Oral exam preparation is changing: not because AI “replaces” studying, but because it makes it possible to train systematically what often remains implicit—presentation skills, argumentation, time management, and the conscious use of connections. For teachers, the challenge is to turn these tools into transparent, assessable teaching practices, avoiding the “shortcut” effect. In this guide we look at how to design aoral exam simulationwith artificial intelligence, how to build coherent rubrics, and how to support students on their way to the Maturità (the Italian secondary school leaving exam), with a look atinnovative AI teachingwithout losing pedagogical centrality. The examples are designed to be applicable in class and at home, including with tools such asStudierAI, while keeping the teacher as the director of the learning pathway and the guarantor of assessment.
Why use AI to prepare for the oral exam (and what changes for teachers and students)
The oral exam is a complex test because it combines subject knowledge and transversal skills: selecting content, building an argumentative line, using appropriate vocabulary, responding to unexpected questions. From a pedagogical perspective, effective training requiresdeliberate practice: frequent attempts, immediate feedback, clear goals, and a progression of difficulty. This is where AI can make the difference: it makes sustainable what at school is often limited by time (individual simulations, targeted repetition, precise feedback).
In AI-based oral exam preparation, the most useful use is not “having the topic explained,” butsimulating the interaction: interview-style questions, requests for clarification, follow-up questions, managing uncertainty. AI can also personalize the pathway: if a student shows recurring gaps (definitions, logical steps, examples), subsequent practice can focus on those points with measurable micro-goals.
What changes for teachers? The role shifts from “questioning when possible” todesigning training environments: defining criteria, performance examples, rubrics, and moments of metacognitive reflection. AI becomes a practice tutor, while the teacher retains: (1) selection of the core concepts, (2) alignment with class objectives, (3) responsibility for assessment, and (4) care for ethical aspects (privacy, transparency, responsible use).
Limits to make explicit to students: AI can make mistakes, can “sound” convincing even when it is inaccurate, and can flatten answers into generic formulas. For this reason, it is essential to frame practice astraining with verification: every output must be checked against textbooks, notes, reliable sources, and the required subject-specific terminology. In other words: AI does not replace studying, but makes it more active and observable.
How an AI oral simulation works: setup, prompts, and rubrics
An effective AI oral simulation is not a “free” chat: it is a structured sequence, with roles and criteria. A replicable workflow in class (or for independent study) can follow these steps: define the scope, generate questions, conduct interview turns, provide rubric-based feedback, and plan the next training session.
To make the process transparent to students, it helps to state explicitly that the goal is not to “guess the question,” but totrain an observable performance: clarity, accuracy, depth, examples, connections, ability to rephrase. This also makes it easier to link the simulation to the assessment criteria already used in the department.
- 1) Setup: choose subject(s), level (e.g., fifth year of upper secondary school), duration (8–12 minutes), and 3–5 core concepts (concepts or authors).
- 2) Questions: generate an outline of questions (opening, deepening, connecting, terminology-check questions).
- 3) Turns: the student answers; the AI presses with requests for clarification (“can you give an example?”, “define…”, “what is the cause/effect?”).
- 4) Formative assessment: feedback on content and delivery using a rubric (descriptive levels, not just a grade).
- 5) Improvement plan: 2 concrete actions for the next attempt (e.g., “include 2 examples,” “reduce filler words,” “close with a summary”).
The heart of the system is the prompts. An effective prompt for teachers doesn’t ask “ask me questions,” but specifies: the examiner’s role, the expected standard, constraints (duration, style), feedback criteria, and error handling. A concise example reusable across subjects:
“Act as a teacher-examiner. Simulate a 10-minute oral interview on these core topics: [list]. Ask 1 opening question, then 6–8 questions for deepening and making connections. After each answer: (a) ask for clarification if a definition or an example is missing; (b) point out 1 strength and 1 area to improve. At the end, assess with a rubric on 4 criteria: accuracy, clarity of presentation, argumentation/connections, subject-specific vocabulary. Use levels 1–4 with concrete descriptors.”
Rubrics are crucial to avoid arbitrary feedback. For AI-supported oral assessment in a formative sense, short rubrics (4 criteria, 4 levels) with observable indicators work well. Examples of indicators: “correctly defines key concepts,” “uses logical connectors,” “provides relevant examples,” “responds to requests for clarification without contradicting themselves.” When the rubric is shared, the student can self-assess before reading the AI feedback: it’s a metacognitive step that increases the effectiveness of training.
How to prepare for the Maturità oral exam with AI (towards Maturità 2027): a smart study pathway
In view of Maturità 2027 preparation (the Italian secondary school leaving exam), it is useful to propose a pathway that combines active review, building connections, and performance training. AI can support each phase, but instructional design must remain anchored to clear objectives: know, connect, present, argue. A 5-phase framework, integrable intoMaturità preparation (the Italian secondary school leaving exam), works well both for heterogeneous classes and for independent students.
