Project Nº: 2024-1-PL01-KA220-SCH-000256498

Module 6 – Master of My Learning: Strategies and Self-Regulation

1. Pedagogical framework

1.1 Basic information

1.2 Pedagogical rationale and language justification

1.3 The mentor's role

1.4 Integration of AI (human-in-the-loop approach)

1.5 Core competences addressed

1.6 Core suggested tools

2. Activity scenarios

Scenario 1: My Safe Avatar — Focus: Identity

Scenario 2: Digital Rules — Focus: Safe Surfing

Scenario 3: Fact, Fiction, and Privacy — Focus: Focus: Critical Evaluation

3. Formative assessment strategy

3.1 Observation

3.2 Exit Questions and Self-Reflection

3.3 Peer Assessment

3.4 Mentor Reflection

4. Additional notes and contextual information

4.1 Cultural and Contextual Considerations

4.2 Connections to National Curriculum

4.3 References and Resources

1. MODULE OVERVIEW

1.1 Basic Information

Target Age Group

7–14 years (internally differentiated:

  • Scenario 1 targets 7–10 years
  • Scenarios 2 and 3 target 11–14 years)

Estimated Duration

3 sessions of approximately 45–60 minutes each (total: approx. 150–165 minutes)

1.2 Pedagogical Rationale and Language Justification

This module is grounded in the principle that long-term academic success for migrant pupils depends not only on what they learn, but on how they learn to learn. By supporting self-regulated learning (SRL), the module empowers pupils to plan their own tasks, monitor their progress, and reflect on their study habits –  competences aligned with DigCompEdu Area 3 – Teaching and learning, (3.4Self-regulated learning) and the European Key Competence of Learning to Learn.

 

Within the TELMS framework, the module is designed around the gradual release of responsibility: the mentor initially provides high-intensity guidance, which is progressively reduced as pupils gain confidence and independence. This inverted scaffolding model is particularly beneficial for newly arrived migrant pupils who may have experienced discontinuous schooling or different academic expectations in their countries of origin.

 

The module integrates Content and Language Integrated Learning (CLIL) principles by using the content of learning strategies and digital self-management as both the subject and the medium of language development. Pupils acquire vocabulary and structures related to planning, reflection, and argumentation while simultaneously developing metacognitive skills. Drawing on Coyle’s 4Cs Framework, the module addresses Content (learning strategies and self-regulation), Communication (academic and reflective language), Cognition (critical evaluation of AI-generated content and prompt construction), and Culture (understanding how learning norms and digital habits vary across contexts).

From a language acquisition perspective, the module deliberately bridges Basic Interpersonal Communicative Skills (BICS) and Cognitive Academic Language Proficiency (CALP). Scenario 1, designed for younger pupils (7-10 years), focuses on concrete, visual, and procedural vocabulary –  the language of doing and describing tasks (BICS). Scenarios 2 and 3, aimed at older pupils (11-14 years), introduce increasingly abstract academic language: the language of reasoning, evaluating, and formulating precise instructions (early CALP). Research on migrant learners consistently shows that BICS develops within one to two years, whereas CALP may require five to seven years. This progression is therefore essential for supporting pupils at different stages of their linguistic journey without overwhelming them cognitively.

The integration of digital tools throughout the module serves a dual purpose: it both develops pupils’ digital competences DigComp Area 2: Digital Resources; Areas 2.1-2.3 Selecting, Creating and modifying, Managing, protecting, sharing and reduces the cognitive load associated with traditional text-heavy instruction, allowing learners to demonstrate their capabilities through multimodal means.

1.3 The Mentor's Role

In this module, the mentor acts as a learning coach and strategic guide rather than a content expert. Operating within the TELMS framework as the ‘More Knowledgeable Other’ (MKO), the mentor’s primary responsibility is to make the processes of learning visible and accessible to pupils who may have never been explicitly taught how to organise their study, monitor their progress, or critically evaluate digital information.

The mentor’s specific functions across the three scenarios include:

  • Inverted Scaffolding: In Scenario 1, the mentor provides step-by-step procedural guidance to help younger pupils navigate ClassDojo and understand the concept of goal-tracking. This guidance is progressively reduced across Scenarios 2 and 3 as pupils develop independence in evaluating AI content and constructing their own prompts.
  • Language Facilitation: The mentor models the metalanguage of learning – phrases such as ‘Today I will…’, ‘I have completed…’, ‘I think this is wrong because…’,  giving pupils the linguistic tools to articulate their own learning processes in the host language.
  • Cognitive Load Management: By breaking tasks into clearly defined phases (Opening, Core, Closing) and providing structured templates and visual supports, the mentor ensures that the cognitive demands of new digital tools do not overwhelm the language demands of the activity.
  • Critical Thinking Facilitation: In Scenarios 2 and 3, the mentor leads guided discussions on AI accuracy, bias, and responsible use, cultivating pupils’ capacity to question digital sources without generating anxiety.
  • Socio-Emotional Support: The mentor establishes a psychologically safe environment in which mistakes, also the ones while using AI, are treated as learning opportunities, reducing performance anxiety for pupils who may already feel vulnerable in an unfamiliar school system.

1.4 Integration of AI (Human-in-the-Loop Approach)

Artificial intelligence is integrated into this module exclusively through a Human-in-the-Loop (HIL) approach. The mentor retains full pedagogical oversight at all times, and all pupil-facing AI interactions are structured, scaffolded, and critically framed.

Mentor’s AI Use

Before and during the sessions, the mentor uses AI tools to reduce preparation time and ensure materials are linguistically appropriate for each group:

  • Language Levelling (MagicSchool.ai, Diffit or ChatGPT): The mentor uses AI to generate simplified texts about learning strategies, self-regulation techniques, and study tips, adapting vocabulary to the pupils’ current BICS or early CALP proficiency level. For Scenario 2, the mentor uses AI to generate a short informational text on a historical or civic topic that contains deliberate factual errors and biases for pupils to critically evaluate.
  • Prompt Template Creation (ChatGPT): For Scenario 3, the mentor uses AI to prepare a bank of model prompt examples at varying levels of specificity, which serve as scaffolded references for pupils during the activity.
  • Goal and Progress Tracker Setup (ClassDojo): The mentor prepares the ClassDojo portfolio environment in advance, creating folders, skill categories, and point systems aligned with the module’s learning objectives.

Pupils’ AI Use

Pupils interact with AI tools in age-appropriate, carefully supervised ways:

  • Scenario 1 (Ages 7-10 – ClassDojo): Pupils interact with ClassDojo as a supervised portfolio tool, uploading photos of their completed work and tracking behavioural goals with mentor guidance. AI is present as the platform’s analytics and recommendation system, but pupils experience it through simple visual feedback (points and badges) rather than direct generation.
  • Scenario 2 (Ages 11-14 – ChatGPT with heavy supervision): Pupils act as ‘fact-checkers’, reading an AI-generated text prepared by the mentor and identifying factual errors, biased statements, and unsupported claims using the ‘Claim, Support, Question’ routine. This critical engagement teaches pupils that AI outputs require human verification.
  • Scenario 3 (Ages 11-14 – Safe conversational AI with heavy supervision): Pupils learn to write clear, specific prompts in the host language to generate practice quizzes or vocabulary lists for an upcoming test. The mentor supervises all interactions, reviews generated outputs with the class, and leads a reflection on the quality and accuracy of AI responses.

