Startup Commentary”Is AI for College Entrance Exam Voluntary Filling a Reliable “Alternative” to Zhang Xuefeng?”

Read More《高考AI填志愿“平替”张雪峰，靠谱吗?》

Positive Reviews: AI-assisted College Application – An Efficiency Revolution and Information Equalization under Technological Inclusiveness

During the college entrance examination season in 2025, the explosive popularization of AI-assisted college application tools has become a typical example of technological empowerment in the education sector. From Baidu’s data showing that “over 10 million users used the AI college application assistant on June 25th” to the second – level recommendation services launched by Internet platforms such as Quark, Baidu, and Douyin, and the free access provided by the Ministry of Education’s “Sunshine College Entrance Examination” platform, AI is reshaping the traditional logic of college application with its high – efficiency and inclusive nature.

First of all, AI-assisted college application solves the pain point of “information overload” under the new college entrance examination reform. The new college entrance examination’s “3+3” and “3+2+1” subject selection models have pushed the complexity of college application to an “epic level”. For example, candidates in Liaoning need to fill in 112 college applications, those in Guizhou need to fill in 96, and most provinces across the country require more than 80. The two thick volumes of the “Guide to College Application” and the “Catalog of Majors” with thousands of pages have left candidates and their parents lost in an “information maze”. AI tools can generate multi – dimensional “aggressive, stable, and conservative” recommendation plans in seconds by inputting key information such as scores, rankings, and interest preferences, covering core data such as colleges, majors, and admission probabilities, which greatly reduces the cost of information screening. Take Quark as an example. After users input information such as a Shanghai candidate’s score of 548 and subject selections of physics/history/chemistry, the system can generate recommendation reports for “college – first” (nearly 600 colleges) and “major – first” (over 4,000 options) respectively. This “structured + personalized” output shortens the original manual screening process that took days or even weeks to just a few minutes.

Secondly, AI-assisted college application realizes “technological equalization” and provides the possibility of fair choices for groups with scarce resources. As reported in the news, candidates from mountainous areas and those who are “the first college students in their families” often have no idea when filling in college applications due to their parents’ lack of experience and cognitive limitations. Traditional high – priced institutions (such as Zhang Xuefeng’s service priced at 12,000 – 18,000 yuan and New Oriental’s face – to – face consultation starting at 6,590 yuan) have set a high service threshold that ordinary families can hardly afford. In contrast, the prices of AI tools (ranging from 98 yuan to 980 yuan) or even free access (such as the Ministry of Education’s platform) have opened an “information window” for these groups. For instance, the Ministry of Education’s “Sunshine College Entrance Examination” platform integrates resources such as enrollment data, employment information, and psychological evaluations, and provides services such as major details and employment prospect inquiries, enabling candidates from remote areas to access the same – quality information support as urban candidates. This “technological inclusiveness” essentially uses digital means to narrow the gap in the distribution of educational resources, so that “choices” are no longer imbalanced due to economic conditions or regional differences.

Finally, AI-assisted college application promotes industry standardization and forces traditional institutions to improve service quality. In the past, the college application industry was in a state of chaos. Some institutions relied on “newcomers trained on – the – job” and “sales pitches”, and users were skeptical about “whether the money spent was worth it”. The popularization of AI tools has reshaped users’ perception of “professionalism” with a transparent and data – driven service logic. Users can verify the results through comparison among multiple tools, and institutions need to prove their value with more professional experience (rather than information asymmetry). For example, although leading institutions such as Zhang Xuefeng still promote their “one – on – one service with real people”, they also need to improve the scientific nature of their plans with the assistance of AI. Institutions like New Oriental strengthen their personalized services by increasing the number of face – to – face consultations (from 1 to 4 – 5 times). This trend of “AI + human” integration essentially reflects the market’s higher requirements for “professionalism”, and the ultimate beneficiaries are candidates and their parents.

Negative Reviews: Data Dependency and Lack of Humanity – The Realistic Limitations and Concerns of AI-assisted College Application

Although AI-assisted college application has significant advantages, its limitations cannot be ignored. From the technical logic to human needs, AI tools have also brought new risks while solving problems.

Firstly, the timeliness of data and differences in models lead to result deviations, and over – reliance may mislead decision – making. As reported in the news, tests found that “under the same score and the same needs, different AI systems predicted the admission rate of the same college from 29% to 99%, and the number of recommended combinations ranged from two or three hundred to thousands”. The root causes of these differences lie in data sources (some tools rely on non – official data), model algorithms (different training logics), and the influence of user preferences (the system adjusts the results according to historical usage habits). For example, if a tool only integrates the admission data of the past three years, and a college has increased its enrollment plan or adjusted the subject selection restrictions this year, the AI may give an incorrect probability due to the unupdated data. If users frequently click on “first – tier cities” in the “regional preference” option, the system may over – recommend colleges in this region and ignore other potential high – quality options. This “data black box” problem makes the AI results more like a “probability game” rather than “precise decision – making”. If candidates rely entirely on a single tool, they may fall into the misunderstanding of “technological superstition”.

