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Generates self-explanation prompts for worked examples, texts, or diagrams to deepen student understanding. Useful when learners passively consume material without engaging with underlying principles.
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/education-agent-skills:self-explanation-prompt-designerThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Designs self-explanation prompts — the specific questions placed at key points in learning material that prompt students to explain TO THEMSELVES why something is true, how it works, or what it means. Chi et al. (1989) discovered that students who spontaneously self-explain while studying worked examples learn dramatically more than those who don't — and crucially, Chi et al. (1994) showed that...
Designs self-explanation prompts — the specific questions placed at key points in learning material that prompt students to explain TO THEMSELVES why something is true, how it works, or what it means. Chi et al. (1989) discovered that students who spontaneously self-explain while studying worked examples learn dramatically more than those who don't — and crucially, Chi et al. (1994) showed that students who are PROMPTED to self-explain also show significant learning gains, even if they wouldn't have self-explained spontaneously. The self-explanation effect is one of the most robust findings in learning science: it works across ages, domains, and material types. The mechanism is that self-explaining forces the learner to generate inferences, fill in gaps, connect new information to existing knowledge, and notice their own confusion — all of which strengthen understanding. The challenge is designing prompts that actually elicit deep self-explanation rather than shallow restating. "Why does this step follow from the previous one?" is a good self-explanation prompt. "What happens in this step?" is not — it elicits description, not explanation. AI is specifically valuable here because it can deliver prompts at exactly the right moment (during study, not after) and can evaluate the quality of student responses in real time.
Chi et al. (1989) conducted the seminal study on self-explanation, observing students studying worked examples in physics. They found that "good students" (those who learned the most) spontaneously engaged in self-explanation: they paused at each step to explain to themselves WHY the step made sense, HOW it connected to the previous step, and WHAT principle it illustrated. "Poor students" read the examples passively, focusing on the surface steps without generating explanations. The difference in learning was dramatic — and it was not explained by prior knowledge, intelligence, or study time. The critical variable was the QUALITY of cognitive engagement during study. Chi et al. (1994) tested whether prompting self-explanation could replicate the benefits seen in spontaneous self-explainers. They gave students a biology text and prompted one group to self-explain each sentence ("What new information does this sentence provide? How does it relate to what you already know?"). The prompted group significantly outperformed the control group on both immediate and transfer tests. This finding was revolutionary because it demonstrated that a simple instructional intervention (prompting) could produce the same benefits as a rare cognitive habit. Hausmann & VanLehn (2007) investigated what makes self-explanation effective. They found that the benefit comes from two components: the CONTENT of the explanation (the ideas generated) and the act of GENERATION itself (producing an explanation rather than reading one). When they compared self-generated explanations with instructor-provided explanations of equal quality, the self-generated ones still produced better learning — suggesting that the act of generating, not just the content, drives the effect. Wylie & Chi (2014) extended self-explanation research to multimedia learning, showing that self-explanation prompts embedded in digital materials (videos, interactive simulations, digital texts) produce the same benefits as prompts in text-based materials. They found that prompts should be placed at points of high conceptual density — where the material introduces a new principle, makes a non-obvious inference, or contradicts common intuition. Rittle-Johnson (2006) studied self-explanation in mathematics and found that self-explanation prompts improve both conceptual understanding and procedural transfer — students who self-explain can not only solve the problems they studied but also apply the principles to new, structurally different problems.
The teacher must provide:
Optional (injected by context engine if available):
You are an expert in self-explanation research, with deep knowledge of Chi et al.'s (1989, 1994) foundational studies, Hausmann & VanLehn's (2007) work on the generation effect in self-explanation, Wylie & Chi's (2014) self-explanation principle in multimedia learning, and Rittle-Johnson's (2006) research on self-explanation in mathematics. You understand that self-explanation is one of the most powerful learning strategies available — and one of the easiest to implement badly. The difference between effective and ineffective self-explanation prompts is the difference between deep conceptual engagement and surface-level restating.
CRITICAL PRINCIPLES:
- **Prompts must elicit EXPLANATION, not DESCRIPTION.** "What happens in Step 3?" elicits description ("We add 9 to both sides"). "WHY do we add specifically 9 — where does 9 come from?" elicits explanation ("Because (6/2)² = 9, and adding this completes the perfect square"). The prompt must target the REASONING, not the CONTENT.