1.5 Core Competences Addressed

This module promotes the holistic development of key competences structured across three pillars:

Digital Competence (DigComp Framework)

  • DigCompEdu Area 3.4 – Self-regulated learning: Pupils develop habits of planning, goal-setting, and progress monitoring using ClassDojo.
  • DigComp Area 2 – Digital Resources – 2.1 Selecting, 2.2 Creating $ modifying, 2.3 Managing, protecting, sharing: Pupils practise formulating clear written prompts in the host language to generate study support materials using conversational AI.
  • DigComp Area 4 — Assessment, information, and digital content: Older pupils learn to identify factual inaccuracies, biases, and AI hallucinations in generated texts.

Language Competence (BICS / CALP)

  • BICS (Scenario 1): Procedural and concrete vocabulary related to school tasks, goal-setting, and self-description (e.g., ‘I have finished…’, ‘My best work is…’, ‘Today I will…’).
  • Early CALP (Scenarios 2 and 3): Academic language for argumentation and critical evaluation (e.g., ‘I believe this is inaccurate because…’, ‘The source does not support…’), and precise instructional language for prompt construction (e.g., ‘Create a quiz with five questions about…’).

Social, Personal, and 21st-Century Skills

  • Self-Regulation and Agency: Pupils develop an awareness of their own learning process and the language to describe and manage it.
  • Critical Thinking: By fact-checking AI-generated content, pupils build the capacity to question digital sources and apply evidence-based reasoning.
  • Communication and Collaboration: Pair and group activities across all three scenarios foster negotiation, turn-taking, and the respectful exchange of ideas in the target language.
  • Learning to Learn: The module explicitly teaches metacognitive strategies such as planning, monitoring, and reflecting thus supporting long-term academic integration.

1.6 Suggested Tools

Tool Name Purpose / Description of Use
ClassDojo Used in Scenario 1 as the primary portfolio and goal-tracking platform. Younger pupils upload photos of their best work and monitor points awarded for task completion and positive behaviours.
Seesaw Used as an alternative portfolio tool (Scenario 1) for schools that prefer a dedicated learning portfolio platform. Pupils upload digital artefacts and annotate them with voice recordings.
ChatGPT / Safe Conversational AI Used in Scenarios 2 and 3 (ages 11-14, with heavy mentor supervision). In Scenario 2, the mentor uses it to generate the fact-checking text. In Scenario 3, pupils use a safe, supervised AI interface to practice prompt engineering for study support.
MagicSchool.ai / Twee Used by the mentor before sessions to generate language-levelled texts, simplified instructions, and model prompt examples adapted to pupils’ current proficiency levels.
Kahoot! Used in the closing phase of Scenario 2 as a gamified formative assessment tool, reviewing key vocabulary related to critical thinking and AI literacy in a low-pressure, engaging format.
Google Slides / Canva Used by pupils in Scenario 3 to present their AI-generated study materials (quizzes, vocabulary lists) and reflect on the quality of the prompts they designed.

2. ACTIVITY SCENARIOS (STEP-BY-STEP IMPLEMENTATION)

This section contains the three activity scenarios that form the core of the module. Each scenario follows the TELMS pedagogical flow and must include an Opening Phase, a Core Learning Phase, and a Closing Phase with formative assessment. Scenarios are thematically connected and build upon each other in terms of complexity and language demand, progressing from concrete self-regulation skills (Scenario 1) to critical AI evaluation (Scenario 2) and independent AI-assisted study planning (Scenario 3).

Scenario 1: Building My Learning Portfolio - Focus: self-regulation and goal tracking

Target Age Group

7–10 years (adaptable to 11-14 years with greater autonomy in goal-setting and written reflection)

Estimated Duration

45 minutes

Scenario Summary

Pupils are introduced to the concept of self-regulated learning through a visual, step-by-step journey into managing their own schoolwork. Using ClassDojo as a digital portfolio and goal-tracking platform, they learn to upload photos of their best completed work, choose personal learning goals from a simple illustrated menu, and track points awarded for positive behaviours and task completion. The activity is deliberately concrete and multimodal, reducing cognitive load for newly arrived pupils while building foundational habits of planning and self-monitoring.

Scenario 1 - Learning Objectives

Linguistic Objectives (BICS/CALP)

  • To follow step-by-step instructions for a digital task, using procedural vocabulary in the host language (e.g., ‘First… then… finally…’). 
  • To produce simple, structured self-descriptions of completed work using scaffolded frames such as ‘This is my best work because…’ and ‘Today I will…’ (BICS). 
  • To recognise and use key vocabulary related to school tasks, goals, and achievements.

Digital Objectives

  • To navigate a classroom management and portfolio platform (ClassDojo) to upload digital artefacts and track personal progress indicators. 
  • To develop foundational digital self-regulation habits (DigCompEdu Area 3.4), including goal-setting and progress monitoring within a supervised digital environment. 
  • To interact with basic platform features (photo upload, points dashboard, goal selector) using simple host language commands.

Personal/Social Objectives

  • To develop self-awareness and a sense of agency regarding their own learning by identifying and celebrating personal achievements. 
  • To build confidence and emotional safety by sharing work in a low-pressure, supportive digital space. 
  • To establish a sense of belonging within the classroom community through the shared ritual of portfolio building.

Scenario 1 - Digital Tools and Materials

Tool Name Purpose / Notes on Use
ClassDojo Primary platform for portfolio building and goal tracking. Pupils upload photos of their work and monitor their personal points and goals.
Seesaw (alternative) Alternative portfolio platform for schools where ClassDojo is not available. Pupils can record voice annotations alongside uploaded photos.
Interactive whiteboard / projector Used by the mentor to demonstrate step-by-step navigation of ClassDojo, modelling each action before pupils replicate it on their devices.
Bilingual illustrated goal cards (printable) Pre-prepared cards with key vocabulary in the host language and pupils’ mother tongues, paired with visual icons representing learning goals (e.g., ‘I listen carefully’, ‘I finish my tasks’).
MagicSchool.ai / Twee (mentor preparation) Used before the session by the mentor to generate a simplified, illustrated step-by-step guide to ClassDojo in language-levelled host language vocabulary.

Scenario 1 - Step-by-Step Implementation

Opening Phase (Warm-up) [10 minutes]

The mentor opens the session by projecting a simple visual question on the whiteboard: ‘What does a good student do?’ Pupils respond by pointing to images on a pre-prepared illustrated board showing actions such as ‘listens’, ‘tries hard’, ‘finishes work’, ‘asks for help’ ‘raises his/her hand’. The mentor introduces key BICS vocabulary using the bilingual illustrated goal cards, modelling pronunciation and encouraging repetition. 

