4) Support for students with SLD/SEN in access and study. Without replacing the measures and tools set out in PDP/PEI plans, it is possible to create text-based concept maps, essential glossaries, study checklists, short-answer self-check questions, and oral presentation outlines. Attention should be paid to the clarity of instructions and consistency with what has been agreed in the class council.inclusive teaching5) Assessments with clear criteria. AI can help build sets of equivalent questions (same difficulty, targeted skills) and formulate more readable instructions. A decisive step is to link each assessment to a rubric or explicit indicators, so as to reduce ambiguity and make grading more transparent.artificial intelligenceTo experiment in a controlled way on a module or a teaching unit, you canstart for freeand define from the outset: objectives, criteria, levels, and constraints (time, compensatory tools, submission method).upper secondary schoolsFair and accessible assessment: rubrics, formative feedback, and progress monitoringStudierAIInclusive assessment does not mean “assessing less,” but assessing better: clarifying what counts, offering equivalent tasks, and providing feedback that guides improvement. In 2026, with complex classes and tight timelines, the key is to make assessmentfair and accessiblethrough simple but rigorous tools.
Rubrics are particularly effective: they describe performance levels with observable indicators (content, method, language, argumentation, accuracy, originality). If shared before the assessment, they reduce anxiety, increase the perception of fairness, and help students understand where to intervene. From a personalization perspective, the rubric can remain the same, while the formats of the assessment change (guided oral, structured written, practical task), provided they are equivalent with respect to the targeted competence.
Formative feedback, then, works when it is timely and specific: not only “right/wrong,” but indications of typical errors, alternative strategies, examples of an effective response, and a realistic next step. AI can help generate drafts of comments or revision checklists, but it is the teacher who calibrates tone, priorities, and objectives, avoiding excessive or irrelevant feedback.differentiated pathwaysFinally, progress monitoring must be sustainable: a few pieces of evidence, collected regularly, and interpreted in light of specific needs. Tracking micro-goals (for example: correctness of steps, use of subject-specific vocabulary, autonomy in planning) makes it possible to highlight real improvements even when final performance is not yet “high.”
School-wide implementation: guidelines, privacy, training, and integration with planningflexible designTo adopt AI sustainably, a school-wide roadmap is needed that avoids isolated initiatives and ensures coherence with the PTOF, departments, and assessment criteria. An effective approach involves progressive steps, with controlled experimentation and documentation of choices.
In this framework, technology is useful when it reduces the gap between teaching intent and everyday practice: less time spent on rewrites and manual adaptations, more time on the educational relationship, observation, and feedback. This is where AI can contribute, provided it is integrated with professional criteria and not used as a shortcut.
Artificial intelligence and personalization: what it can do (and what it can’t) in the classroom
Used well,Module-based experimentation: start with a single teaching unit per subject, with success indicators (participation, accessibility, quality of student work, grading time).can supportAlignment with planning: link activities and assessments to competencies, core concepts, and shared rubrics, to maintain consistency across classes and sections.in at least three areas: content (explanations at different levels, alternative examples), exercises (graded variants, remediation and enrichment), and feedback (formative comments and study suggestions). In practice, it can help produce multiple versions of the same teaching “core,” keeping objectives and prerequisites consistent.
sign up for freeand test the creation of leveled materials on a topic you are already teaching.What it cannot do reliably, instead, is replace the teacher’s professional judgment: it does not know the class’s history, it does not observe dynamics and motivation, and it can produce plausible but incorrect answers. Moreover, there are structural limits to consider:
- Bias and stereotypes: models can reflect biases present in the data, with risks affecting examples, language, and expectations toward certain groups of students.
- Transparency: it is not always clear “why” an output was generated; for this reason, human criteria and checks are needed.
- Subject-matter reliability: in some subjects or on specific content, AI can oversimplify, confuse definitions, or “make up” references.
- Privacy and data: use in a school context requires attention to personal data, students’ work, and informed consent.
The operational rule is simple: AI can speed up the production of materials and suggest alternatives, butinstructional supervisionremains essential. Every output must be checked, contextualized, and “signed off” by the teacher, like any external resource.
StudierAI to facilitate inclusive teaching: practical use cases for upper secondary schools and universities
Tools likeStudierAIcan be useful when the goal is to turn a “single” lesson into a set of equivalent, accessible, and modular resources. Below are some typical use cases, designed forupper secondary schoolsand universities, with attention to SLD/SEN and level management.