Secondly, AI cannot capture “hidden information” and is difficult to replace the “human touch” of human services. Filling in college applications is not only about data matching but also a deep understanding of “people”. Candidates’ interests, career plans, family resources, and even the “employment binding” between colleges and specific industries (such as the targeted cooperation between some non – 985 colleges and state – owned enterprises or public institutions) all belong to the “hidden rules” that cannot be covered by public data. For example, although a certain ordinary college is not a well – known institution, its computer major has long – term cooperation with local Internet companies, and the employment rate of its graduates is as high as 95%. This kind of information can only be obtained through alumni interviews and industry research, and it is difficult for AI tools to present it directly through data. In addition, the “emotional value” of human services is irreplaceable. The “one – on – one communication” provided by institutions such as Zhang Xuefeng can adjust the recommendation logic by observing the candidates’ personalities and family backgrounds (for example, avoiding majors that “require strong social skills” for introverted candidates). However, the “interest preference” options (8 fixed dimensions) of AI tools essentially simplify the complex “human nature” into labels, which may ignore the candidates’ deep – seated needs (such as “liking chemistry but hating experiments”).

Thirdly, the lack of industry supervision may exacerbate the “abuse of tools” and damage users’ rights and interests. Currently, although the market for AI-assisted college application is bustling, there is a lack of unified technical standards and service specifications. Some tools exaggerate the “admission probability” to attract users. Some institutions use “AI + experts” as a gimmick, but actually generate template plans with AI, and the experts only make formal modifications. Even worse, some take advantage of candidates’ anxiety and bundle – sell high – priced “guaranteed admission services” (which actually do not guarantee admission). The phenomenon of “a chaotic industry with newcomers trained on – the – job” mentioned in the news, if combined with the “technological endorsement” of AI tools, may form a “pseudo – professional” service. What users pay for may be a “template piled up with data” rather than a truly personalized plan. This regulatory vacuum not only affects the user experience but may also lead to the transformation of “technological empowerment” into “technological exploitation”.

Suggestions for Entrepreneurs: Build a Professional Service Ecosystem of “AI + Human” Centered on User Needs

Facing the opportunities and challenges in the market of AI-assisted college application, entrepreneurs need to grasp the following key points:

1. Strengthen data quality and build a “dynamic + authoritative” database. Data is the core competitiveness of AI tools. Entrepreneurs need to integrate authoritative data sources such as the Ministry of Education’s “Sunshine College Entrance Examination” platform, college official websites, and employment reports, and establish a real – time update mechanism (such as tracking the adjustment of college enrollment policies and changes in the employment market). In addition, a “user feedback – data correction” closed – loop can be introduced. If users find recommendation deviations after using the tool (such as the actual admission score of a college not matching the prediction), the system needs to quickly verify and correct the model to improve the credibility of the results.

2. Clarify the tool’s positioning and conduct user education. AI-assisted college application is essentially an “auxiliary tool” rather than a “decision – making substitute”. Entrepreneurs need to help users establish a rational understanding through product design (such as prominently marking “data for reference only”) and content popularization (such as releasing a guide on “how to use AI-assisted college application dialectically”). For example, a “multi – tool comparison” function can be added to the tool to guide users to cross – verify the results with 2 – 3 tools. Or a “15 – minute consultation package of AI + human” can be launched, where professional consultants interpret the AI reports to reduce users’ decision – making risks.

3. Explore the integration model of “AI + human” to make up for the lack of humanity. AI solves efficiency and information asymmetry problems, while human services address in – depth and emotional needs. Entrepreneurs can create a hierarchical service of “light – weight AI + professional consultants”. The basic version of the AI tool is free or low – priced (covering 90% of general needs). The advanced version provides “AI report + one – on – one consultant interpretation” (priced at 500 – 1000 yuan), where consultants with educational consulting qualifications and more than three years of experience optimize the plan based on candidates’ personalities, family resources, and other hidden information. The high – end version provides customized services of “AI data + on – the – spot research + alumni interviews” for high – net – worth users (such as verifying the employment binding between a college and an industry). This hierarchical model can not only meet the needs of different users but also improve the professionalism of services.

4. Pay attention to the sinking market and promote technological inclusiveness. Candidates from mountainous areas and “the first college students in their families” are the key targets of technological inclusiveness. Entrepreneurs can cooperate with education departments and public welfare organizations to promote free AI-assisted college application tools in schools in underdeveloped areas, or reduce the usage threshold through “public welfare subsidies” (such as allowing poor students to use the advanced version of the service for free with a certificate). At the same time, functions such as “dialect version” and “simplified operation interface” can be developed to lower the technological usage threshold and truly ensure that “no candidate loses in the college application process due to information asymmetry”.

5. Promote industry standardization and participate in the co – construction of supervision. Entrepreneurs need to actively cooperate with colleges, education institutions, and industry associations to promote the formulation of technical standards (such as data source specifications and model transparency requirements) and service specifications (such as banning false “guaranteed admission” promotions and clarifying the qualification requirements for consultants) for AI-assisted college application. For example, they can initiate a “College Entrance Examination Volunteer Service Alliance” and release a “White Paper on the AI-assisted College Application Industry” to purify the market environment through self – discipline and external supervision and avoid the “bad money driving out the good”.

Filling in college applications is essentially a “human choice” – a way to meet the future self. The value of AI tools lies in using technology to reduce the “information cost” of choices rather than replacing “human thinking”. Entrepreneurs need to keep in mind that technology is a means, and user needs are the core; efficiency is the foundation, and professionalism and humanity are the long – term competitiveness. Only in this way can AI-assisted college application truly become a “helper” for candidates rather than a “decision – maker”.