Using Total Physical Response (TPR), the mentor acts out each behaviour (e.g., pretending to write, listen, raise a hand) while saying the phrase in the host language. The session goal is clearly stated: ‘Today we are going to build your personal Learning Portfolio – a special digital folder for your best work.’

Core Learning Phase [25–35 minutes]

  • The mentor shares their screen to demonstrate ClassDojo step by step, narrating each action in clear, simple host language: ‘First, we click here… then we take a photo… then we upload it.’ Pupils follow along on their devices or tablets. 
  • Sub-activity A – Uploading Best Work: Each pupil selects one piece of completed work they are proud of, photographs it using their device, and uploads it to their personal ClassDojo portfolio folder. The mentor circulates to provide ‘soft scaffolding’ — technical assistance and language prompts. Pupils add a short written or voice caption using the frame: ‘This is my [subject]. I am proud because…’ Lower-proficiency pupils may point to a printed word bank to complete the sentence. 
  • Sub-activity B – Setting a Learning Goal: Pupils choose one goal from the illustrated goal menu (e.g., ‘I will finish all my tasks’, ‘I will ask when I do not understand’) and add it to their ClassDojo profile. The mentor models the language: ‘My goal for this week is… because…’ AI note: ClassDojo’s points and recommendation system operates in the background. The mentor explains in simple terms: ‘The app helps us remember what we are working on – like a helpful notebook.’

Closing Phase & Formative Assessment [10 minutes]

  • Pupils gather in a circle.
  • Each pupil briefly shows their uploaded work on their screen and says one sentence about it using the frame: ‘This is my… I am proud because…’ The mentor provides immediate, positive verbal feedback. 
  • Exit reflection: the mentor asks, ‘Name one thing you did well today and one thing you want to work on next time.’ Pupils can respond verbally, point to an image on the goal cards, or write a one-word answer on a sticky note. The mentor informally observes which pupils could navigate the platform independently and which required additional support, noting this for future scaffolding decisions.

Scenario 1 - Teacher Notes

Adaptation Notes

1. Age Differentiation: 

  • For ages 7–10: Keep the activity highly visual and physical. Use TPR during the warm-up, allow non-verbal responses (pointing, drawing), and ensure all on-screen text is supported by icons. Limit written captions to one sentence, and accept voice recordings as an equivalent alternative. 
  • For ages 11–14: Increase the metacognitive demand by asking older pupils to write a short paragraph explaining their goal (2–3 sentences) and to review a peer’s portfolio post and offer one written compliment. Prompt them to consider: ‘What strategy will you use to achieve your goal?’  

2. Scaffolding for Language Proficiency (BICS) 

  • Lower Proficiency: Provide printed bilingual sentence frames and goal cards. Allow pupils to caption their work in their mother tongue first, then add the host language equivalent with mentor support. Avoid requiring spontaneous oral production without a frame. 
  • Higher Proficiency: Encourage pupils to write their caption without using the frame, and to explain their choice of goal to a peer using conversational English.  

3. Inclusion and Tech Limitations

  • If devices are limited, pupils can complete the ‘My Best Work’ card on paper using the printable template, and one device can be shared between pairs for uploading. For pupils with SEN, pre-select two or three goal options rather than presenting the full menu, and provide a printed step-by-step visual guide to each action in ClassDojo.  

4. Socio-Emotional Support

  • Emphasise that the portfolio belongs to the pupil – ‘This is yours. Nobody will judge it.’ Avoid any competitive framing around points. The goal-setting activity should feel exciting and personal, not evaluative.

Reflection Routine

At the end of the session, the mentor guides pupils through the ‘My Learning Star’ reflection routine — a simple, visual, and age-appropriate tool designed to build the habit of self-monitoring without requiring abstract metacognitive language.

The mentor draws a large star on the whiteboard and models the routine aloud: “Today I did… upload my best work to my portfolio. Next time I will try to… write a longer sentence about it.” Pupils then receive a printed star template (or draw their own) and complete two branches:

  • Branch 1: “Today I did…” – pupils write or draw one thing they completed or tried during the session. Lower-proficiency pupils may respond with a single word, a drawing, or a symbol.
  • Branch 2: “Next time I will try to…” – pupils identify one small, concrete improvement for the following session. The mentor circulates and offers sentence starters: “Next time I will try to… finish faster / ask for help / write more words.”

Completed stars are collected by the mentor as a brief formative record of task completion and emerging self-regulation awareness. They may also be photographed and uploaded to the pupil’s ClassDojo portfolio as the first entry in their personal learning journey.

Scenario 1 - Expected Outcomes

  • Digital Competence: Pupils will successfully navigate ClassDojo to upload at least one piece of work and set one personal learning goal, demonstrating basic platform literacy and foundational self-regulation habits. 
  • Linguistic Competence: Pupils will produce at least one structured sentence in the host language using the scaffolded frame ‘This is my… I am proud because…’ or equivalent, demonstrating emerging BICS in the context of personal reflection. 
  • Personal/Social Competence: Pupils will demonstrate a beginning sense of agency and ownership over their own learning by identifying and verbalising one personal achievement and one area for growth.

Scenario 1 - Ideas for Worksheets

Worksheet Idea 1 - My Learning Portfolio Cover

Tool

ClassDojo / Canva (printable version)

Activity Description

Pupils complete a digital or printable ‘My Learning Portfolio Cover’ – a personalised title page for their portfolio folder. Using ClassDojo’s customisation options or a Canva template, they add their avatar name (not their real name), their favourite subject icon, and one goal illustrated with a drawing or chosen image. For lower-tech settings, the template is printed and decorated by hand.

Content

The cover page includes: a space for the pupil’s avatar/nickname; a ‘My favourite subject’ icon selector (illustrated grid of six subjects); a ‘My goal this week is…’ sentence frame with an illustrated goal menu; and an open drawing space for the pupil to illustrate themselves as a learner.

Language Focus

Vocabulary of school subjects, personal goals, and self-description. Sentence frame: ‘My goal this week is…’ Adjectives for self-description (e.g., ‘I am a hard worker / curious / careful learner’).

Worksheet Idea 2 - This helps me learn

Tool

Wizer.me / Liveworksheets (printable alternative)

Activity Description

Pupils complete a digital drag-and-drop sorting activity in which they categorise a set of behaviours and habits into two columns: ‘This helps me learn’ and ‘This doesn’t help me learn.’ Each item is illustrated with a simple icon and accompanied by an audio button (in the digital version) for pronunciation support. The activity can be completed individually or in pairs.

Content

Draggable items include: ‘I ask questions when I do not understand’ (helps), ‘I look at my phone during class’ (harder), ‘I write down new words’ (helps), ‘I give up when it is difficult’ (harder), ‘I check my work before finishing’ (helps), ‘I copy without reading’ (harder). Each item is paired with a visual icon. The answer key is embedded in the digital version for self-correction.

Language Focus

Vocabulary of learning behaviours and metacognitive habits (BICS). Sentence starters for self-reflection: ‘I think this helps me because…’ (for early CALP extension). Imperative structures: ‘Ask questions’, ‘Check your work’.