Phase 1 — Active review (not a summary). Ask students to produce short answers to targeted questions (definitions, causes/effects, comparisons). AI can generate graded sets of questions and, above all, ask for examples and counterexamples. Goal: turn knowledge into rapid retrieval, reducing “blank spots” at the start of the interview.
Phase 2 — Maps and connections (guided interdisciplinarity). Interdisciplinarity is not a list of hooks: it is a network of motivated relationships. Here AI can help propose possible bridges, but the teacher should always require thejustification of the link(“why does this concept shed light on that other one?”). Concrete activity: each student builds 3 connections between two subjects and prepares 2 bridge sentences (one descriptive and one argumentative) to use in the interview.
Phase 3 — Presentation: from content to performance. This is where AI study methods for teachers come into play: not “study more,” but “study better” through short cycles of presentation (2–3 minutes) with immediate feedback on structure and clarity. Teaching tip: have students work on standard “openings” and “closings,” because they reduce anxiety and increase coherence. Example opening: definition + context + thesis. Example closing: summary + implication + next connection.
Phase 4 — Time management and unexpected questions. A typical weakness in oral exams is dwelling on details and losing the thread. Train two routines: (1) a 20–30 second answer (central idea + one example), (2) a 90 second answer (central idea + two arguments + mini-summary). AI can time the turns and propose “disruptor questions” to test logical resilience.
Phase 5 — Anxiety and self-regulation. Without encroaching on clinical domains, you can teach school-based strategies: brief breathing before starting, a 3-point outline, use of intentional pauses. A good AI exercise is the “restart”: the student stops halfway and must resume with a realignment sentence (“So far I’ve shown…, now I’ll move on to…”). It’s a communication skill that often distinguishes a merely sufficient performance from a solid one.
Can AI assess an oral exam reliably?
The useful answer for schools is: AI can support an assessment, but it cannot be the only source of judgment. Reliability depends on three factors: input quality (faithful transcription or student-produced text), clarity of criteria (rubric), and human oversight. Without these elements, the risk is confusing fluency with subject competence, or penalizing non-standard communication styles.
To make AI-supported oral assessment more robust, it is best to anchor it to observable and verifiable criteria. Some useful examples at department level:
- Clarity: recognizable structure (introduction–development–summary), understandable sentences, use of connectors.
- Accuracy: precise definitions, absence of conceptual errors, relevant examples.
- Argumentation: presence of a thesis, reasons, cause-effect relationships, comparison between positions or interpretations.
- Vocabulary: appropriate use of subject-specific terms, ability to rephrase without losing precision.
The issue of bias remains: language models can favor a more “academic” style and penalize linguistic varieties or students with specific needs. For this reason, AI should be used as asecond readerand not as a judge: it produces evidence (strengths, critical points, rephrasing examples), while the final decision remains with the teacher, who considers context, learning pathway, and progress.
Privacy and responsible use: avoid entering personal data, health information, or sensitive details. Agree on class rules: what can be uploaded, how to cite sources, how to verify answers. If you use audio recordings or transcripts, define retention times and educational purposes. Transparency is not bureaucracy: it is part of digital competence and citizenship.
StudierAI in class and at home: guided simulations, feedback, and student autonomy
To make practice scalable, you need a tool that turns simulation into routine: questions aligned with the syllabus, readable feedback, trackable progress, and assignable tasks. StudierAI is designed precisely for this: to support teachers and students in AI-based oral exam preparation with guided and customizable pathways. If you want to explore it with the class or independently, you canstart for freeand evaluate how to integrate it into your practices, keeping criteria and transparency at the center (for context and mission:who we are).
In class, an effective model is the “rotating guided simulation”: 10 minutes of pair work (one presents, one observes with the rubric), then 10 minutes with the AI to refine one specific criterion (e.g., examples, definitions, connections). The teacher collects minimal evidence (one note per student) and decides the focus for the following week. This reduces the correction workload and increases practice frequency.
At home, autonomy grows if the student knows what to train. Here AI is useful when the task is well defined: “present in 2 minutes and get feedback on structure,” “answer 5 deepening questions and check definitions,” “simulate an interdisciplinary connection and defend it with an example.” Tracking progress (even just as a checklist) helps turn training into a habit, not an occasional pre-exam activity.
For innovative AI teaching oriented toward oral exams, three final recommendations for teachers:
- Align: objectives, simulation activities, and the rubric must say the same thing (instructional coherence).
- Make improvement visible: few indicators, often, with micro-goals (assessment for learning).
- Teach critical use: fact-checking, citing sources, awareness of AI’s limits (responsibility).
If AI is embedded in a clear methodological framework, the result is not an “easier” oral exam, but a better-trained one: students who can explain, connect, and support their ideas. And that is exactly what schools can and must assess, today and on the road to Maturità 2027 (the Italian secondary school leaving exam).
