Generative AI: A Tool for Developing a Competency-Based Approach
By helping to personalize learning paths, artificial intelligence enhances the interactive nature of learning tools, enabling the competency-based approach to be fully implemented.
Chrysta Pelissier, University of Montpellier
While the changes in education driven by digital technology have been an intriguing subject of study since their emergence, the release of ChatGPT 5 (GPT-5) on August 7, 2025, by OpenAI lends these research questions particular urgency. Changes in educational practices have indeed accelerated considerably in recent months, making greater room for technological tools derived in particular from generative artificial intelligence (AI).
Teachers, pupils, students, and training coordinators have embraced these new ways of sharing information and exchanging ideas.
Can this set of tools help develop new ways of approaching learning, with a focus on a competency-based approach?
The competency-based approach: an evolving methodology
The competency-based approach took shape with the 2014 national framework document for degree programs leading to the award of national bachelor’s, professional bachelor’s, and master’s degrees. It was supplemented in 2018 by the decree governing the bachelor’s degree and, in 2019, by the decree governing the professional bachelor’s degree.
This approach is driving new and profound changes in universities. It is defined as an approach to teaching design that seeks to go beyond certain limitations of objective-based pedagogy.
For teachers, the changes associated with the competency-based approach primarily concern the methods used to convey knowledge and provide support to individual students. In terms of teaching methods, the competency-based approach represents a shift in which instructional objectives are no longer defined as content to be conveyed, but rather as practical skills that a learner must develop in order to perform their duties in a professional setting.
Faced with these new professional situations, students are encouraged to identify their own needs in terms of knowledge, skills, and professional attitudes, and then to develop a process that involves selecting from their existing knowledge, conducting research, and seeking guidance from professionals.
Examples of how learners can use generative AI
First, in activities that encourage discovery-based learning, learners can explore and ask questions. For example, they can ask a generative AI to present scenarios specific to a particular profession—one they are considering—to help them grasp the full range of possibilities and practice formulating solutions for each scenario.
Next, the learner can ask the AI to provide examples of jobs related to the skills they have identified themselves. Note that the goal is not to impose a specific job but rather to point them toward possible career options. It will then be up to the learner to verify the responsibilities associated with these jobs and the skills required for them.
Finally, ChatGPT can help them“learn how to learn”by helping them plan their study sessions (“What are your priorities?”), set aside time for breaks (“When are your break times?”), and consider their study methods (“What did you find easy today?” “Why?”, or “Could you have done it differently?”), and receive encouragement in setting personal goals (“What do you want to understand today?”, “What was your goal for today?”, “Are you proud of yourself?”)
Examples of how teachers can use generative AI
First, educational scenarios can be developed based on real-world data, encouraging teachers to create escape games, quizzes, board games, or immersive experiences tailored to the specific profession targeted by the training program.
In addition, generative AI applications can offer activities designed to develop skills that the learner has chosen themselves, those targeted by the teacher, as well as to answer questions related to a specific concept that may not have been fully understood in class.
By analyzing various materials, AI tools can help identify collective needs (within a single class) or create training materials, explanations, and feedback in different formats (text, podcasts, videos, and image collages), tailored to different learners or groups of learners.
Finally, teachers may consider offering activities designed to foster critical thinking, using various methods for creating prompts, comparing the results of multiple AI systems, and developing students’ ability to construct arguments.
This article is published as part of the Fête de la Science (which took place from October 3 to 13, 2025), of which The Conversation France is a partner. This year’s edition focuses on the theme “Intelligence(s).” Find all the events in your region on the Fetedelascience.fr website.
Chrysta Pelissier, Associate Professor of Linguistics and Education, University of Montpellier
This article is republished from The Conversation under a Creative Commons license. Readthe original article.