- **Place prompts at points of high conceptual density.** Wylie & Chi (2014): self-explanation prompts are most effective when placed where the material introduces a new principle, makes a non-obvious step, or is likely to be misunderstood. Don't prompt at every step — prompt at the steps that MATTER for understanding.
- **The generation is the learning.** Hausmann & VanLehn (2007): even if a student's self-explanation is imperfect, the ACT of generating it produces learning. Design prompts that require generation (constructing an explanation in their own words) rather than recognition (selecting the right answer from options). Free-response prompts > multiple choice.
- **Self-explanation prompts should be answerable.** A prompt that the student cannot answer without additional instruction is not a self-explanation prompt — it's a test question. Self-explanation works by activating and connecting EXISTING knowledge. The material should contain enough information for a thoughtful student to construct an explanation.
- **Distinguish self-explanation from other activities.** Self-explanation is not summarising (restating the content), not predicting (guessing what comes next), and not evaluating (judging whether something is good). It is explaining the REASONING behind something — the WHY and the HOW, not the WHAT.
Your task is to design self-explanation prompts for:
**Learning material:** {{learning_material}}
**Target understanding:** {{target_understanding}}
The following optional context may or may not be provided. Use whatever is available; ignore any fields marked "not provided."
**Student level:** {{student_level}} — if not provided, design for a general secondary school context.
**Subject area:** {{subject_area}} — if not provided, infer from the material.
**Delivery mode:** {{delivery_mode}} — if not provided, design for prompts embedded in a digital learning environment (shown on screen at key points).
**Known misconceptions:** {{known_misconceptions}} — if not provided, identify the most likely misconceptions based on the material.
Return your output in this exact format:
## Self-Explanation Prompts: [Material Description]
**Material:** [What students are studying]
**Target understanding:** [The deep insight students should reach]
**Number of prompts:** [How many, and why this number]
### Prompt Placement Map
[Show where each prompt falls in the material — at which step, sentence, or element. Explain why THAT point was chosen.]
### Self-Explanation Prompts
For each prompt:
**Prompt [N] — placed at: [specific location in the material]**
- **Prompt text:** [Exactly what the student sees]
- **Target explanation:** [What a good self-explanation would include]
- **Why here:** [Why this point in the material is a high-value prompt location]
- **Common shallow response:** [What students say when they're describing rather than explaining]
- **Follow-up if shallow:** [What to ask next if the student's response is surface-level]
### Quality Indicators
[How to distinguish deep self-explanation from shallow restating — with specific examples of each]
| Quality Level | What It Sounds Like | Example | What to Do |
|---|---|---|---|
| Deep | [Characteristics] | [Example response] | [Affirm and continue] |
| Partial | [Characteristics] | [Example response] | [Prompt for elaboration] |
| Shallow | [Characteristics] | [Example response] | [Redirect to reasoning] |
### Scaffolding Sequence
[For students who struggle to self-explain: how to move from open prompts to increasingly structured scaffolds without giving the answer]
**Self-check before returning output:** Verify that (a) prompts elicit explanation not description, (b) prompts are placed at conceptually dense points, (c) prompts require generation not recognition, (d) the material contains enough information to construct an explanation, and (e) quality indicators distinguish deep from shallow responses.
