Choosing a university career is a decisive process for students' professional future in their educational path. This decision becomes difficult for the aspiring college student in an increasingly diversified and competitive world. The pressure to make academic decisions, coupled with a lack of experience and knowledge about the various career options, can create confusion and anxiety for applicants. In addition, social and family expectations often add to the stress, as students may feel compelled to choose careers that do not necessarily align with their interests and passions.

Therefore, this work aimed to develop a mobile application empowered with artificial intelligence (AI) that offers personalized vocational guidance. The system is structured in several stages, with the first stage focusing on collecting information on academic programs, scholarships, and financing in higher education institutions. This is achieved through an innovative strategy that involves the creation of algorithms based on the Web scraping technique. This strategy, which systematically and automatically extracts relevant data from web pages, empowers students by providing accurate and updated information, giving them more control over their career decisions.

The next step was to develop a survey to apply to the students, for which we made a test adaptation by Kuder, who created a test designed to explore the interests and tastes of people to identify vocational and professional preferences, yielding ten areas of preferences of personal and social nature. We used generative artificial intelligence to create a questionnaire adapted to each person based on Kuder's test with ten questions to finally provide a detailed analysis of the most suitable profession for each individual. Artificial intelligence algorithms and machine learning processes are essential in this vocational orientation process. AI analyzes labor market trends, available opportunities, the user's profile and preferences, and the data collected. This analysis uses a multidimensional approach considering the user's academic skills, personal skills, and expectations. The above is related to the data identified from academic programs through web scraping that we previously performed.

We tested the application first with students in their last year of high school and first semesters of university, and they obtained very satisfactory results in usability and the recommendations received.

Keywords: Artificial Intelligence, Career Choice, Recommendation system, mobile application.