| name | self-explanation-prompt-designer |
| description | Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles. |
| disable-model-invocation | false |
| user-invocable | true |
| effort | medium |
| skill_id | ai-learning-science/self-explanation-prompt-designer |
| skill_name | Self-Explanation Prompt Designer |
| domain | ai-learning-science |
| version | 1.0 |
| evidence_strength | strong |
| evidence_sources | ["Chi et al. (1989) — Self-explanations: how students study and use examples in learning to solve problems","Chi et al. (1994) — Eliciting self-explanations improves understanding","Hausmann & VanLehn (2007) — Explaining self-explaining: a contrast between content and generation","Wylie & Chi (2014) — The self-explanation principle in multimedia learning","Rittle-Johnson (2006) — Promoting transfer: effects of self-explanation and direct instruction"] |
| input_schema | {"required":[{"field":"learning_material","type":"string","description":"The specific content students are studying — a worked example, a text passage, a diagram, or a demonstration that students need to deeply understand"},{"field":"target_understanding","type":"string","description":"What students should understand DEEPLY after engaging with this material — the conceptual insight, not just the surface content"}],"optional":[{"field":"student_level","type":"string","description":"Age/year group and proficiency level"},{"field":"subject_area","type":"string","description":"The curriculum subject"},{"field":"delivery_mode","type":"string","description":"How the self-explanation prompts will be delivered — embedded in text, AI-generated during study, teacher-led, or integrated in a tutoring system"},{"field":"known_misconceptions","type":"string","description":"Specific misconceptions students commonly hold about this content"}]} |
| output_schema | {"type":"object","fields":[{"field":"self_explanation_prompts","type":"array","description":"The specific prompts placed at key points in the material — what to ask and where to ask it"},{"field":"prompt_rationale","type":"object","description":"Why each prompt is placed where it is and what understanding it is designed to elicit"},{"field":"quality_indicators","type":"object","description":"What a good self-explanation looks like versus a poor one — so the teacher or AI can evaluate student responses"},{"field":"scaffolding_sequence","type":"object","description":"How to support students who struggle to self-explain — from open prompts to more structured scaffolds"}]} |
| chains_well_with | ["digital-worked-example-sequence","adaptive-hint-sequence-designer","intelligent-tutoring-dialogue-designer","metacognitive-monitoring-ai-contexts"] |
| teacher_time | 4 minutes |
| tags | ["self-explanation","Chi","metacognition","worked-examples","comprehension","deep-learning","generation"] |
Self-Explanation Prompt Designer
What This Skill Does
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.
Evidence Foundation
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.
