The technical limitations of AI humanizer tool

The technical limitations of AI humanizer tool

The main technical limitations of AI humanizer tool are as follows:
Data dependency and timeliness limitations
AI tools, such as the volunteer filling system, rely on massive data for training, but there is a lag in key information such as university enrollment policies and major settings. For example, when some universities adjusted their admission rules in 2025, AI failed to update data in a timely manner, resulting in biased recommendation results.  ‌
Lack of innovation
Existing AI tools are mostly based on template generated content, making it difficult to propose original research questions. According to an experiment conducted in a certain scientific journal, only 12% of AI generated topics were rated by experts as having breakthrough potential, especially in scenarios that require personalized innovation needs (such as manufacturing process improvement) where performance is insufficient.  ‌
Format rigidity risk
Some universities require the submission of "AI detection reports" to avoid risks, and directly using AI generated text may trigger academic integrity systems. The Shanghai Jiao Tong University team only passed the proposal review through human-machine collaboration (AI completes 30% of the basic work), while directly submitting AI generated content faces homogenization issues.  ‌
Cross disciplinary collaboration barriers
In the manufacturing industry, different equipment data formats are incompatible (such as Siemens and Fanuc systems), requiring an additional data conversion layer, resulting in an 8-month extension of the project cycle. Similar issues also exist in multi departmental collaboration scenarios, affecting overall efficiency.  ‌

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