Scenario: Learning material: "A worked example showing how to solve a quadratic equation by completing the square: Step 1: x² + 6x + 2 = 0. Step 2: Rearrange to x² + 6x = -2. Step 3: Add 9 to both sides: x² + 6x + 9 = -2 + 9 = 7. Step 4: Factorise the left side: (x + 3)² = 7. Step 5: Take the square root of both sides: x + 3 = ±√7. Step 6: Solve: x = -3 ± √7." / Target understanding: "Why completing the square works — specifically, understanding that (b/2)² is the number that transforms x² + bx into a perfect square trinomial, and why this transformation is useful (it lets us take the square root of both sides)" / Student level: "Year 11, GCSE Higher" / Known misconceptions: "Students think 9 appears 'by magic' — they can follow the steps but don't understand where 9 came from. Students also struggle with ±√7 — they forget the negative root or don't understand why there are two answers"
Material: Worked example solving x² + 6x + 2 = 0 by completing the square Target understanding: Why (b/2)² completes the square — the algebraic structure, not just the procedure Number of prompts: 3 — placed at the three points of highest conceptual density (Steps 2, 3, and 5)
The worked example has 6 steps. NOT every step needs a prompt:
Prompt 1 — placed at: Step 2 (x² + 6x = -2)
Prompt 2 — placed at: Step 3 (x² + 6x + 9 = 7)
Prompt 3 — placed at: Step 5 (x + 3 = ±√7)
| Quality Level | What It Sounds Like | Example | What to Do |
|---|---|---|---|
| Deep | References the mathematical PRINCIPLE — explains WHY, connects to structure, uses own words | "We add 9 because (6/2)² = 9, and that's what makes x² + 6x + 9 factorise into a perfect square. We need a perfect square so we can take the square root in the next step." | Affirm: "Exactly right. You've identified the key principle." Move on. |
| Partial | Correct but incomplete — states part of the reason without the full chain | "9 comes from halving 6 and squaring it." | Prompt for elaboration: "Good — but WHY does halving and squaring give you the right number? What happens when you add it to x² + 6x?" |
| Shallow | Describes the action or restates the procedure without explaining the reasoning | "We add 9 to both sides to complete the square." | Redirect: "You've described WHAT we did, but I need you to explain WHY. Where does 9 specifically come from? Why not 4 or 16?" |
For students who struggle to self-explain, move through these levels:
Level 1 — Open prompt (default): "Explain why we added 9 to both sides."
Level 2 — Directed prompt: "Look at the number in front of x (it's 6). Try halving it. Now try squaring what you get. What number do you get? Does that match what was added?"
Level 3 — Verification prompt: "Try expanding (x + 3)². What do you get? Now compare that to x² + 6x + 9. What do you notice?"
Level 4 — Completion prompt: "We add (b/2)² because x² + bx + (b/2)² = (x + b/2)². In this case, b = 6, so (b/2)² = ___. And x² + 6x + ___ = (x + ___)². Fill in the blanks."
The sequence moves from maximum generation (Level 1) to maximum scaffolding (Level 4). Always start at Level 1 — the generation attempt, even if it fails, produces learning (Hausmann & VanLehn, 2007). Only escalate if the student is genuinely stuck, not just producing an imperfect explanation.
Self-explanation prompts slow down study time. A student who self-explains at every step will take significantly longer to work through material than a student who reads passively. This is a DESIRABLE difficulty (Bjork, 1994) — the extra time produces deeper learning — but it can create practical problems in time-constrained contexts (exam revision, homework). Teachers must make conscious choices about where to invest self-explanation time.
The quality of self-explanation is hard to evaluate automatically. An AI can detect the LENGTH of a response and the presence of key terms, but evaluating whether a self-explanation is genuinely deep (rather than long and shallow) requires understanding the conceptual content. Current LLMs can approximate this evaluation, but they may mis-classify sophisticated but unconventional explanations as shallow.
Self-explanation works best with conceptually rich material. Procedural content with minimal conceptual depth (e.g., formatting a spreadsheet, conjugating regular verbs) benefits less from self-explanation prompts because there is less to EXPLAIN. Self-explanation is most powerful when the material contains non-obvious reasoning, hidden connections, or common misconceptions.
Students may need training to self-explain. Chi et al. (1994) found that prompting alone was effective, but other studies suggest that students benefit from initial training in what a good self-explanation looks like. The scaffolding sequence above addresses this, but in the first session using self-explanation prompts, the teacher may need to model the process explicitly.
npx claudepluginhub garethmanning/education-agent-skills --plugin education-agent-skillsGenerates elaborative interrogation prompts (why/how questions) to deepen encoding and comprehension. Useful when students memorize without understanding or need deeper processing of content.
Calibrates explanations to the learner's level using scaffolding and Socratic questioning. Use when users ask 'how does X work?' or show conceptual gaps.
Explains concepts using Socratic-style dialogue, guiding users to understanding through reflective reasoning and analogies rather than lectures.