Read More《AI 面试、AI 老师、学习机，职教巨头们摸着石头过河的 AI 路》
Positive Reviews: Differentiated Exploration and Rational Cognition Drive Vocational Education AI into a Pragmatic and In – depth Development Stage
The integration of vocational education and AI demonstrates unique industry value through the differentiated layouts of leading institutions. The “dislocated competition” among the three giants, Huatu, Fenbi, and Zhonggong, is not simply a strategic evasion. Instead, it is an active attempt at error – correction and a deepening of understanding of the boundaries of technology application during the industry’s exploration period, which injects crucial practical value into the development of vocational education AI.
Firstly, the differentiated paths of leading institutions verify the diverse possibilities of AI technology in vocational education scenarios. Huatu chose to start from the structured interview for civil servants, precisely grasping the core of “a standardized scoring framework in non – standard scenarios.” Through the human – machine collaboration model of AI simulation practice + manual correction, it not only reduces the repetitive labor cost of teachers but also ensures the reliability of teaching effects. This strategy of “free at first, focusing on refinement” reflects the emphasis on user experience – by offering free trials to accumulate real feedback and then gradually optimizing the product, it avoids the blind commercialization of “using technology for the sake of technology.” Fenbi focuses on AI question – answering and accompanying learning. The fact that its AI question – brushing system class achieved sales of over 14 million yuan and had 40,000 paid students within two months of its launch directly verifies the market acceptance of “AI as a service tool.” By designing a structured learning process, it solves the pain point of users’ “passive reception,” enhances the service value through instant response, and ultimately stimulates the willingness to pay. Zhonggong’s AI learning machine goes beyond the short – term thinking of “exam training” and aims at “employment ability cultivation,” covering the entire college life cycle. The long – term learning path design from national situation awareness to career sprint meets the users’ deep – seated demand for “ability improvement” and opens up a new track of “hardware + content + service” for vocational education AI.
Secondly, the industry is forming a rational understanding of the value of AI. Cai Jinlong, the rotating CEO of Huatu, proposed that “the real value of AI lies in providing products with higher cost – performance and reducing operating costs.” This view breaks the misunderstanding that “AI must increase prices to increase revenue” and returns to the essence of education – technological progress should benefit more people. Chen Jianhua, the CTO of Fenbi, emphasized that “AI is not a selling point but a tool to make services more user – friendly.” Its AI teacher solves the pain point of users’ “timely response” through multi – modal interaction, which is a typical example of integrating technology into the service process. Zhonggong combines AI capabilities with long – term employment services through the hardware carrier of the learning machine, avoiding the formality of “using hardware for the sake of hardware.” These practices together send a signal: the core of vocational education AI is not to show off technology but to solve users’ real needs, improve teaching efficiency, and reduce service costs.
Finally, the “self – revolution” of leading institutions provides a model for industry transformation. When Fenbi launched the AI question – brushing system class, it clearly stated that “it would impact the existing business model,” but still chose to initiate the change actively. Huatu controls the cost of trial – and – error through internal “training” and the human – machine collaboration mechanism. Zhonggong shifts from “passing the exam” to “employment ability cultivation” and reconstructs the product logic. This attitude of “actively embracing the trend” is more valuable to the industry than short – term profits. Large institutions, with their teaching and research accumulation, user insights, and data resources, embed AI tools into mature business processes to achieve a leap in efficiency, providing a reference path for the integration of “technology + business” for small and medium – sized enterprises.
Negative Reviews: Disputes over the Boundaries of Technology Implementation and Hidden Concerns of Short – sighted Industry Behaviors
Although the exploration of vocational education AI is worthy of recognition, the challenges and disputes in its implementation process cannot be ignored. Multiple issues still need to be vigilant, ranging from the actual effects of technology application to the potential risks of the industry ecosystem.
Firstly, there are doubts about the actual needs and input – output ratio of some AI application scenarios. Take civil service exam question – answering as an example. The news mentioned that “the quality of civil service exam question analysis has been highly optimized, and the proportion of students who cannot understand the analysis is only 3‰,” which means that the target user group for AI question – answering is extremely small, and the marginal benefit is limited. At the same time, civil service exam question – answering belongs to a “low – frequency and high – sensitivity” scenario. One ineffective response may trigger strong negative emotions. If AI cannot guarantee the quality of response, it may damage the brand reputation. In addition, the standardization degree of civil service exam problem – solving steps is high (it can be completed in 3 – 5 steps), and there is no need for hierarchical teaching. The value of an AI teacher in this scenario is difficult to surpass that of a real – life teacher. These real – world conditions limit the application space of AI in civil service exam question – answering. If institutions blindly follow the trend and invest, they may face the risk of resource waste.