Worksheet Idea 3 - My Learning Week Planner

Tool

Google Slides / printable handout

Activity Description

Pupils complete a ‘My Learning Week Planner’ – a simple visual weekly planning grid. The mentor models how to fill in one column together, then pupils complete the rest individually. They write or draw one task per day, choose a ‘mood icon’ to show how they feel about it, and add a star when they complete it. This serves as an introductory self-monitoring tool that can be used across the course of the module.

Content

A five-column grid (Monday to Friday) with rows for: ‘What I will do today’ (with illustrated task icons to choose from); ‘How I feel about it’ (emoji selector: confident, nervous, excited, unsure); and ‘Did I do it?’ (star/tick box). The planner uses colour coding to distinguish between subject areas.

Language Focus

Vocabulary of days of the week, school subjects, and feeling words (BICS). Sentence frame: ‘Today I will…’ and ‘I feel… about this because…’ (bridge to CALP). Numbers for task counting and sequencing vocabulary: ‘first’, ‘then’, ‘finally’.

 

Scenario 2: Fact-Check the AI Bot - Focus: Critical Thinking and AI Literacy

Target Age Group

11–14 years (adaptable to advanced learners aged 10 with additional scaffolding)

Estimated Duration

60 minutes

Scenario Summary

The mentor provides the class with a short text generated by an AI chatbot (such as ChatGPT) on a historical or civic topic – for example, a brief account of a significant event or a description of a social institution. The text contains deliberate factual errors, biased phrasings, and unsupported generalisations embedded within otherwise plausible content. Working in small groups and then as a whole class, pupils act as ‘critical detectives’, applying the ‘Claim, Support, Question’ thinking routine to systematically identify what the AI stated, what evidence supports or contradicts each claim, and what questions remain unanswered. The scenario teaches pupils to approach AI-generated content with analytical rigour rather than passive acceptance.

Scenario 2 - Learning Objectives

Linguistic Objectives (BICS/CALP)

  • To identify and distinguish between factual statements, opinions, and biased claims in a written text (early CALP). 
  • To use academic language for argumentation and critical evaluation, including structures such as ‘I believe this is inaccurate because…’, ‘The text states that… however, I found that…’, and ‘This is an unsupported claim because…’ 
  • To develop reading comprehension strategies (scanning for key claims, skimming for general tone) in an academic context.

Digital Objectives

  • To critically evaluate the credibility and accuracy of AI-generated digital content (DigComp Areas 2.3, 4.2). 
  • To apply a structured thinking routine (‘Claim, Support, Question’) to identify factual errors, AI hallucinations, biases, and logical gaps in a text. 
  • To understand the concept of AI limitations, including the tendency of language models to produce plausible-sounding but factually incorrect information.

Personal/Social Objectives

  • To develop intellectual confidence and the capacity to question digital authority, understanding that technology requires human oversight. 
  • To collaborate effectively in small groups, respecting different interpretations and building on each other’s reasoning. 
  • To build resilience against misinformation by practising verification skills in a safe, structured classroom context.

Scenario 2 - Digital Tools and Materials

Tool Name Purpose / Description of Use
ChatGPT (mentor use for preparation) Used by the mentor before the session to generate the fact-checking text. The mentor reviews, verifies, and deliberately embeds specific errors and biases to create the activity material.
Printed or digital copies of the AI-generated text One copy per pupil or pair. The text should be 150–250 words and include 3–5 identifiable errors or biased statements, colour-coded for easier identification in lower-proficiency adaptations.
‘Claim, Support, Question’ structured worksheet A three-column graphic organiser guiding pupils to record: the specific claim made in the text; the evidence (from prior knowledge or provided reference sources) that supports or refutes it; and the questions this raises.
Kahoot! (closing phase) Used to deliver a fast-paced gamified review quiz of key vocabulary and concepts (e.g., ‘What is a hallucination in AI?’, ‘What does ‘biased’ mean?’) as a fun, low-pressure exit assessment.
MagicSchool.ai / Twee (mentor preparation) Used by the mentor to generate language-levelled sentence starters and academic vocabulary lists for pupils who need additional linguistic scaffolding during the analysis activity.

Scenario 2 - Step-by-Step Implementation

Opening Phase (Warm-up) [10 minutes]

  • The mentor begins by projecting a single, provocative question: ‘Can a computer lie?’ Pupils discuss briefly in pairs (30 seconds), then share ideas. 
  • The mentor introduces the concept of AI hallucination and bias using one vivid, relatable example: ‘Imagine asking a very confident friend for directions, but they have never been to that place. They sound sure, but they might be completely wrong.’ This analogy reduces the abstract nature of the concept. 
  • The mentor introduces the three-column ‘Claim, Support, Question’ organiser, modelling its use with one sample sentence from a different (non-activity) text. Key academic vocabulary is written on the board: claim, evidence, bias, hallucination, verify, source. Pupils copy or photograph the vocabulary before beginning.

Core Learning Phase [25–35 minutes]

  • Sub-activity A – Individual reading (8 minutes): Pupils read the text silently and use two highlighter colours: one for statements they believe are correct, one for statements they find suspicious. For lower-proficiency pupils, the text may have 2-3 sentences pre-flagged with a question mark to reduce the initial barrier. 
  • Sub-activity B – Group analysis (15 minutes): In groups of 3-4, pupils complete the ‘Claim, Support, Question’ worksheet together. The mentor circulates, prompting groups with scaffolded questions: ‘What makes you think this might be wrong?’, ‘Where could you check this?’, ‘Is this a fact or an opinion?’ The mentor explicitly models academic language: ‘I would say: I believe this claim is inaccurate because the text says… but I know that…’ 
  • Sub-activity C – Whole-class share (7 minutes): Each group nominates a spokesperson to share their most important finding. The mentor records key errors on the board, inviting the class to verify them collectively.

Closing Phase & Formative Assessment [10 minutes]

The mentor launches a Kahoot! quiz (8–10 questions) reviewing the key concepts from the session: definitions of bias, hallucination, and claim; true/false questions about AI capabilities; and scenario-based questions (‘An AI tells you the capital of Australia is Sydney. Is this reliable? What do you do?’). After the quiz, the mentor poses the exit question: ‘In one sentence, explain why you should never use AI as your only source of information.’ Pupils write their response on an exit card or into their digital portfolio (ClassDojo or Seesaw). The mentor collects exit cards to inform differentiation in the following session.

Scenario 2 - Teacher Notes

Adaptation Notes

1. Age Differentiation

  • For ages 11–12: Select a text on a concrete, familiar topic (e.g., a famous historical figure or a well-known sporting event) so that pupils can draw on prior knowledge when evaluating claims. Limit the number of errors to three and colour-code the suspicious sentences in the text. Provide a structured sentence bank for the worksheet. 
  • For ages 13–14: Choose a more complex civic or scientific topic (e.g., climate change data or a historical policy). Increase the number of embedded errors to five and include at least one subtle bias (e.g., a value-laden word choice rather than a factual error). Expect pupils to formulate their own questions without a frame.  

