Can artificial intelligence help us better understand people, personalize support, and strengthen the competencies that truly matter?

Four universities—two in Colombia and two in Spain—shared their answers in this webinar. Not theory. Real-world practice.

Why Do the Skills That Don’t Appear on a CV Matter?

More than 85% of academic, professional, and life success can be explained by socio-emotional competencies. This is not a hypothesis; it is the result of years of accumulated evidence across multiple fields.

The World Bank reaffirmed this in its latest 2025 report: 7 out of 10 key skills for the future belong to this category. Yet across Latin America, more than 50% of companies struggle to find people who possess them.

This challenge is compounded by another structural issue: university dropout rates approaching 50% across the region, far above the OECD average of approximately 21%. The lack of socio-emotional development is not unrelated to this phenomenon.

The question is no longer whether these competencies matter.

The real question is how to measure them without bias, how to develop them at scale, and how to do so based on evidence.

That is exactly what our four guest speakers came to discuss.

Areandina (Colombia): From Employability to Life Purpose

Luz Esperanza, National Director of Alumni at Corporación Universitaria Areandina, works with more than 48,000 students and graduates.

Her team decided to move beyond the traditional concept of employability and focus on the individual. The shift was not merely conceptual—it responded to a real challenge: generic processes that failed to adapt to individual needs.

They have assessed hundreds of students and alumni by integrating AI into their support model: understand, guide, and connect.

“We realized that the most important thing is self-awareness. We no longer begin by asking, ‘What job do you want?’ We begin by asking, ‘Who are you?’”

The integration of Human AI addressed three key limitations of their previous model:

• Subjectivity and self-perception bias.

• Lack of traceability in assessments.

• Difficulty scaling personalized support.

Today, they can:

• Identify more than 30 socio-emotional competencies in just minutes.

• Transform free-text responses into analyzable data about how a person thinks, makes decisions, and relates to others.

• Generate concrete action plans rather than simply producing assessments.

The impact is clear: employability is no longer a generic process. It becomes an individualized experience that aligns more closely with each person's life project.

In practical terms, students arrive at recruitment processes better prepared because they know themselves better and can communicate their strengths more effectively.

Public University of Navarra (Spain): Assessing Those Who Manage Conflict

Eduardo Santos, researcher in the Department of Public Law at the Public University of Navarra (UPNA), presented a case with a highly specific challenge: training and evaluating restorative justice facilitators—a professional profile where self-awareness is not optional.

The challenge was twofold:

• How to assess complex competencies such as empathy, bias awareness, and decision-making.

• How to do so rigorously within a pilot program linked to public policy initiatives.

The project involved 47 participants and combined:

• Free-text responses based on natural language.

• Custom-designed qualitative questions.

• Comparative analysis between self-perception and objective results.

The outcome included 25 individual reports and one group report, making it possible to:

• Identify real patterns in key competencies such as empathy, conscientiousness, and openness.

• Foster deep reflection among participants.

• Detect discrepancies between what individuals believe about themselves and what they actually demonstrate.

“For many people, it helped them reflect on the tools they have—and the ones they do not.”

The initiative also addressed a less visible but critical institutional challenge:

Evaluating the evaluators themselves and improving the quality of the training program.

University of Navarra – Innovation Factory (Spain): Measuring to Develop

Belén Goñi, Director of the Innovation Factory at the University of Navarra, shared the experience of the GPI program, where students from different disciplines spend three years working on entrepreneurship and innovation projects.

The initial challenge was clear:

Traditional assessment tools were heavily influenced by self-perception and did not provide sufficiently reliable measurements.

The solution involved:

• Assessing entrepreneurial competencies through AI, without the possibility of conscious response bias.

• Designing personalized development plans based on the results.

• Measuring real progress between the second and third years of the program.

But the value extends beyond the individual student.

At the program level, they also solved another key challenge:

A lack of visibility into group-level patterns.

They can now:

• Identify competencies that consistently score lower across cohorts, such as self-confidence.

• Adapt the training program based on real data.

• Design both individual and collective interventions.

“Every student is different, but when you analyze them together, patterns emerge that we simply couldn’t see before.”

University of La Sabana (Colombia): From Pilot Project to Institutional System

Andrés Mejía, director of two master's programs at the University of La Sabana, presented the most scalable case: 313 students from 25 different academic programs participating in an open innovation pilot.

The starting point was a familiar challenge:

• Traditional assessments based on surveys and interviews were lengthy, biased, and difficult to translate into action.

The integration of Human AI provided a solution:

• More natural and authentic assessments based on students’ own language.

• Standardized diagnostics that can be analyzed at multiple levels—individual, group, program, and faculty.

• Automatic generation of personalized development plans.

But the most significant transformation occurred at the strategic level:

• Early detection of psychoeducational risk factors.

• Real-time segmentation and comparative analysis.

• Longitudinal tracking of students from undergraduate studies through postgraduate education.

“We don’t just diagnose. We have tools for tutoring and mentoring that can be tailored to each student.”

This shifts university management from a reactive model to a predictive one.

What Connects These Four Experiences?

Different institutions. Different contexts. Different objectives.

And yet, they all faced the same underlying challenges:

• Generic processes that fail to capture individual differences.

• Biased or unreliable assessments.

• Limited evidence to support decision-making.

• Difficulty scaling personalized support.

And the same solutions emerge:

• Data-driven self-awareness.

• Genuine personalization, not just personalization in theory.

• Objective and traceable assessment.

• Concrete action plans.

• The ability to analyze at scale.

Human AI does not replace teachers, advisors, or mentors.

It enables them to do their work better.

Measuring to Improve. Supporting to Transform.

The evidence is clear: socio-emotional competencies can be developed.

But development does not happen through intuition alone.

It happens through measurement, feedback, and action.

What these four cases demonstrate is a shift in mindset:

Moving from guessing to understanding, from generalization to personalization, and from assessment to evidence-based support.

What Comes Next?

The question is no longer whether AI should be integrated into education.

The question is how to do it with purpose:

To better understand people, make better decisions, and create meaningful impact.

The universities moving in this direction are not simply experimenting with technology.

They are redesigning their educational models.

Is your institution ready to be next?

We can help you design a pilot tailored to your context, your students, and your goals.

Because the future of education is not about technology.

It is about truly understanding people.