The Grand Oral Exam and French as a Foreign Language: How Artificial Intelligence Is Reinventing Student Preparation

An Exam That Challenges Traditional Teaching Habits

Since its introduction in 2021, the Grand Oral — a high-stakes oral component of the French baccalaureate — has profoundly changed how teachers approach spoken-language preparation. Twenty minutes before an examining panel, a question designed by the student themselves, and a demonstrated ability to "connect knowledge to contemporary issues": the exam goes far beyond recitation, demanding structured thinking and persuasive expression.

For allophone or bilingual students — a significant population in French schools welcoming newly arrived immigrants or pupils in international streams — this format represents an additional challenge. Mastering written French is already a long journey; delivering it orally with ease, nuance, and conviction is another matter entirely.

FLE and the Challenge of Academic Oral Language

The teaching of French as a Foreign Language (FLE — Français Langue Étrangère) has historically prioritised everyday communication levels (A1–B1) before moving into academic or specialised registers. Yet the Grand Oral-style exercise calls for a very particular register: structured argumentation, spontaneous reformulation, and the ability to hold a line of reasoning under the pressure of examiner questions.

This register can be trained. It requires hours of exposure to model discourse, rapid-planning exercises ("oral mind-mapping", "thinking aloud"), and above all frequent, supportive feedback. This is where digital tools — and more recently artificial intelligence — enter the picture.

AI as a Training Partner

Next-generation conversational assistants offer something unprecedented: an interlocutor available at any hour, patient, capable of simulating a demanding examiner or a supportive coach depending on the student's needs. A student can thus:

• Rehearse their presentation as many times as desired, receiving immediate feedback on argument clarity, discourse cohesion, and lexical register.
• Practise the interaction by responding to unexpected questions, with the AI playing the role of a curious or sceptical examiner.
• Work on phonology using speech-recognition tools paired with pronunciation-correction modules (liaisons, linking sounds, sentence rhythm).
• Build their idiomatic repertoire by requesting alternative ways to express the same idea, or more elegant phrasings suited to a C1 level.

This kind of practice does not replace the teacher's expert eye; it complements it. Students arrive in class with accumulated practice already behind them, better positioned to benefit from their teacher's experienced feedback.

Limitations That Cannot Be Ignored

Enthusiasm for these tools should not obscure their pedagogical limitations. An AI assistant, however capable, cannot perceive posture, breathing, or the hesitation that betrays a deep misunderstanding. It also risks validating formulations that are grammatically correct but intellectually hollow — what FLE teachers sometimes call "fluent emptiness."

Moreover, reliance on automatic correction can inhibit the linguistic risk-taking that is nonetheless essential to acquisition. Learning a language means accepting mistakes, being corrected, and starting over. An overly accommodating tool can short-circuit this process.

The ideal educational stance is therefore one of reflective use: the FLE teacher introduces the tool, demonstrates its possibilities and biases, and invites students to think critically about what they receive from it. Only on this condition does digital technology become formative rather than substitutive.

Toward an Augmented Pedagogy

Experiments carried out across several French school districts show encouraging results: B1–B2 level students who used a conversational assistant to prepare their Grand Oral reported a significant reduction in speaking anxiety, as well as greater ease in structuring their arguments.

These results are not surprising when one considers what language-acquisition researchers call the output hypothesis: to progress, learners must not only receive language (input) but also produce it (output) under conditions that push them to their limits. AI offers precisely this low-social-risk output space, where making a mistake is a source of progress rather than shame.

Implications for Teacher Training

Integrating these tools into FLE practice requires an evolution in both initial and continuing teacher education. Teachers need:

• Solid digital literacy to assess the relevance of a tool against specific pedagogical objectives.
• A clear ethical framework governing the data produced by students when interacting with third-party systems.
• Didactic reflection on how to articulate AI-mediated activities with authentic human interaction.

FLE departments at universities are beginning to integrate these questions into their master's programmes. Professional associations such as the GFEN and the FIPF are publishing resources to help practitioners navigate this shifting landscape.

A Pedagogical Opportunity to Seize with Discernment

The Grand Oral, with its demands for intellectual autonomy and personal expression, is paradoxically the exam that resists machine delegation most effectively. A robot cannot sit the exam in a student's place. What AI can do, however, is multiply opportunities to practise, to encounter linguistic otherness, and to sharpen thinking through dialogue.

For FLE students, this opportunity is particularly valuable: it partially bridges the deficit of exposure to the target language experienced by those for whom French is not the language of the home. It does not replace immersion, but it offers a useful, available, and personalised simulation of it.

Artificial intelligence in education is neither a panacea nor a threat. It is a mirror held up to the language for those willing to practise in it. The quality of the reflection depends, as always, on the quality of the hand that holds it.