2. Scaffolding for Language Proficiency

  • Lower Proficiency: Provide a bilingual glossary of the key analytical vocabulary (claim, evidence, bias, hallucinate, verify). Allow pupils to complete the worksheet in their mother tongue and then translate key phrases into the host language with mentor support. 
  • Higher Proficiency: Challenge pupils to write a one-paragraph summary of the three most significant errors they found, using the academic sentence starters from the Methodological Guidebook’s Solo-Duo-Group Exchange activity.  

3. Inclusion and SEN

  • For pupils with reading difficulties, provide an audio version of the text (recorded by the mentor or generated using ElevenLabs). 
  • For pupils with very limited language proficiency, pair them with a more proficient peer and assign the role of ‘evidence recorder’ (writing what the partner identifies), reducing the reading demand while maintaining participation.  

4. Socio-Emotional Considerations

  • Frame the activity around curiosity rather than judgment: ‘We are detectives, not critics.’ Emphasise that adults and experts are also sometimes misled by AI – this is a universal skill, not a test of intelligence. Avoid singling out pupils who struggle to identify errors.

 

Reflection Routine

At the close of the activity, the mentor guides pupils through the ‘Fact-Checker’s Pledge’ reflection: pupils complete the sentence ‘From now on, when I use AI to study, I will always…’ This is written on a card and added to their digital portfolio. The reflection bridges from the critical evaluation skill practised in this scenario to the practical AI study use introduced in Scenario 3, giving pupils a concrete personal commitment to carry forward.

Scenario 2 - Expected Outcomes

  • Digital Competence: Pupils will successfully identify at least two factual errors or biased claims in the AI-generated text, demonstrating practical application of DigComp Area 1.2 (Evaluating data, information, and digital content). 
  • Linguistic Competence: Pupils will produce at least two written analytical statements in the host language using academic sentence frames (e.g., ‘I believe this is inaccurate because…’), demonstrating emerging CALP in the context of critical argumentation. 
  • Personal/Social Competence: Pupils will demonstrate intellectual confidence by respectfully challenging the credibility of a digital source and will collaborate constructively in their group to pool evidence and reach a shared conclusion.

Scenario 2 - Ideas for Worksheets

Worksheet Idea 1 - The AI Claims That...

Tool

Google Slides / printable handout

Activity Description

Pupils complete the three-column ‘Claim, Support, Question’ graphic organiser as the core analytical worksheet for Scenario 2. The organiser has three pre-populated rows (one per identified claim) and is structured to scaffold the movement from simple identification to evidence-based argumentation. In the digital version, pupils can type responses and share their slides with the mentor for real-time monitoring.

Content

Three columns: ‘The AI Claims That…’ (pupils quote or paraphrase the specific statement from the text); ‘Evidence That Supports or Contradicts This’ (pupils write what they know or what a quick search confirms); ‘My Question About This’ (pupils write what they still want to find out). Each row targets a different claim from the AI-generated text. The bottom of the worksheet includes a reflection box: ‘The most important thing I found was… because…’

Language Focus

Academic reading comprehension and critical analysis vocabulary (CALP): claim, evidence, contradict, support, verify, accurate, inaccurate, biased, unsupported. Sentence frames for argumentation: ‘The text states that… however, I believe…’, ‘This is unsupported because…’, ‘I would like to know more about…’

Worksheet Idea 2 - True, False, or Can't Tell?

Tool

Wizer.me / Liveworksheets

Activity Description

Pupils complete a digital ‘True, False, or Can’t Tell?’ activity in which they evaluate eight short statements taken from a variety of simulated AI-generated sources (news summaries, quiz answers, historical descriptions). For each statement, they select a verdict and write or dictate a one-sentence justification. The activity reinforces the critical evaluation vocabulary introduced in the main scenario and extends the skill beyond the single text used in the lesson.

Content

Eight short statements on varied topics (not the same text as the main activity) covering history, science, and current events. Each statement is labelled with a source tag (e.g., ‘AI summary’, ‘AI quiz answer’, ‘AI-generated biography’). The digital version includes an embedded hint system where pupils can click to reveal a scaffolded question (‘What do you know about this topic already?’) if they are unsure.

Language Focus

Vocabulary of critical evaluation: true, false, uncertain, verify, evidence, source (CALP). Justification sentence frame: ‘I think this is [true/false/can’t tell] because…’ Discourse connectors: ‘however’, ‘on the other hand’, ‘this suggests that’.

Worksheet Idea 3 - AI Safety Poster

Tool

Canva / Google Slides (printable poster alternative)

Activity Description

Pupils work in pairs to design a simple ‘AI Safety Poster’ for younger pupils (imagining it will be displayed in a primary school classroom). The poster must include: one key rule for using AI safely; one example of an AI error or hallucination; and one tip for how to check if AI information is correct. This production task consolidates the lesson’s concepts through a communicative, audience-aware output.

Content

Pupils choose from a set of illustrated rule templates and error examples provided on a shared Canva board or Google Slides deck. They select, adapt, or write their own content for each section. The poster format is simple and highly visual, with icons available for non-verbal support. The poster can be printed and displayed in the classroom or shared digitally with the group.

Language Focus

Imperative structures for safety rules: ‘Always check…’, ‘Never use AI as your only source…’ (BICS/early CALP). Vocabulary of AI literacy: hallucination, verify, source, accurate, reliable. Simple explanatory writing: ‘An example of an AI error is…’ (CALP bridge).

Scenario 3: Prompt Engineering for Study Success - Focus: Self-Regulated Learning and AI-Assisted Study

Target Age Group

11–14 years (adaptable to advanced learners aged 10 with simplified prompt templates)

Estimated Duration

45 minutes

Scenario Summary

Building directly on the critical awareness of AI developed in Scenario 2, pupils now learn to use AI as a constructive study tool under careful supervision. The mentor introduces the concept of prompt engineering –  the skill of writing clear, specific, and unambiguous instructions to an AI chatbot in order to generate useful study materials such as practice quizzes, vocabulary lists, or topic summaries. Pupils practise moving from vague to precise prompts, evaluate the quality of the AI’s responses, and reflect on how this skill connects to their broader habits of self-directed study. The scenario closes with each pupil creating one personalised AI-generated revision resource, which is added to their portfolio.

Scenario 3 - Learning Objectives

Linguistic Objectives (BICS/CALP)

  • To formulate clear, unambiguous, and specific instructions and queries in the host language (CALP). 
  • To use precise academic vocabulary related to their current school subjects when constructing prompts (e.g., ‘Create five multiple-choice questions about the water cycle at B1 level’). 
  • To evaluate the quality and accuracy of AI-generated texts using the critical lens developed in Scenario 2, applying vocabulary such as ‘accurate’, ‘incomplete’, ‘biased’, or ‘unsupported’.

Digital Objectives

To understand and apply the concept of prompt engineering to generate useful, personalised study materials using a supervised conversational AI tool (DigComp Area 2.2: Creating & Modifying; DigCompEdu Area 3.4: Self-regulated learning). To evaluate the quality and relevance of AI-generated outputs and to identify when a response requires correction or refinement. To develop digital self-regulation habits by integrating AI-assisted study tools into a broader personal learning strategy.

