The 2026 school-leaving exam arrives in a context where schools (rightly) demand solid, well-justified, consistent assessments, but time and resources remain limited. In this scenario, AI can become operational support: not to “grade in place of the teacher,” but to speed up reading, structure feedback, and make the decision-making path more transparent. In this article you’ll find a practical approach to grading school-leaving exam essays with AI and to AI-supported oral assessment for the school-leaving exam, with guidance on privacy and communication.
Why in 2026 grading (written and oral) is under pressure
For many teachers and committees, the pressure is not only “quantitative” (number of papers, large classes), but also “qualitative”: you need to demonstrate consistency between criteria, justify judgments, and handle requests for clarification. In the 2026 school-leaving exam this is amplified because high expectations, tight timelines, and the need to document assessment choices all intersect. Here AI can help mainly on three fronts:standardize the language of feedback,reduce first-read timeandimprove traceabilityof observations (strengths, recurring errors, suggestions). The key point: AI does not replace professional responsibility, but it can make the process more sustainable.
Grading school-leaving exam essays with AI: a practical, controllable workflow
An effective workflow for how to use AI to grade student work must be repeatable, verifiable, and aligned with the rubric. The most common mistake is asking AI “what grade would you give?”: it’s better to use it as an assistant to extract evidence and propose feedback wording, leaving the final decision to the teacher. Here is a step-by-step process that many schools are adopting.
- 1) Define the rubric before reading: criteria (content, coherence, accuracy, vocabulary, argumentation) and level descriptors. AI works best when it has clear constraints.
- 2) “Assisted” first read: ask AI to summarize the thesis, structure, and key passages, pointing out where the paper supports (or does not support) claims with examples. This reduces orientation time without replacing reading.
- 3) Evidence by criterion: for each rubric criterion, have AI extract 2–3 quotations or references (paraphrases) that justify a level. This gives you a documentary basis that is also useful in case of discussion among examiners.
- 4) Two-layer feedback: (a) a concise comment “to hand back” to the student; (b) internal technical notes (recurring errors, points to verify). AI can propose clear, non-judgmental wording, but the teacher reviews it.
- 5) Consistency among teachers: share prompts, the rubric, and “model” feedback examples. The goal is not to standardize sensitivities, but to reduce unmotivated variations in language and strictness.
In practice, grading school-leaving exam essays with AI works when you ask: “show me where this criterion is evident” and “propose improvement-oriented feedback,” not when you delegate judgment. Also, it is useful to keep arecord of requests and responses(even just as notes) to support transparency and consistency.
Oral assessment with artificial intelligence: simulations, grids, and feedback
The oral exam requires real-time observation, managing student anxiety, and alignment among examiners. AI-supported oral assessment for the school-leaving exam can be supported mainly in the preparation and documentation phases: simulations, more operational grids, and post-test feedback. AI can help generate sets of questions consistent with syllabi and thematic cores, varying difficulty and style (clarifying questions, connections, counterarguments).
A useful approach is to treat the simulation as an “authentic task” withobservable criteria: clarity of presentation, relevance, quality of connections, use of subject-specific vocabulary, ability to argue and to handle unexpected questions. AI can propose a grid with indicators and descriptors, but it is essential that the committee adapts it to its context and shares it transparently.
- Guided simulations: AI generates an interview outline with 8–12 progressive questions and possible follow-ups.
- Post-oral feedback: starting from the teacher’s notes (not necessarily a transcript), AI can turn rough notes into a structured comment: strengths, improvement priorities, study suggestions.
- Fairness: use prompts that ask to assess only evidence (examples, definitions, connections) and avoid requests that infer motivation or “ability” in a generic way.
In short: AI supports preparation and feedback, while the teacher remains in control of facilitation and judgment. This is particularly useful when multiple people must converge on common criteria, reducing ambiguity and differences in interpretation.
MIM guidelines, privacy, and transparency: how to use AI without risks
To use AI tools for high school teachers sustainably, you need simple, shared rules. The MIM’s guidance on the use of AI at school (and, more generally, the privacy framework) points to some practical principles: data minimization, clear purposes, security, and transparency. With the 2026 school-leaving exam in mind, it’s worth adopting an internal “checklist.”
- Data: avoid entering unnecessary personal data. When possible, anonymize (initials, codes) and separate the paper from the student’s identity.
- Transparency: explain to students and families that AI is used to support feedback and consistency, while assessment remains the responsibility of the teacher/committee.
- Bias and quality: check samples of papers with and without AI support to monitor any distortions (for example, penalizing non-standard but correct styles).
- Documentation: keep the rubric, prompts, and shared criteria. In case of discussion, it is more useful to show the assessment pathway than the AI “output.”
These precautions reduce risks and increase trust. The goal is to make AI an ally of teachers’ professionalism: more time for teaching and guidance, less time on repetitive tasks.
How StudierAI can help teachers and committees: use cases for essays and orals
Among AI tools for high school teachers,StudierAIcan be used as support to standardize rubrics, generate feedback, and prepare simulations. The idea is simple: turn repetitive activities (summarizing, structuring comments, proposing questions) into faster steps, while maintaining pedagogical control. If you want to understand the approach and the project, you can also consultabout us.
Concrete examples of use with the 2026 school-leaving exam in mind:
- Ready-to-use rubrics: creating or adapting rubrics for text types, with clear descriptors and language that can be shared within the department.
- Personalized and consistent comments: drafts of feedback by criterion (content, structure, vocabulary), with practical suggestions for revision and study.
- Reports for the committee: summaries of recurring class issues (frequent errors, areas for improvement), useful for calibrating targeted review without exposing sensitive data.
- StudierAI for school-leaving exam oral simulations: generating questions, follow-ups, and observable criteria, plus feedback templates to help the student improve between one test and the next.
If you want to experiment gradually, you canstart for free(orsign up for free) and begin with just one step of the workflow: for example, generating feedback by criterion or creating an oral-exam grid. Effectiveness is measured on two indicators:time savedandfeedback quality/transparency. When these grow together, AI truly becomes a teaching and organizational support, not a risk to be managed.
