The autumn recruitment period has kicked off, and 12.7 million college graduates have flooded into the job market. After many people submit their resumes, the first thing they wait for is not an interview invitation, but a series of online assessment links. The logic of the test, the personality scale, and the AI video interview are all linked together. A survey shows that more than 60% of large and medium-sized companies have included online assessments as part of their school recruitment process. Many fresh graduates have participated in dozens of assessments in a single season, spending dozens of hours on exams, but often come to nothing. Job hunting has not yet entered the core competency competition, and young people are first trapped in an endless war of attrition through assessment.
Faced with the frequent submission of tens of thousands of resumes, standardized assessment is a pragmatic choice for enterprises to improve the efficiency of preliminary screening. Industry cases show that AI-assisted preliminary screening can shorten the initial interview cycle of school recruitment by more than half, and significantly reduce the cost of single recruitment. In the school recruitment market with an extremely short recruitment window, the efficiency improvement effect of technical tools is obvious. There is nothing right or wrong in the tool itself, but when assessment changes from an auxiliary screening method to a rigid checkpoint with layers of added value, companies’ pursuit of efficiency will all fall on job seekers.
The inherent limitations of standardized assessments are destined to be unable to carry an overly heavy selection function. Most general assessments on the market follow a set of question banks that cover the entire industry. Intellectual questions and personality scales are highly universal but extremely weak in specificity. Research in the field of human resources has long confirmed that general cognitive assessment has limited predictive validity for specific job performance. When the selection criteria become increasingly distant from the actual job requirements, the evaluation becomes a formalistic “threshold game.” It may seem like a rigorous process, but in fact it has long deviated from the core original intention of matching people with jobs.
The solidification of the question bank has spawned a question-writing industry chain. Answer templates and keyword strategies can be seen everywhere on social platforms. Job seekers no longer polish their real abilities, but instead figure out algorithm preferences and cater to machine scoring. The judging logic of AI interviews is more like an opaque black box. The system only scores based on facial expressions, voice intonation, and keyword frequency, lacking the questioning and flexible judgment of a real interviewer. According to the Personal Information Protection Law, when automated decision-making involves important personal rights and interests, the parties concerned have the right to know the explanation and reject the pure algorithm decision. However, in reality, "the system failed" is often the only answer. The reasons for failure are unknown, the channels for objections are not smooth, and the fairness of the process is greatly compromised.
Assessment results are not interchangeable, which is another crux of the internal friction in job hunting. Different companies have different thresholds, and different positions in the same group must be retested. The questions are similar, but the time cost is doubled. The energy that fresh graduates should spend on polishing their professional abilities and preparing for in-depth interviews is instead wasted on answering repeated questions. The company saves human resources for internal screening, but makes the entire job seeker group spend extra time. This is essentially a disorderly transfer of recruitment costs. Sichuan and Chongqing and other places have explored cross-regional mutual recognition of public professional quality assessments. If the industry can promote the sharing of assessment results, it will not only reduce duplication of work, but also force service providers to improve content quality.
Technical tools can only ever play a supporting role, and the final judge of recruitment should always be people. Enterprises should get rid of the misunderstanding that the more links there are, the more rigorous they are. They should proactively reduce the recruitment process, set up inspection projects based on the core competencies of the job, cut off irrelevant homogeneous assessments, and prepare for real-person interviews. At the same time, they should implement compliance requirements, clearly inform the assessment rules, assessment dimensions and result weights, and smooth channels for feedback and objections. At the industry level, we can explore the establishment of unified assessment standards and a mutual recognition mechanism for results to reduce repeated assessments from the root cause. Regulatory authorities also need to fill in the gaps in a timely manner, clarify the application boundaries of recruitment algorithms, standardize the collection and use of personal information, and protect the bottom line of employment fairness.
Recruitment is a two-way choice and should not become a one-way consumption level. Leave efficiency to the tools, return fairness to the process, and put respect throughout the entire process of two-way selection. Only then can recruitment return to the original intention of matching people with jobs, and only young people can find their own career path in a more friendly employment environment. (Fang Yuanlu)