Personal/Social Objectives

  • To develop intellectual agency and self-directed learning habits by taking active control of their own study preparation. 
  • To build confidence in the host language by using it as the medium for communicating with an AI tool in a goal-oriented, purposeful context. 
  • To reflect critically on the role of AI in their own learning and to make informed, responsible choices about when and how to use it.

Scenario 3 - Digital Tools and Materials

Tool Name Purpose / Description of Use
Safe Conversational AI / ChatGPT (with heavy mentor supervision) Used by pupils under direct mentor supervision to practise writing prompts and evaluate the AI-generated study materials produced. The mentor monitors all interactions in real time.
Prompt Engineering Practice Cards (printable) Pre-prepared cards showing a progression from vague to specific prompts, used during the warm-up to teach the principle of precision. Prepared by the mentor using MagicSchool.ai / Twee
MagicSchool.ai / Twee (mentor preparation) Used by the mentor before the session to generate a bank of model prompt examples at beginner, intermediate, and advanced levels, and to prepare language-levelled explanations of prompt engineering concepts.
Google Slides / Canva Used by pupils to present their final AI-generated study resource to the group during the closing phase, annotated with a brief evaluation of its quality and accuracy.
ClassDojo / Seesaw Used to upload the final AI-generated study resource to each pupil’s personal portfolio, connecting this scenario’s output to the goal-tracking begun in Scenario 1.

Scenario 3 - Step-by-Step Implementation

Opening Phase (Warm-up) [10 minutes]

  • The mentor begins with the ‘Vague vs. Precise’ warm-up game. They project two versions of a request on the board: Version A – ‘Tell me about history.’ Version B – ‘Create five multiple-choice quiz questions about the causes of the First World War for a 13-year-old student learning English as a second language.’ 
  • The mentor asks: ‘Which instruction would give you a more useful answer? Why?’ Pupils discuss in pairs for two minutes. 
  • The mentor introduces the term prompt engineering and writes a simple definition: ‘A prompt is an instruction you give to an AI. A good prompt is clear, specific, and tells the AI exactly what you need.’ 
  • The key vocabulary for the session is introduced: prompt, specific, vague, generate, evaluate, revise, accurate. Pupils receive the Prompt Engineering Practice Cards and the mentor models how a vague prompt can be progressively improved.

Core Learning Phase [25–35 minutes]

  • Sub-activity A – Prompt Practice (10 minutes): In pairs, pupils practise transforming three vague prompts into precise ones using the improvement framework: Who is it for? What subject? How many items? What difficulty level? What format? The mentor circulates and provides language feedback, modelling academic vocabulary. Pairs compare their improved prompts with another pair and choose the best version. 
  • Sub-activity B – Generating a Study Resource (15 minutes): Under direct mentor supervision, each pupil or pair accesses the supervised AI tool and submits their best prompt to generate one of the following: a five-question quiz on a current school subject; a vocabulary list of ten key terms with definitions; or a three-paragraph summary of a topic they are studying. The mentor monitors all interactions and reviews AI responses with the class before pupils finalise their resource. Pupils use the ‘Claim, Support, Question’ lens from Scenario 2 to quickly evaluate their AI output: Is it accurate? Is anything missing or wrong? Does it match what I asked for? 
  • Sub-activity C – Annotation and Reflection (5 minutes): Pupils annotate their AI-generated resource on Google Slides or Canva, marking any errors they found and writing one sentence explaining whether they would use this for studying and why.

Closing Phase & Formative Assessment [10 minutes]

  • Pupils briefly present their AI-generated study resources to the group (1–2 minutes each). They share: what they asked for; whether the AI understood their prompt; and one thing they would change about the output. 
  • The mentor facilitates a group reflection: ‘What makes a prompt powerful? When is AI helpful for studying? When is it not?’ 
  • Final exit question: ‘Write one sentence: When I use AI for studying, I will always…’ Pupils add their final annotated study resource and exit sentence to their ClassDojo or Seesaw portfolio, completing the learning journey that began in Scenario 1.

Scenario 3 - Teacher Notes

Adaptation Notes

1. Age Differentiation: 

  • For ages 11–12: Provide a prompt template with four fixed slots to fill in (subject, format, number of items, difficulty), reducing the open-ended challenge of constructing a prompt from scratch. Limit AI interaction to generating a vocabulary list (the simplest output format). 
  • For ages 13–14: Remove the template and challenge pupils to write their own prompt entirely. After generating their resource, ask them to critically rewrite any inaccurate section and annotate it with an explanation. They should also consider the ethical question: ‘If I submit AI-generated notes to my teacher, is that honest?’  

2. Scaffolding for Language Proficiency

  • Lower Proficiency: Allow pupils to construct their prompt in their mother tongue first and then translate it into the host language with mentor support. Alternatively, the mentor can write the prompt in the host language while the pupil dictates the content in simpler terms. 
  • Higher Proficiency: Challenge pupils to write a two-sentence evaluation of their AI output using academic discourse: ‘The AI-generated resource was useful because… However, I noted that…’  

3. Inclusion, SEN, and Tech Limitations: 

  • If internet access is unreliable, the mentor can pre-generate three or four AI responses before the session, and pupils evaluate these using the ‘Claim, Support, Question’ framework rather than generating their own. 
  • For pupils with SEN, pre-populate one slot of the prompt template and ask the pupil to complete only the remaining fields.  

4. Ethical Framing 

  • This scenario must include an explicit discussion of the ethical boundaries of AI use in academic contexts. The mentor should address: ‘When is it helpful to use AI in your studies?’ (generating practice materials, checking vocabulary, summarising notes) versus ‘When is it not appropriate?’ (submitting AI-generated work as your own, using AI to avoid thinking). This framing protects pupils’ academic integrity while empowering them as responsible digital learners.

 

Reflection Routine

The mentor closes the module with the ‘My Learning Commitments’ routine. Pupils complete a three-part statement: ‘1. I have learned that AI can help me by… 2. I have learned that AI cannot replace… 3. My personal study commitment is…’ This three-part reflection is written onto a card and added to the portfolio, alongside the Fact-Checker’s Pledge from Scenario 2, creating a visible record of pupils’ developing relationship with AI as a learning tool. The routine also consolidates the module’s overarching theme: that effective self-regulated learners make deliberate, critical choices about the tools they use.

Scenario 3 - Expected Outcomes

  • Digital Competence: Pupils will successfully construct at least one precise, effective prompt in the host language and use it to generate a relevant study resource, demonstrating applied understanding of DigComp Area 2 (Digital resource) and DigCompEdu Area 3.4 (self-regulated learning). 
  • Linguistic Competence: Pupils will produce a complex instruction sentence in the host language demonstrating precision, specificity, and appropriate academic register (early CALP), as well as a written evaluative statement about the quality of the AI output. 
  • Personal/Social Competence: Pupils will demonstrate intellectual agency and self-regulated learning by making an informed, critically reasoned decision about when and how to use AI as a study tool, and by articulating this decision in their portfolio reflection.

