AI-Powered Skills Portrait: Revealing Student Competencies Beyond the CV

AI Level

Jules COTTIN DE NICOLA, NEOMA Business School Mélissa BLACODON, NEOMA Business School Maxime SCHLUBACH, NEOMA Business School

Training / education / pedagogy

Nowadays, many students struggle to articulate what they're capable of. They have a hard time translating their internships or projects into skills that employers can recognize. Our project uses AI that converses with the student. It asks them questions about their experiences. The AI then translates their answers into a Skills Profile. This helps companies understand the candidate's true value and set a fair and transparent salary.

Education is the government's largest budget item. It is therefore vital that young people find their place in the workforce. This project is important because it helps students who lack self-confidence. The AI transforms their simple words into professional skills. It helps uncover hidden talents among those who may not have attended prestigious universities.It is also a response to the new laws of 2026. Companies will soon have to prove why they pay an employee a certain amount in Europe. Our AI provides concrete evidence to justify salaries honestly. This prevents unfair judgments and career guidance errors. In short, AI makes recruitment more human and equitable. It gives all students a solid foundation for their entire career.

Through this competition, we aim to develop our AI skills and knowledge of digital tools. The challenge is an opportunity to strengthen our teamwork, analytical skills, and ability to generate innovative ideas. Each team member also hopes to participate in a project that addresses a current issue, meet professionals, expand their network, and foster their curiosity. This might even lead to some entrepreneurial ideas. As students passionate about AI, we would be delighted to collaborate to shape the future and be agents of change.

1. What is the situation or context you are addressing? Many students today struggle to present their skills effectively. Their CVs rarely reflect their true potential: experiences are poorly described, informal skills are invisible, and the gap between what they have actually done and what they manage to communicate is often significant. Recruiters, overwhelmed by applications, rely on keywords and formatting rather than real competencies. As a result, talented students, especially those without prestigious networks or grandes écoles backgrounds, are systematically undervalued. This is not a question of competence, but of expression. PortraitScopie is an existing competency mapping solution developed by an HR engineer, designed to reconcile people with employment by structuring their skills into detailed, readable portraits. Rather than summarizing a profile in a few bullet points, it describes each competency in depth, capturing savoir-faire, savoir-être, and knowledge. Our team builds on this framework and proposes to extend it through a conversational AI layer specifically designed for students. At the same time, new European regulations coming into force in 2026 will require companies to justify salary decisions transparently, making the ability to objectively document and communicate skills more critical than ever. 2. What is your critical analysis of this situation? Existing tools like Brio or The Patch attempt to bridge this gap, but they work from existing documents, CVs, cover letters, and optimize their form without questioning their substance. They improve presentation but do not reveal what lies beneath. Furthermore, a purely scoring-based approach risks reducing candidates to a set of data points, ignoring motivation, context, and personal trajectory. At worst, it recreates a form of social credit system that penalizes those who are already least visible. There is also a risk that conversational AI, if poorly designed, simply replicates the same questions a recruiter would ask, displacing the problem rather than solving it. AI cannot replace self-confidence, but it can help structure and articulate it. The real challenge is not to automate recruitment, but to give every student the tools to be understood on their own terms. 3. What perspectives were discussed and how were they debated or arbitrated within your team? Our team debated four key tensions. PortraitScopie as a foundation vs. starting from scratch. We chose to integrate PortraitScopie as a competency reading framework, while adding a conversational layer the original solution did not fully address. PortraitScopie provides the structure; AI provides the dialogue. Quantitative scoring vs. qualitative portrait. We concluded that scoring is useful as a first filter for recruiters, but insufficient alone. The portrait of competence must remain human and contextual, capturing not just what a student can do, but how and in what circumstances they developed that ability. Designing for the student vs. designing for the recruiter. We decided to design primarily for the student, giving them full control over what they share and with whom. The recruiter receives a structured, readable output, but the student owns the process. Guidance vs. openness. The tool must not lock students into predefined categories. The goal is exploration and empowerment, not optimization toward a single outcome. 4. What contribution are you proposing, and under what conditions could it be implemented? We propose an AI-powered conversational tool built around PortraitScopie's competency framework, structured in three stages. Stage 1: Reveal. The student does not just upload a CV. The AI asks targeted questions about their experiences, projects, extracurricular activities, and informal skills, uncovering competencies they would not have thought to mention. The conversation is designed to feel natural, not like a form. Stage 2: Match. The student inputs job offers they are targeting. The AI identifies keyword trends, required competencies, and gaps between what the student has and what the market expects, drawing on established frameworks like the ROME classification or the European ESCO system. Stage 3: Act. For each identified gap, the AI suggests concrete actions: rephrasing existing experiences, joining student associations, completing MOOCs, or pursuing certifications. It generates a personalized roadmap ordered by priority and adapted to the student's timeline. The output is a living Skills Portrait, not a static document, but an evolving profile that grows with the student. It can be shared selectively with recruiters, respecting GDPR standards and the new European salary transparency regulations. Conditions for implementation: Human and legal expertise. Career counselors and HR professionals should validate the competency framework. A legal expert in GDPR compliance is essential to guarantee that student data remains private, portable, and never used without explicit consent. Technical infrastructure. The tool requires access to structured occupational data (ROME, ESCO) and integration with AI language models capable of nuanced conversational analysis. A simple, accessible interface designed with students in mind is critical to adoption. Field validation. We propose piloting the tool with a group of 20 student testers across different academic backgrounds, alongside 5 HR managers, to test whether the Skills Portrait genuinely helps students articulate their value and helps recruiters make fairer, more transparent decisions. Grounded in practice. One of our team members is actively building a SaaS platform called Go Stage, designed to help students find internships through CV optimization, LinkedIn profile analysis, and cover letter generation. This contribution is therefore grounded in real product development, not just theory, and reflects a genuine commitment to solving this problem beyond the scope of this challenge.