人工智慧的應用範圍廣泛，科技的進步帶來方便和進步，人工智慧應用在實務上能夠協助人們在做一些重複性的工作時，能透過機器達到更高的效率，亦能夠避免人為的疏失，提升生產的效率。自動化的操作已經被應用於多個領域，人力資源部門在過去有許多繁雜的事務需處理，需要大量的人力和時間成本投入，而人才的重要性在一企業佔有舉足輕重的角色，因此倘若能夠降低人力資源部門所需花費的時間又能提高用人的準確度，才能精準的為企業帶來效益。
在本研究最初利用深度訪談收集各指導教授在收學生時會參考的標準做題目的設計，並且給於明確的參考順序和配分比重，客製化的系統建置目的在於能夠精確的反應出老師收學生的條件和門檻，以利此系統未來能實際的被應用於新入學的學生。因此，在此研究中收集了現有指導教授的學生分數，並參照學生們目前在實驗室內的工作滿意度及組織承諾，觀察這之間的關係，並利用統計分析看彼此之間的相關及預測效果，以期望此系統能被類推至未來進入台灣科技大學企業管理所的學生，輔助他們做決策。系統的操作在於能夠在學生和教授面談前，透過填答後自動產生的簡單結果以提供學生們做參考，降低學生在尋找指導教授時時間緊湊的問題，也期望能讓每位學生進到最適合的研究領域學習。
本研究在人才甄選的方式上與以往較為不同，且過去並無相關的研究輔助學生尋找指導教授，也較無研究在探討學生在各實驗室的工作滿意度和組織承諾。因此，期望透過此系統的初步發展，增進碩士生和指導教授的適配度，人崗的匹配才能讓碩士生享有最適合的資源，也讓指導教授在指導學生時較不會有無法指導的情況發生。藉此系統的輔助，不僅增加碩士生與指導教授的適配度，也能夠讓師生雙方在剛接觸時便能對彼此有初步的了解。

Artificial Intelligence has a wide range of applications. Advances in technology bring convenience and progress. Artificial intelligence applications can actually help people to achieve higher efficiency through machines when doing repetitive tasks, and can also avoid artificial negligence. Improve the efficiency of production. Automated operations have been applied in many fields. The human resources department had many complicated tasks to deal with in the past, requiting a lot of manpower and time cost investment. The importance of talents plays an important role in an enterprise. The time required by the human resources department can improve the accuracy of employment, so as to bring benefits to the enterprise accurately.
In this study, in-depth interviews were used to collect the standards that each advisor will refer to when enrolling students to design the questions, and give a clear reference sequence and weighting. The purpose of the customized system is to accurately reflect the conditions and thresholds for advisors to accept students, so that this system can be practically applied to new students in the future. Therefore, in this study, we collected the existed student scores of each advisor, and referenced the student’s current job satisfaction and organizational commitment in each laboratory. Observing the relationship between them and used statistical analysis to see the correlation and to predict the effect. In the hope that this system can be analogized to future students who enter the Business Administration department of National Taiwan University of Science and Technology to assist them in making decisions. The operation of the system is to provide students with reference through the simple results automatically generated after the answer before they have an interview with advisors. Between the students and advisors, it can reduce the problem of time-consuming when students are finding their guidance teacher. It is also expected that each student can reach the most suitable advisor for their research fields.
This study is different from the past in the way of talent selection. In the past, there was no relevant research to assist students to find advisors and there is less research to discuss students’ job satisfaction and organizational commitment in each laboratory. Therefore, it is expected that through the preliminary development of this system, the degree of adaptation between master students and advisors will be improved. The matching of human positions will enable master students to enjoy the most resources and also make the advisors less likely to be unable to guide the students. With the assist of the system, it not only increases the degree of adaption between master students and advisors, but also enables teachers and students to have a preliminary understanding of each other when they first contact.

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