For many teachers, the real obstacle isn’t the idea of personalizing, but time.StudierAIcan support the design of inclusive and multilingual learning pathways with a workflow centered on goals, competencies, and alignment between activities and assessment. Some concrete use cases, particularly useful inupper secondary schools:multilingualism1) Differentiated materials without losing class unity: you can generate leveled handouts (basic/intermediate/advanced) on the same content, keeping concepts and keywords unchanged. This supports heterogeneous groups and personalized pathways, without creating two parallel “classes.”StudierAI2) Controlled translation and simplification: instead of a generic translation, you can request a rephrasing with constraints (length, language level, retention of technical terms, contextualized examples). It is particularly useful for newly arrived students or for those studying in Italian as an L2.
3) Coherent exercises and assessments: starting from objectives and prerequisites, you can create sets of graded exercises (comprehension, application, transfer) and tests with linguistic variants, while keeping the same assessment criteria. This also helps manage timing and catch-up work.
4) Progress monitoring: with recurring activities (quizzes, short writing, authentic tasks) you can collect evidence and turn it into observable indicators: growth in subject-specific vocabulary, improved textual cohesion, autonomy in problem solving. The goal isn’t to “measure everything,” but to make the pathway visible.
If you want to experiment quickly, you canstart for freewith a single unit: choose a topic, define 3 essential objectives, and create two language versions (standard Italian and simplified Italian, or Italian + a bridge language). Then check with a short authentic task and a shared rubric.assessmentBest practices, privacy, and assessment: integrating AI responsiblywellbeingIntegrating AI sustainably requires a shared operational framework within the department and, when possible, within the class council. Here are some practical guidelines, useful for protecting students and teaching quality.
AI and inclusive learning: principles, opportunities, and limits
AI can support inclusion if it is used as a tool forQuality criteria: check subject-matter accuracy, linguistic clarity, cultural inclusivity, and the presence of non-stereotypical examples. If a text is “too perfect” but not very understandable, ask for a rephrasing with essential vocabulary and shorter sentences, without losing precision.andAuthentic assessments: prioritize tasks that require process (drafts, steps, justified choices), application to local cases, oral discussion, or lab work. AI can help with preparation, but assessment must observe real, transferable competencies., not as a substitute for the educational relationship. In practice it can help to: generate versions of the same text with different levels of linguistic complexity; propose alternative examples and explanations; create graded exercises; provide rapid formative feedback; translate and rephrase with constraints (for example by keeping key concepts and subject-specific vocabulary).
The principles to hold firm are:If you are considering a more structured adoption, it may be useful to learn about the project’s approach and values: you can find more information on the pageabout us. For an operational trial with your class or your department, you can alsosign up for freeand start with a pilot unit: a few objectives, differentiated materials, an authentic assessment, and a shared rubric. It is often the most effective way to turn multilingualism from an organizational difficulty into a teaching resource.(sources, steps, criteria). AI works well to speed up preparation and expand options; however, the teacher’s guidance remains necessary to ensure coherence with the curriculum and the class’s real needs.
There are also limits and risks:bias(cultural or linguistic stereotypes),dependency(reduced autonomy), andexcessive simplification(loss of conceptual precision). To avoid them, it’s best to set clear constraints: keep key terms, indicate prerequisites, ask for contextualized examples, and always include a human review phase.
Practical strategies for multilingual pathways with AI (in class and online)
Below are some replicable activities in upper secondary schools, useful both in person and in digital environments. AI can drastically reduce preparation time, but the educational value comes from choosing the criteria: language level, subject objective, collaboration mode, and assessment tools.
- Multilingual subject glossaries: select 15–25 key terms from the unit (definition, example, collocations). Ask AI to propose controlled translations and usage sentences, then validate in class with matching activities and mini-quizzes.
- Leveled readings: start from an authentic text (textbook, article, historical document) and generate 2–3 versions with increasing complexity, keeping the same concepts. Include shared questions and differentiated questions, so everyone works on the same subject core.
