Correcting Written Productions with ChatGPT?

The process of correcting written production is inherently complex, often leading educators and learners to seek simplification. Writing fundamentally serves the purpose of communication; therefore, any correction must first verify whether the intended communicative objective has been successfully met. This holistic review typically necessitates addressing three major interconnected levels: the content and richness of ideas, the organization and flow, often managed through connectors, and finally, the language itself.

These three levels are reflected in official evaluation grids, such as those used for the DELF B2 exam. For instance, morphosyntax (grammar) accounts for only 5 points out of 25. The majority of the score is allocated to task realization (content) and coherence (organization), indicating that structural and conceptual elements carry significant weight. Learners often mistakenly focus only on surface errors, like past participle agreement, neglecting the crucial work of sentence reformulation necessary for formal writing variety.

Correction is described as a profession, implying a set of skills and practices developed through repetition. Educators acquire tacit know-how—the ability to perform the task without always being able to articulate the exact steps taken. This complexity is precisely where artificial intelligence, lacking this professional experience, requires explicit guidance to function effectively.

Artificial intelligence holds the potential to significantly augment the learning of written skills and introduce novel collaborative opportunities between students and teachers. For self-preparers of exams like the DELF, AI assistants now offer comprehensive correction across the three revision levels, often providing explanations that surpass those offered by human instructors. This capability extends beyond simple surface language checks, which older tools like Bon Patron only addressed.

The most interesting opportunity provided by AI is that by interacting with the machine, the learner pays closer attention to the process of task realization and revision itself.

While receiving a corrected text is beneficial, the true value lies in the long-term improvement derived from structuring the activity and engaging in the revision process. This strongly encourages the practice of self-correction, a skill deemed necessary and highly valued, particularly from the B2 level onward, especially concerning grammatical control.

Artificial intelligence is not positioned to replace the teacher; rather, the primary role of the educator shifts toward guiding the learner through the self-correction process. Activities can be designed where students learn to dialogue effectively with the machine, formulating precise questions and instructions. This interaction allows the teacher to alleviate some of the time burden associated with initial correction.

A simple scenario involves the student drafting a text, using ChatGPT for an initial cleanup of frequent language errors, such as plural 's' issues. The student then integrates these changes into a second version, which the teacher subsequently corrects in detail, focusing on more precise objectives. Furthermore, utilizing specialized chatbots for specific text types, like argumentative essays, presents another interesting avenue for focused practice.

It is important to imagine activities where one learns to dialogue with the machine, asking it questions, drafting instructions, etc.

The effectiveness of AI hinges entirely on the user's ability to instruct it properly, countering the common critique that the tool 'does whatever it wants.' A chatbot simulates human conversation, but complex correction tasks demand precise instructions, known as prompts. These prompts must be clear, direct, and specific, often utilizing imperative verbs. Crucially, users should request simple explanations, as the AI tends toward verbosity.

For basic language correction, instructions can request a table format detailing the original sentence, the correction, and a simple explanation of the rule violated. This structured output is highly comprehensible for learners. A more advanced technique involves directing the chatbot to prompt the user to correct errors themselves, waiting for the user's response before proceeding to the next error, fostering active engagement.

For highly complex tasks, such as fully correcting a DALF C1 argumentative essay, creating a specialized GPT is beneficial, although this requires a paid subscription. A specialized GPT encapsulates all necessary instructions and documentation within its configuration, meaning the end-user (the student) does not need to input lengthy prompts repeatedly. This method precisely frames the correction process and prevents misuse, such as the AI writing the text entirely for the student.

The demonstration showcased a specialized GPT configured for the Production écrite DELF B2 book, using four structured steps: criterion analysis, language self-correction guidance, improvement summary, and next steps. By providing the GPT with the exam criteria and linguistic benchmarks, the system delivers objective, technical feedback rapidly. This allows the teacher to focus on the more human dimensions, like originality and vocabulary application.

The interaction proved highly effective for guiding development. When an argument needed more depth, the AI could cite the insufficient section and propose concrete ways to enrich the idea, such as detailing consequences. Furthermore, the system could identify stylistic weaknesses, flagging overly familiar expressions or weak verbs (like 'faire' or 'avoir'), and suggest formal reformulations, ensuring the output remains tailored to the student's direction.

User tests involving both teachers and learners indicated that the tool was easy to use and explanations were comprehensible for the B2 level, significantly improving the visibility of the revision process. However, a surprising finding was that language errors were not corrected sufficiently, perhaps because users failed to explicitly ask for more synonyms or connective suggestions, underscoring the need for active interaction.

Significant limitations exist, particularly concerning access; free versions of these tools often cease correction midway, usually before reaching the language review stage, necessitating a paid subscription for regular use. Another challenge involves digitizing handwritten copies, which still requires revision, especially ensuring the AI only transcribes and does not automatically correct perceived errors during digitization.

Generative AI prioritizes content creation over educational optimization, demanding precise framing to avoid irrelevant output. Crucially, current AI models cannot reliably provide scaled scores for complex assessments like the DELF B2. Tests showed AI is significantly harsher on linguistic criteria because it lacks the human examiner's ability to tolerate minor errors that do not impede overall comprehension.

The main risks involve creating dependency on the machine or nullifying learning gains if the student simply asks the AI to complete the task. Learners must practice self-correction independently before consulting the tool. Finally, educators must ensure the protection of personal data when submitting student work for analysis. The concluding advice is pragmatic: do not wait for the perfect tool, as evolution is rapid; testing functional tools now provides immediate value.