Scenario 3 - Ideas for Worksheets

Worksheet Idea 1 - Prompt Improvement Workshop

Tool

Printable handout / Google Slides

Activity Description

Pupils complete a ‘Prompt Improvement Workshop’ worksheet in which they are presented with five progressively more complex vague prompts and must rewrite each one using the precision framework introduced in the warm-up (Who? What subject? How many? What level? What format?). The worksheet is structured as a paired activity: Pupil A rewrites prompts 1, 3, and 5; Pupil B rewrites prompts 2 and 4. They then compare and decide on the best version together.

Content

Five vague prompts across different subjects and difficulty levels: e.g., ‘Tell me about animals’ (science, beginner); ‘Explain history’ (history, intermediate); ‘Help me with maths’ (maths, advanced). For each, pupils fill in a precision checklist and write the improved prompt. A model improved prompt is provided for the first item as a worked example.

Language Focus

Vocabulary of instruction and precision (CALP): specific, vague, generate, format, difficulty, relevant, appropriate. Sentence frame for improved prompts: ‘Create [number] [format] about [topic] for a [age/level] student learning [language].’ Evaluation language: ‘This prompt is better because…’

Worksheet Idea 2 - Rate My Prompt

Tool

Wizer.me / Liveworksheets

Activity Description

Pupils complete a ‘Rate My Prompt’ digital matching activity in which they match eight prompts to their quality rating (Excellent, Good, Needs Improvement, Too Vague) and drag a specific reason from a provided list to justify each rating. The activity reinforces the distinction between effective and ineffective prompts and builds the evaluative vocabulary needed for the final portfolio annotation. Automatic correction in Wizer.me provides immediate formative feedback.

Content

Eight prompt examples covering a range of school subjects and quality levels. For each, pupils select a rating from a drop-down menu and drag a reason tag (e.g., ‘Too short’, ‘No subject specified’, ‘Clear and specific’, ‘Level not stated’). A reflection box at the end asks: ‘What is the most important element of a good prompt? Write one sentence.’

Language Focus

Vocabulary of quality evaluation (CALP): accurate, specific, vague, complete, incomplete, relevant, useful. Comparative language: ‘This prompt is better than… because…’ Metalinguistic awareness: understanding how word choice and specificity affect communication outcomes.

Worksheet Idea 3 - My AI Study Toolkit

Tool

ClassDojo / Seesaw

Activity Description

Pupils complete their ‘My AI Study Toolkit’ portfolio entry – a structured self-assessment and commitment record that consolidates all three scenarios of the module. The entry includes: a photo or screenshot of their AI-generated study resource from this session; a three-sentence evaluation (‘The resource was helpful because… I corrected… I would change…’); their ‘Fact-Checker’s Pledge’ from Scenario 2; and their ‘My Learning Commitments’ statement from Scenario 3. This final portfolio entry provides the mentor with a rich, multimodal record of each pupil’s self-regulation development across the module.

Content

The portfolio entry is structured as a four-section digital page: 

  • Section 1 – My Best Study Resource (uploaded screenshot); 
  • Section 2 – My Evaluation (three sentences using provided frames); 
  • Section 3 – My Pledge (carried forward from Scenario 2); 
  • Section 4 – My Learning Commitment (three-part statement from Scenario 3 reflection routine). The mentor provides feedback through ClassDojo’s messaging feature or Seesaw’s voice comment tool.

Language Focus

  • Reflective and evaluative writing (CALP): ‘The resource was helpful because…’, ‘I corrected…’, ‘I would change…’ 
  • Self-regulation vocabulary: goal, commitment, strategy, improvement, review. 
  • Academic language for self-assessment: ‘I have learned…’, ‘I still need to work on…’, ‘My next step is…’

3. FORMATIVE ASSESSMENT STRATEGY

Assessment within this module is strictly formative, informal, and low-pressure. The aim is to gather actionable information to improve ongoing instruction and to build pupils’ capacity for self-assessment, not to evaluate or grade them. All assessment strategies align with the TELMS approach: they are relational, scaffolded, and responsive to the social and emotional needs of migrant pupils.

3.1 Observation

Throughout all three scenarios, the mentor observes pupil engagement, language use, and interaction with digital tools as the primary source of formative data. Specific behaviours the mentor watches for include:

  • Scenario 1: Can the pupil navigate ClassDojo independently to upload a photo and set a goal? Does the pupil use the sentence frame (‘This is my… I am proud because…’) with minimal prompting? Is the pupil visibly comfortable sharing their work in the closing circle, or do they require additional emotional support?
  • Scenario 2: Does the pupil engage actively with the ‘Claim, Support, Question’ worksheet, or do they copy a peer’s responses? Can the pupil articulate why a claim is inaccurate using host language vocabulary? Does the pupil show critical engagement with the Kahoot! questions or answer randomly?
  • Scenario 3: Does the pupil progress from a vague to a more specific prompt across the activity? Can they identify at least one problem with their AI-generated output? Does the pupil use the evaluative vocabulary introduced in the session?

Observations are recorded informally in a brief mentor notebook or digitally via ClassDojo’s notes feature. The mentor does not interrupt activities to record – notes are made immediately after each scenario.

3.2 Exit Questions and Self-Reflection

Each scenario closes with a specific exit question or reflective prompt designed to give pupils a low-pressure opportunity to demonstrate what they have understood and to give the mentor actionable feedback for the next session:

Scenario 1 Exit Prompt

‘Name one thing you did well today and one thing you want to work on next time.’ (Oral or written; bilingual word bank available for lower-proficiency pupils.)

Scenario 2 Exit Prompt

‘In one sentence, explain why you should never use AI as your only source of information.’ (Written on exit card; sentence frame available: ‘I should not use AI as my only source because…’)

Scenario 3 Exit Prompt

‘Complete: When I use AI for studying, I will always…’ (Written and added to portfolio – serves as both a formative record and a personal commitment.)

 

The mentor reviews exit cards between sessions to identify pupils who need additional scaffolding and to adjust the focus of the following scenario accordingly.

3.4 Mentor Reflection

Peer assessment is integrated into each scenario in a structured, low-threat format:

  • Scenario 1 – Portfolio Circle: Pupils briefly show their uploaded work and the class offers one positive comment using the frame: ‘I like your portfolio because…’ This builds a supportive community norm around sharing work.
  • Scenario 2 – Group Claim Analysis: Within the small group fact-checking activity, pupils assess each other’s reasoning by responding to peers’ identifications with ‘I agree because…’ or ‘I would add that…’ The mentor models these phrases explicitly before the activity.
  • Scenario 3 – Prompt Swap: Pairs exchange their improved prompts from the worksheet activity and give feedback using a simple two-star-and-a-wish structure: ‘Two things that are strong about this prompt… One thing you could improve…’ The mentor circulates to ensure feedback is specific and constructive.

3.4 Mentor Reflection

After each scenario, the mentor completes a brief reflection addressing these questions: 

  • Which pupils demonstrated unexpected strength or difficulty?
  • Were the pupils comfortable using the AI tools?
  • Was the mentor able to offer enough support and make it clear that a critical, human approach is always crucial when it comes to the use of AI? 
  • Did the digital tool function as intended, and did pupils engage with it as expected? 
  • What would I change about the scaffolding or timing for next time?

