Gains:
- Ability to understand the concepts of differentiation (content, process, product) and readiness and produce material sets that adapt the same outcome to different levels.
- Ability to design layered tasks and scaffolding with artificial intelligence support for students with support needs, advanced levels and students who need language support.
- Understanding the risk of artificial intelligence reducing the student to a label and bias and seeing that differentiation should be combined with the teacher's recognition of the student
No two students are the same in a classroom. Some people understand the topic the first time it is explained, some want three more examples; While some are thirsty for an advanced question, some are stuck on the basic concept; Some are just learning Turkish, some are reading with dyslexia. Differentiated instruction is a teaching approach that allows achieving the same outcome in different ways. Differentiation is made in three dimensions: content (what is learned/at what difficulty), process (how it is learned/with what activity), product (how the student demonstrates what he has learned). AI makes differentiation practically possible by rapidly adapting the same outcome to multiple levels. But the most critical warning in this unit: AI can reduce the student to a label; Differentiation becomes injustice unless it is combined with the teacher's recognition of the student.
Readiness, interest and learning profile
Readiness is the level of prior knowledge and skill that the student has for that learning outcome — it is not "smart/not smart", it is where they are at that moment and it changes. Differentiation is based on three things: readiness (basic/intermediate/advanced), interest (capturing the student's curiosity), and learning profile (visual, auditory, hands-on preferences — take these as flexible preferences, not rigid patterns).
Scaffolding is setting up temporary supports for the struggling student: clue card, step-by-step example, word list, incomplete solution. The support is removed as the student progresses—much like scaffolding is removed when the building is finished. AI easily produces scaffolded and unscaffolded versions of the same task.
Attention: Differentiation does not mean "giving easy work to the weak student and difficult work to the good student". Everyone achieves the same gain; The changing path is the level of support and difficulty. The aim is not to equalize, but to move everyone forward.
Step by step: Layered task design with AI
- Start with a single gain. Differentiation happens around the same gain.
- Define three layers. Request basic (scaffolded), intermediate (standard), advanced (deepening) versions.
- Separate content/process/product. Specify on what dimension you will differentiate (e.g. same content, different product option).
- Craft scaffolding. Tips, examples, vocabulary support for struggling students; Open-ended expansion for the advanced learner.
- Add language support. A visual, short sentence version for students whose Turkish skills are developing.
- Label control. Does the output mold a student; Check if it is flexible and temporary.
- Incorporate student knowledge. YOU decide who is in which layer based on your own observation.
three mini cases
Case 1 — Three-fold reading. The reading level in one classroom teacher's classroom varied widely. He gave an informational text to the AI and asked for three versions: scaffolded (short sentence + image + word box), standard, advanced (additional critical question). Everyone tried to achieve the same goal in the same 40 minutes; Preparation was reduced to 20 minutes with AI. The teacher determined who would receive which version based on the students she knew.
Case 2 — Mathematics with scaffolding. For 5 students who were having difficulty solving the problem, a teacher asked the AI for a "step-by-step hint version of the same problem": a little guidance at each step, the first step example solved. For advanced students, he explained the same problem as "find more than one solution." Two weeks later, 4 of the struggling group switched to the clueless version — they served as scaffolding and were dismantled.
Case 3 — Returning from the label trap. A teacher described a group of students to AI as "unsuccessful students" and asked for materials. AI constantly offered this group the simplest, monotonous tasks, implicitly putting the students in a "can't do" position. The teacher noticed this and changed the prompt to “scaffolded tasks that move forward for students who are still at a basic level in this outcome.” When the language changed, the suggestions also became escalatory. The label lowered expectations.
Copiable templates
1) Three-layer task set:
Your role: [subject] teacher. Gain: "[gain]", [X. class].Create three layers of tasks for this ONE outcome: (1) basic: step-cued, scaffolded; (2) intermediate: standard; (3) advanced: open-ended, deepening. Let all three reach the SAME outcome. Do not label any students; temporarily define the layers as "those who are currently at this level".
2) Scaffolding (scaffolding) production:
Scaffold the following task for a student who is struggling with the subject: solve the first step as an example, add a short hint to each step, give the necessary keywords in a box. DO NOT lower the difficulty of the mission, just increase the boost. Task: [paste]
3) Language support version:
Adapt the following material for a student whose Turkish is developing: short sentences, frequently used words, a visual suggestion and an example for each step if possible. Keep the content and context, just make the language accessible. Material: [paste]
4) Expansion for advanced learner:
For students who have an early grasp of the "[gain]" topic, suggest 3 open-ended tasks that deepen the same topic: real-life application, comparison/analysis, and a small production task. Let there be tasks that challenge thinking, not additional "busy work".
Weak prompt / Strong prompt
Weak prompt:
Write easy questions for weak students.
Labeling, lowering expectations and disconnecting from achievement; "easy" is vague, it doesn't achieve the same gain.
Powerful prompt:
Your role: math teacher. Objective: "The student multiplies two-digit numbers." In this achievement, for students who are currently at the basic level, produce 4 problems with scaffolding, the first step of which is solved as an example and has clues at each step. Lower the difficulty; increase support. Let them reach the same gain.
Size
What changes?
Sample request to AI
Beware of risk
Content
Difficulty, reading level
"Three levels of release"
not to reduce earnings
Process
Event type, scaffolding
"Produce hinted version"
restoring independence
Product
Display format
"Poster/oral/written option"
keep the measure constant
language
accessibility
"Short sentence + visual"
Do not dilute the content
Common mistakes
- Label the student. Patterns like “poor/lazy/superior” distort both AI output and expectation; The level is temporary and specific to the achievement.
- Lowering the gain. Thinking that differentiation is "an easy job"; everyone should reach the same goal.
- Never dismantle the scaffolding. Constant hinting inhibits independence; The support is gradually removed.
- Changing the learning style to a rigid pattern. Saying "This child is visual" and limiting it to only one way; preferences are flexible.
- Leaving the definition of student to AI. The teacher decides who is where; AI does not know the student.
Tip: Frame the differentiation as "produce three versions, I'll distribute." Let the AI replicate the material; You, as the only person who knows which version, decides which version will go to which student.
In summary
Differentiation means achieving the same outcome in different ways in content, process and product dimensions. AI reduces the production of three layers of tasks, scaffolded versions and language support from a single acquisition to minutes; this makes differentiation practically feasible. However, AI may reduce the student to a label and lower expectations. Define the layers temporarily and specific to the achievement, never lower the achievement and decide with your own observation which student is where.
Application task
Select an achievement and ask AI for basic-intermediate-advanced versions with the "Three-tier task set" template. Check the output from two perspectives: (1) do all three versions achieve the same gain, (2) does any version implicitly put a group in a "can't" position. Then connect the basic version to a plan to gradually remove the clues and write it in 3 sentences.
checklist
- [ ] Do all three layers achieve the same gain?
- [ ] Did I describe students as "at this level right now" instead of labeling them?
- [ ] Have I established a plan to gradually remove the scaffolding?
- [ ] Have I made the content accessible without diluting it in the language support version?
- [ ] Did I decide which version would go to which student based on my own observation?