standpoint, educational ethics, and teaching experience” [1] , will enable teachers to truly embrace the changes of the intelligent era psychologically.

From the perspective of Generative AI, the professional development of vocational college teachers is undergoing a profound paradigm shift. GenAI is not only a challenge to the existing TPACK competencies of teachers but also a catalyst for their reshaping and enhancement. This study posits that effectively addressing this challenge cannot rely on sporadic, ad-hoc training, but requires the construction of a systematic strategy for competency development. This strategic framework, structured in a progressive hierarchy of “foundational-integrative-comprehensive,” synergistically advances teachers’ competencies across the multiple dimensions of technology, subject matter, pedagogy, and ethics. It is supported by guarantees at the institutional, organizational, and cultural levels. The ultimate goal is to facilitate the fundamental role transition of vocational teachers from “transmitters of knowledge” to “designers and facilitators of human-AI collaborative, intelligent learning.”

The strategic framework constructed in this study is primarily based on literature review and theoretical speculation and lacks the support of empirical data. Its universality and effectiveness await validation through large-scale empirical research in the future. Furthermore, vocational colleges encompass a wide variety of disciplines, and the needs and strategic priorities for TPACK competency development may differ significantly between fields (e.g., engineering versus liberal arts). This study has not undertaken an in-depth, differentiated exploration of these areas.

Future research could be extended in the following directions: First, developing a TPACK competency assessment scale specifically for vocational college teachers would provide a scientific tool for the precise diagnosis of their abilities and the effective evaluation of development strategies. Second, quasi-experimental studies could be conducted to empirically test the proposed strategic framework and examine its actual impact on teacher competency improvement. Finally, as the “teacher-student-machine” tripartite interactive model deepens, future research should intensify its focus on new pedagogical relationships, cognitive learning processes, and educational evaluation systems, with the aim of constructing a more complete educational theory for the intelligent age.