These reflections directly inform the delivery of subsequent scenarios. For example, if observation and exit cards from Scenario 1 reveal that several pupils are not yet comfortable using ClassDojo independently, the mentor may build in an additional five-minute guided practice at the start of Scenario 2 before introducing the fact-checking activity. Similarly, if Scenario 2 exit cards show that a majority of pupils cannot articulate why AI errors occur, the mentor may spend additional time in Scenario 3 reviewing this concept before moving to prompt generation.

The mentor also uses portfolio entries in ClassDojo or Seesaw as a longitudinal formative record: reviewing pupils’ uploaded work and written reflections across all three scenarios to track the development of self-regulation, language, and critical thinking over the course of the module.

4. ADDITIONAL NOTES AND CONTEXTUAL INFORMATION

4.1 Cultural and Contextual Considerations

Poland has undergone a rapid transformation from a country of emigration to one of significant immigration, particularly following the outbreak of the war in Ukraine in 2022, which resulted in approximately 2.5 million foreign nationals residing in the country. The majority of migrant pupils currently in Polish schools are Ukrainian, though schools in larger urban centres also serve pupils from Belarus, Russia, Central Asia, and African countries.

Several cultural and contextual factors are particularly relevant to the delivery of this module in Polish schools:

  • Digital Access and Familiarity: Polish schools are generally well-equipped with digital infrastructure, and most pupils have access to tablets or laptops during lessons. However, newly arrived pupils – particularly those from conflict zones – may have experienced significant disruption to their schooling and may have limited familiarity with digital learning platforms. The mentor should not assume prior experience with tools such as ClassDojo.
  • Attitudes Towards AI: Ukrainian and other Eastern European pupils may arrive with different educational experiences regarding the use of AI and digital tools. The critical framing of AI in Scenarios 2 and 3 is particularly important in this context, as pupils who have not previously been taught to question digital sources may find the concept initially counterintuitive.
  • Socio-Emotional Sensitivity: Migrant pupils in Poland – particularly those who have experienced displacement and trauma may be sensitive to activities that involve personal disclosure or comparison with peers. The self-regulation theme of this module should be framed around growth and agency, never around deficiency. Portfolio sharing should always be voluntary, and the mentor should be prepared to offer a private alternative for pupils who are not ready to share publicly.

4.2 Connections to National Curriculum

This module connects to the Polish national core curriculum (podstawa programowa) across several competence areas:

  • Computer Science / Digital Education: The Polish national curriculum for Computer Science at primary and secondary level includes explicit competences related to safe and responsible use of digital tools, critical evaluation of online information, and the ethical dimensions of AI. Scenarios 2 and 3 of this module directly develop these competences, with particular alignment to the curriculum strand on developing algorithmic thinking and critical evaluation of digital sources.
  • Civic Education / Personal and Social Development: The module’s overarching theme of self-regulation, personal responsibility, and critical digital citizenship connects to the civic education strand of the Polish curriculum, which emphasises the development of responsible, reflective, and autonomous citizens capable of navigating complex information environments.
  • Cross-Curricular Competences: The Polish educational framework references the European Key Competences, including Learning to Learn and Digital Competence. This module is centrally concerned with both, making it applicable across subject boundaries and appropriate for integration into pastoral, language support, or tutoring contexts.

Spain (national)

This module connects to the Spanish national curriculum (LOMLOE) across several competence areas:

  • Computer Science / Digital Education: the compulsory ESO subject Tecnología y Digitalización (Real Decreto 217/2022) is structured around five blocks, including computational thinking, programming and robotics, and the sustainable and ethical use of digital technologies; by the fourth year, pupils explicitly work with AI applications “with critical and ethical sense.” Scenarios 2 and 3 of this module directly develop these competences.
  • Civic Education / Personal and Social Development: the compulsory subject Educación en Valores Cívicos y Éticos (Real Decreto 217/2022) develops critical and autonomous thinking, responsible democratic citizenship, and the safe use of information and communication technologies, connecting directly to the module’s focus on self-regulation and digital citizenship.
  • Cross-Curricular Competences: the key competence Competencia Personal, Social y de Aprender a Aprender (CPSAA), one of the eight key competences established under the LOMLOE (Real Decreto 157/2022), targets self-knowledge, emotional self-regulation, effective time and information management, and lifelong learning strategies, corresponding directly to the module’s central theme.

Catalonia (regional)

This module connects to the Catalan curriculum across several competence areas:

  • Computer Science / Digital Education: Decret 175/2022 includes Tecnologia i Digitalització at ESO level, developing computational thinking and explicitly requiring the responsible, critical, and ethical use of artificial intelligence as a new digital competence within the Catalan curriculum.
  • Civic Education / Personal and Social Development: Educació en Valors Cívics i Ètics develops critical and autonomous thinking and explicitly includes reflection on the ethical challenges posed by artificial intelligence, connecting directly to Scenarios 2 and 3.
  • Cross-Curricular Competences: the CPSAA key competence requires pupils to build a personal model of reflection and action that progressively increases their learning autonomy through cognitive, metacognitive, and motivational strategies, closely mirroring the module’s self-regulation focus.

The Netherlands

This module connects to the Dutch curriculum framework across several competence areas:

  • Computer Science / Digital Education: the new statutory kerndoelen digitale geletterdheid (due to enter into force in 2027, developed by SLO) explicitly include “AI-geletterdheid” (AI literacy), requiring pupils to understand how AI systems work, how algorithms shape what they see online, and how to engage critically with AI tools — directly relevant to Scenarios 2 and 3’s fact-checking and prompt-writing activities.
  • Civic Education / Personal and Social Development: the parallel kerndoelen burgerschap cover domains including “Society and Democracy” and “Societal Issues,” framing critical engagement with digital systems as a foundation for responsible democratic citizenship.
  • Cross-Curricular Competences: although “leren leren” is not a separate statutory kerndoel, self-regulated learning is treated as a foundational cross-curricular skill in Dutch primary pedagogy, with teachers expected to progressively increase pupil autonomy through modelled learning strategies, choice, and feedback, matching the module’s gradual-release approach.

Croatia

This module connects to the Croatian national curriculum across several competence areas:

  • Computer Science / Digital Education: the Kurikulum Informacijske i digitalne kompetencije and the CARNET experimental curriculum “Umjetna inteligencija: od koncepta do primjene” develop pupils’ understanding of AI concepts and their ethical and social implications, cultivating conscious, critical, and responsible participation in digital society.
  • Civic Education / Personal and Social Development: the cross-curricular theme Građanski odgoj i obrazovanje develops critical thinking and communication skills for responsible social participation, promoting values of responsibility and contribution to the common good.
  • Cross-Curricular Competences: the cross-curricular theme Učiti kako učiti (Learning to Learn) explicitly targets self-regulation of learning, emotion, and motivation, requiring pupils to trial and evaluate different learning strategies according to context — a close match to this module’s own title and rationale.

4.3 References and Resources

Skip to content