Secondly, the market acceptance and core value of hardware products need to be verified. Zhonggong launched an AI learning machine, aiming to cover the entire college life cycle with an “immersive learning environment.” However, Huatu’s doubts about hardware are quite representative – are users willing to pay for an “extra device”? If the hardware only serves as a “container” for content (such as exclusive courses being locked to the hardware), its value essentially depends on the content barrier rather than the irreplaceability of the hardware itself. Once the content barrier is broken (such as when competitors launch similar courses), the market competitiveness of the hardware will decline significantly. In addition, the current college student group generally uses tablets for learning. Zhonggong’s learning machine needs to prove its unique advantages compared with tablets (such as customized learning paths and integrated employment services), otherwise, it may fall into the formality of “using hardware for the sake of hardware.”
Thirdly, there are short – sighted behaviors such as “AI plagiarism” in the industry, which may damage the content ecosystem. The news mentioned that some enterprises use the “rewriting and summarizing” ability of AI to generate content variants in batches and reach users through an all – channel distribution matrix. This “plagiarism competition” is essentially information transfer rather than value creation. In the short term, small institutions may quickly acquire customers through “punching above their weight.” However, in the long term, it will lead to content homogenization in the industry, a lack of in – depth research, and even misinformation (such as the repeated spread of low – quality content may mislead users). If there is a lack of supervision, this phenomenon of “bad money driving out good” may hinder real technological innovation and value creation and damage the content foundation of the vocational education industry.
Fourthly, the integration of technology and the essence of education still needs to be deepened. Yang Wenfei of 51CTO pointed out that if an AI system can only identify the distribution of wrong questions and teachers cannot interpret the teaching problems behind the data, the system will become a “digital report.” This problem also exists in vocational education. Some institutions overly focus on technological functions (such as AI generating answers and automatic test paper compilation) but ignore the core of education – the participation of “people.” For example, Huatu emphasizes that “the core of human – machine collaboration is to solve the issue of responsibility attribution.” However, if the teaching team fails to understand the advantages of AI tools (such as freeing up time for personalized tutoring), the value of technology cannot be truly realized.
Suggestions for Entrepreneurs: Focus on Needs, Deeply Explore Scenarios, and Uphold Value
Facing the opportunities and challenges of vocational education AI, entrepreneurs can focus on the following aspects:
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Accurately Position Needs and Avoid “Pseudo – Scenario” Investment: The value of AI technology must be based on real needs. Entrepreneurs need to conduct in – depth research on the pain points of target users (such as whether they really need AI question – answering? Does the hardware solve the problems that tablets cannot meet?). Instead of blindly following popular trends, for example, in the civil service exam field, they can give priority to scenarios that are “non – standard but have a standardized framework” (such as interview simulation) or focus on service integration such as “long – term employment ability cultivation” that is difficult to cover with tablets to enhance the necessity of AI application.
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Design Products with a “Tool – Oriented” rather than a “Technology – Oriented” Mindset: AI is a tool to improve service efficiency, not a selling point. Entrepreneurs should focus on “how AI can make existing services more user – friendly.” For example, solve users’ “stuck problems” through instant response or guide users from “passive reception” to “active learning” through a structured learning process. The success of Fenbi’s AI question – brushing system class is a typical example of integrating AI into the learning process and enhancing the service experience, which is worthy of reference.
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Emphasize “Human – Machine Collaboration” rather than “Human – Machine Replacement”: The core of education is the responsibility and warmth of “people.” AI needs to collaborate with real – life teachers rather than replace them. Entrepreneurs can refer to Huatu’s “internal training” mechanism (testing AI tools in their own scenarios first) or design a quality control process of “AI generation + manual verification” to ensure the reliability of services and traceability of responsibilities. At the same time, they need to promote the “conceptual innovation” of the team, so that teachers understand the value of AI tools (such as freeing up time for personalized tutoring) rather than regarding them as a threat.
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Beware of the “Plagiarism Competition” and Uphold Content Value: Short – term traffic growth should not come at the expense of content quality. Entrepreneurs should focus on “information creation” rather than “information transfer.” For example, use AI to assist in in – depth research (such as analyzing the ability deficiencies behind users’ wrong questions), generate personalized learning paths, or combine multi – modal technologies (such as digital humans + 3D modeling) to improve the quality of courses. In the long run, content depth and service value are the core barriers of an enterprise.
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Balance “Light” and “Heavy” and Make Good Use of External Resources: 99% of education enterprises do not need to build their own large – scale models. They can quickly achieve functional iteration by calling third – party APIs (such as using mature NLP models to optimize question – answering) and concentrate resources on the “integration of technology and business.” For example, use AI to analyze users’ learning data to help teachers interpret teaching problems, rather than simply generating data reports. At the same time, large institutions can rely on their teaching and research accumulation, process advantages, while small institutions need to make up for their shortcomings with speed and flexibility and establish differentiated advantages in niche scenarios.
The development of vocational education AI is not a “short – term carnival” but a “marathon” that requires patience and in – depth exploration. Entrepreneurs need to take users’ needs as the core, technology as a tool, and content as the foundation, and find a balance between “fast” and “slow,” “light” and “heavy” to sail steadily in the AI wave.
