The analysis of data and user behavior

Data intelligence, analyzing different patterns of behavior, will allow us to see how the current educational system guides various stakeholders (students, teachers, primary and secondary schools, vocational training centers, middle and higher degrees, universities, business schools, companies, and workers) to achieve partial objectives that, from my point of view, are not aligned with the main objective we should expect from the education sector: "Helping students develop competencies and ethical values that help ensure their physical, emotional well-being, and employability in the future."

Over the years, this "data intelligence" may help us understand why some or many centers that apparently do not have school failure, or those in which a high percentage of their students pass the university entrance exams, suffer high dropout rates when they enter university. Or even without experiencing any or very little dropout rate, the employability of the "excellent students qualified by the system" lacks employability, or even those who have high employability feel frustrated because they are not happy."

In other words, we may encounter educational institutions with a high reputation for meeting certain standards (low rates of school failure, high rates of success in university entrance exams, low dropout rates) whose students are not happy and are frustrated for having chosen a path that does not align with their interests and personal competencies. Additionally, they may face many difficulties entering the job market.

The importance of AI in academic and professional guidance

With this significant contribution of AI, we will realize the importance of academic and professional guidance in the teaching and learning process. The objective of this process is for the student to "self-discover" and recognize their strengths (to enhance them), identify their weaknesses (to mitigate them), and acknowledge and internalize their life interests and passions.

Only if this self-discovery process is carried out in a matured manner, the student will gain the necessary criteria to begin deciding whether to pursue a humanistic, social, technological, or scientific bachelor's degree. Only if, during this process, the student internalizes the importance of aligning all these competencies, values, and interests with employability, will their future frustration or happiness to a certain extent depend on it.

Therefore, besides making certain models or educational centers that apparently are "exemplary" in meeting specific ratios very visible, AI will also highlight how they may actually be generating unhappy and frustrated students in adulthood. Conversely, certain centers (such as specific middle and higher-level cycles), often invisible, may be generating happy individuals with a high degree of employability.

Data intelligence will bring to light the impact and relevance of the academic and career guidance process as a fully integrated and continuous process in the teaching and learning journey from very early ages, not just from the fourth year of secondary education (ESO) or later ages.

Given the importance of academic and career guidance, AI, for example, with its personalized virtual tutoring based on "successful" patterns of other students in terms of their happiness and employability, can become a mechanism to evaluate and implement to make this process more efficient. In any case, with or without artificial intelligence, this is a process that we must focus on and improve.

In conclusion, I am convinced that AI will show us, based on data and evidence, that we are not systematically guiding the different agents in the value chain of the education system with coherent indicators to ensure that today's students, future citizens, and workers, are happy, while guaranteeing them the highest possible employability percentage.

Perhaps you might be interested

What is AI, artificial intelligence, and its applications?