Training in AI requires more than an introduction to its tools

Training in AI requires more than an introduction to its tools

A few weeks of training, a certificate to share and a new skill to display on a professional profile. As artificial intelligence spreads, introductory courses are proliferating. Some providers present themselves as schools and associate the discovery of AI tools with promises of mastery or employability.

Such programmes can meet a genuine need. They introduce learners to language models, teach them how to structure prompts and allow them to experiment with possible uses. Their scope, however, should correspond to what they actually teach and assess.

Moving from discovering AI to using it in a professional context requires building knowledge, gaining experience and demonstrating competence.

An introduction provides points of reference and enables learners to complete their first exercises. Professional practice, by contrast, requires the ability to analyse a need, select a method, verify a result and recognise its limitations. It also calls for the ability to adapt one’s approach as the problem changes.

Knowing how to use AI to prepare a document does not, therefore, mean knowing how to integrate it into every activity within a profession. Each use requires appropriate knowledge, learning and practice.

This is precisely the role of a structured learning pathway. It organises progression, builds the necessary prerequisites and defines the competencies to be acquired. Learning activities and assessments are designed to enable learners to develop these competencies and to verify their acquisition. This is the principle of constructive alignment.

This progression should involve practical work, individual and collaborative projects, experimentation and professional simulations.

Students encounter difficulties, formulate hypotheses, test their solutions and correct their mistakes. Formative feedback helps them understand what works and revise their work accordingly. Over time, they learn to apply what they have acquired to new situations. This is known as transfer of learning.

Soft skills and life skills are an integral part of this process. Critical thinking comes into play when verifying answers. Communication enables students to explain a need and defend a line of reasoning. Cooperation requires them to test their ideas against those of others and to make their work usable by their peers. Metacognition helps them recognise what they understand and what they still need to explore further.

These competencies need to be practised and observed in action. Listing critical thinking in a curriculum is not enough. Assessment should make it possible to determine how a student detects an inconsistency, responds to an objection and adjusts their approach.

Assessment should therefore examine both the outcome and the process that produced it. A deliverable produced with AI constitutes initial evidence. Students are also expected to explain how it was built, justify their choices, present the checks they carried out and adapt their solution to new constraints.

Assessed practical work, project reviews and oral defences make it possible to evaluate this understanding. This approach reflects the principles of authentic assessment, based on tasks representative of professional practice and on explicit criteria.

The same standard should apply to certificates and digital badges.

When an introductory certificate is associated, on a professional network, with a skill area as broad as artificial intelligence or machine learning, readers may attribute to it a scope that exceeds the learning outcomes actually assessed.

A verifiable credential ID confirms the provenance of a certificate. It does not, in itself, demonstrate the holder’s level of competence. The value of a certification rests on its competency framework, its assessments, its award criteria and the evidence it requires. Its title should specify what it attests.

Holberton EDU, part of the HBTN group, grounds its pedagogy in this commitment to building and validating competencies. Experiential learning, project-based learning and peer learning combine technical knowledge, problem-solving and the development of human skills.

Students build solutions, test them, explain their decisions and develop their work in response to the feedback they receive. AI is integrated into this approach with a firm emphasis on understanding, discernment and responsibility.

This approach is underpinned by rigorous assessment and certification processes that link stated competencies to concrete achievements, demonstrated understanding and an assessed level of autonomy.

The quality of a training programme is therefore measured by what learners can accomplish, explain and adapt by the end of their pathway. An introduction has its own value. A promise of professional readiness carries an additional responsibility.

For Holberton EDU, training means building competencies, putting them to the test and certifying them with rigour.

‍