1) Adapting materials into multiple versions. Starting from a subject text or the teacher’s notes, you can generate: a simplified version (controlled vocabulary, short sentences), a standard version, and an in-depth version. This makes it possible to keep the same topic but with a different cognitive load, promoting access without lowering essential objectives.
2) Alternative explanations and multiple channels. For the same concept (e.g., chemical equilibrium, derivative function, text analysis), AI can propose analogies, contextualized examples, micro-summaries, and guiding questions. The teacher chooses what is consistent with their methodology and with the class, building an access “menu.”
3) Leveled activities and targeted remediation. From an inclusive teaching perspective, it is often useful to prepare graded exercises: basic level (prerequisites), intermediate (application), advanced (complex problems or authentic tasks). With AI, you can speed up the generation of variants, keeping correction criteria and objectives unchanged.
4) Support for students with SLD/SEN in access and study. Without replacing the measures and tools set out in PDP/PEI plans, it is possible to create text-based concept maps, essential glossaries, study checklists, short-answer self-check questions, and oral presentation outlines. Attention should be paid to the clarity of instructions and consistency with what has been agreed in the class council.
5) Assessments with clear criteria. AI can help build sets of equivalent questions (same difficulty, targeted skills) and formulate more readable instructions. A decisive step is to link each assessment to a rubric or explicit indicators, so as to reduce ambiguity and make grading more transparent.
To experiment in a controlled way on a module or a teaching unit, you canstart for freeand define from the outset: objectives, criteria, levels, and constraints (time, compensatory tools, submission method).
Fair and accessible assessment: rubrics, formative feedback, and progress monitoring
Inclusive assessment does not mean “assessing less,” but assessing better: clarifying what counts, offering equivalent tasks, and providing feedback that guides improvement. In 2026, with complex classes and tight timelines, the key is to make assessmentfair and accessiblethrough simple but rigorous tools.
Rubrics are particularly effective: they describe performance levels with observable indicators (content, method, language, argumentation, accuracy, originality). If shared before the assessment, they reduce anxiety, increase the perception of fairness, and help students understand where to intervene. From a personalization perspective, the rubric can remain the same, while the formats of the assessment change (guided oral, structured written, practical task), provided they are equivalent with respect to the targeted competence.
Formative feedback, then, works when it is timely and specific: not only “right/wrong,” but indications of typical errors, alternative strategies, examples of an effective response, and a realistic next step. AI can help generate drafts of comments or revision checklists, but it is the teacher who calibrates tone, priorities, and objectives, avoiding excessive or irrelevant feedback.
Finally, progress monitoring must be sustainable: a few pieces of evidence, collected regularly, and interpreted in light of specific needs. Tracking micro-goals (for example: correctness of steps, use of subject-specific vocabulary, autonomy in planning) makes it possible to highlight real improvements even when final performance is not yet “high.”
School-wide implementation: guidelines, privacy, training, and integration with planning
To adopt AI sustainably, a school-wide roadmap is needed that avoids isolated initiatives and ensures coherence with the PTOF, departments, and assessment criteria. An effective approach involves progressive steps, with controlled experimentation and documentation of choices.
- Define a usage policy: teaching purposes, permitted activities, citation/transparency criteria, homework management, and prevention of improper uses.
- Privacy and data protection: minimize personal data, avoid entering sensitive information, clarify roles and responsibilities, and collect consent where necessary according to school procedures.
- Teacher training: focus on inclusive design, output verification, bias management, and building prompts oriented to objectives and criteria (not just to “answers”).
- Module-based experimentation: start with a single teaching unit per subject, with success indicators (participation, accessibility, quality of student work, grading time).
- Alignment with planning: link activities and assessments to competencies, core concepts, and shared rubrics, to maintain consistency across classes and sections.
One last practical suggestion: appoint a small working group (digital lead, inclusion coordinator, department representatives) to collect examples, rubric templates, and “good instructions.” If you want to quickly try a workflow and understand how to adapt it to your context, you can alsosign up for freeand test the creation of leveled materials on a topic you are already teaching.
In 2026, the combination of inclusive teaching and artificial intelligence can become a real advantage for teachers and students: more accessibility, more consistency, more opportunities for success. The condition is one: use AI as a planning and support tool, with shared criteria, ethical attention, and educational responsibility always at the center.