- Linguistic scaffolding for complex tasks: have AI generate guided prompts, connectors, sentence starters, and paragraph models (describe an experiment, argue a thesis, comment on a graph). Gradually reduce the supports over time.
- Bilingual rubrics and explicit criteria: create rubrics with clear descriptors (content, method, language, collaboration). A bilingual version reduces ambiguity and increases the perception of fairness, especially in oral exams and project work.
- Multilingual formative feedback: prepare “comment banks” (strengths, next step, actionable suggestion) and ask AI to adapt them to the student’s language level and language, keeping a respectful, improvement-oriented tone.
Practical tip: to prevent translation from “flattening” concepts, always ask for two checks: (1) a list of subject-specific terms that must not be translated or must remain in standard form; (2) a brief fidelity check (“summarize in 5 points what must not be lost”).
How StudierAI can help teachers create inclusive and multilingual pathways
For many teachers, the real obstacle isn’t the idea of personalizing, but time.StudierAIcan support the design of inclusive and multilingual learning pathways with a workflow centered on goals, competencies, and alignment between activities and assessment. Some concrete use cases, particularly useful inupper secondary schools:
1) Differentiated materials without losing class unity: you can generate leveled handouts (basic/intermediate/advanced) on the same content, keeping concepts and keywords unchanged. This supports heterogeneous groups and personalized pathways, without creating two parallel “classes.”
2) Controlled translation and simplification: instead of a generic translation, you can request a rephrasing with constraints (length, language level, retention of technical terms, contextualized examples). It is particularly useful for newly arrived students or for those studying in Italian as an L2.
3) Coherent exercises and assessments: starting from objectives and prerequisites, you can create sets of graded exercises (comprehension, application, transfer) and tests with linguistic variants, while keeping the same assessment criteria. This also helps manage timing and catch-up work.
4) Progress monitoring: with recurring activities (quizzes, short writing, authentic tasks) you can collect evidence and turn it into observable indicators: growth in subject-specific vocabulary, improved textual cohesion, autonomy in problem solving. The goal isn’t to “measure everything,” but to make the pathway visible.
If you want to experiment quickly, you canstart for freewith a single unit: choose a topic, define 3 essential objectives, and create two language versions (standard Italian and simplified Italian, or Italian + a bridge language). Then check with a short authentic task and a shared rubric.
Best practices, privacy, and assessment: integrating AI responsibly
Integrating AI sustainably requires a shared operational framework within the department and, when possible, within the class council. Here are some practical guidelines, useful for protecting students and teaching quality.
- Privacy and data: avoid entering personal data or sensitive information in prompts. Use anonymized examples and, for student work, prefer excerpts without names. Keep only what is needed for assessment and clarify retention timelines.
- Transparency and learning agreement: make it explicit when AI is allowed (study, brainstorming, revision) and when it is not (in-class tests, individual production). Always ask students to declare AI use and to attach, if useful, a brief note on what was changed.
- Quality criteria: check subject-matter accuracy, linguistic clarity, cultural inclusivity, and the presence of non-stereotypical examples. If a text is “too perfect” but not very understandable, ask for a rephrasing with essential vocabulary and shorter sentences, without losing precision.
- Authentic assessments: prioritize tasks that require process (drafts, steps, justified choices), application to local cases, oral discussion, or lab work. AI can help with preparation, but assessment must observe real, transferable competencies.
To measure the impact on inclusion, define 3–5 simple indicators: oral participation of L2 students, completion of assignments, quality of arguments, use of subject-specific vocabulary, perceived self-efficacy (micro-questionnaires). Compare data before/after introducing leveled materials and bilingual rubrics.
If you are considering a more structured adoption, it may be useful to learn about the project’s approach and values: you can find more information on the pageabout us. For an operational trial with your class or your department, you can alsosign up for freeand start with a pilot unit: a few objectives, differentiated materials, an authentic assessment, and a shared rubric. It is often the most effective way to turn multilingualism from an organizational difficulty into a teaching resource.
