P KAT 05 · Enable
Personalisation
What this KAT is for
Reach for this when you want students to have agency — over the goal they pursue, the artefact they produce, the process they take, or the pace at which they move. Personalisation differs from Differentiation in one key way: the choice belongs to the learner, not the teacher.
Familiar examples: a P4 student who races through Math practice in 10 minutes and is bored for the rest of the lesson; a P3 Chinese Language class where some students are ready to write paragraphs and others need more vocabulary work first; a Science project where one student wants to build a model, one wants to write a report, and one wants to make a video. Personalisation lets each child make decisions about their own learning — within structures you design.
MOE defintion:
Allow teachers to harness non-linear, interactive and adaptive features of digital tools to give students choice in their learning goals, artefacts, processes and pace.
How to choose a tool
Choice isn't the same as Personalisation. A menu where every option leads to the same place is fake choice. To do real P work, the tool needs to do at least one of these three things:
1 Signal 01 · Real choice
Choices lead to genuinely different paths
If every option produces the same artefact, students stop choosing meaningfully. The student who picks "podcast" should end up with a different kind of work than the student who picks "infographic."
2 Signal 02 · Student-set pace
Each student moves at their own speed
Self-paced practice, adaptive pathways, on-demand content. The tool doesn't hold fast students back or rush slow ones.
3 Signal 03 · Student-named goals
Learners set their own targets
Goal-setting prompts, personal progress trackers, "what I want to get better at" templates. The student knows what they're working towards because they helped name it.
Tools by subject
Across all subjects:
Choice-board project menus Free — students pick from poster, video explainer, or infographic to demonstrate a topic.
Personal inquiry questions on Padlet / SLS Free— students post one question they personally want answered about the topic — drives the unit.
SLS native
SLS Adaptive Learning Pathways MOE— activity routing branches based on student response — no two pathways need look the same.
SLS Choice activities (optional activities/quiz) MOE — menu of tasks at different challenge levels or interests — student chooses entry point.
SLS Student Notes & reflection prompts MOE — space for student-authored goal-setting and reflection across a unit.
AI differentiation
Students must complete the SLS AIEd Literacy Module before engaging with any AI tool in the classroom.
SchoolAI — student Spaces Norms Free — teacher-configured AI tutor in a chat-style Space — students work at their own pace; teacher monitors all conversations live via Mission Control.
Mizou Norms Free— teacher-configured AI roleplay tutor — student drives the conversation within teacher-set boundaries.
MagicSchool AI student tools: AI tutor/ AI Learning Assistant Norms Free — school-safe AI assistants with teacher-set guardrails for tutoring, brainstorming, and feedback.
English Language:
Choice-board writing prompts — students pick the prompt that interests them most — voice and engagement rise together.
Canva for Education Free — students choose their format — poster, presentation, infographic — to demonstrate understanding.
Chinese Language:
自学 vocabulary microsite (flashcards / matching / cloze / quiz) — students choose entry point based on confidence — same vocabulary, four ways in.
eZhishi Norms Free — students choose vocabulary sets and reading passages based on their own interest and confidence.
Padlet for student-led 词语 collection Free — students contribute words they've noticed and want to learn — driven by their own reading.
Mathematics:
Choice of explanation method Free — students choose to explain their solution as a bar model, a number sentence, or a verbal recording.
Science:
Self-paced PhET exploration with reflection log Free — students log what they tried, what surprised them, what they want to investigate next.
Lesson exemplars · Maris Stella teachers
Add your lesson — submit via Good Practices.
Design tips
Personalisation needs scaffolds, not absence. Younger learners (P3, P4) need clear menus, exemplars and goal-setting prompts before they can choose well.
The choice has to be real. If every "option" leads to the same place, students will spot it and disengage. Make the choices visibly different.
Frame GenAI as a tutor, never a homework-completer. Teach students to ask good questions and verify outputs. The skill is the conversation, not the copy-paste.
Goal-setting is a skill — teach it. A blank "set your goal" prompt is intimidating. Give a sentence starter: "By the end of this week I want to be able to…"
Self-paced doesn't mean unsupervised. Check in every few sessions. Some students self-pace into stagnation; others into burnout.