| name | cognitive-load-analyser |
| description | Analyse a learning task for cognitive load problems and recommend specific design improvements. Use when tasks overwhelm students, instructions feel complex, or materials need simplifying. |
| disable-model-invocation | false |
| user-invocable | true |
| effort | medium |
| skill_id | memory-learning-science/cognitive-load-analyser |
| skill_name | Cognitive Load Analyser |
| domain | memory-learning-science |
| version | 1.0 |
| evidence_strength | strong |
| evidence_sources | ["Sweller (1988) — Cognitive load during problem solving: effects on learning","Sweller (1994) — Cognitive load theory, learning difficulty, and instructional design","Paas & van Merriënboer (1994) — Instructional control of cognitive load in the training of complex cognitive tasks","Sweller et al. (2019) — Cognitive Architecture and Instructional Design: 20 Years Later (updated CLT)","Kalyuga et al. (2003) — The expertise reversal effect"] |
| input_schema | {"required":[{"field":"task_description","type":"string","description":"The learning task, instruction, or resource to analyse"},{"field":"student_level","type":"string","description":"Age/year group and expertise level (novice/intermediate/advanced)"}],"optional":[{"field":"task_materials","type":"string","description":"Description or text of worksheets, slides, or instructions used"},{"field":"student_profiles","type":"array","description":"From context engine: working memory profiles, prior knowledge data"},{"field":"lesson_context","type":"string","description":"What comes before and after this task in the lesson"}]} |
| output_schema | {"type":"object","fields":[{"field":"load_analysis","type":"object","description":"Breakdown of intrinsic, extraneous, and germane load with ratings"},{"field":"problem_areas","type":"array","description":"Specific elements creating unnecessary cognitive load"},{"field":"modification_suggestions","type":"array","description":"Concrete changes to reduce extraneous load and optimise germane load"},{"field":"expertise_reversal_check","type":"string","description":"Whether scaffolds may be counterproductive for advanced learners"}]} |
| chains_well_with | ["worked-example-fading-designer","explicit-instruction-sequence-builder","scaffolded-task-modifier","text-complexity-analyser"] |
| teacher_time | 4 minutes |
| tags | ["cognitive-load","task-design","scaffolding","instructional-design","working-memory"] |
Cognitive Load Analyser
What This Skill Does
Evaluates a learning task, instruction set, or resource for cognitive load across three dimensions: intrinsic load (inherent complexity of the content), extraneous load (unnecessary difficulty caused by poor design), and germane load (productive cognitive effort directed at schema building). Produces a specific diagnosis of where load is excessive and concrete modification suggestions. AI is specifically valuable here because cognitive load analysis requires simultaneously evaluating content complexity, instructional design quality, and learner expertise level — a skill that typically requires training in instructional design that most teachers lack.
Evidence Foundation
Sweller (1988, 1994) established Cognitive Load Theory (CLT) as a framework for understanding why some instructional designs fail: human working memory can hold approximately 4-7 elements simultaneously, and learning fails when the total cognitive load exceeds working memory capacity. Sweller distinguishes intrinsic load (determined by element interactivity — how many elements must be processed simultaneously), extraneous load (caused by poor instructional design), and germane load (productive effort directed at building schemas). Paas & van Merriënboer (1994) operationalised CLT for instructional design, demonstrating that reducing extraneous load consistently improves learning outcomes. Sweller et al. (2019) updated the theory to incorporate evolutionary psychology and refine the distinction between biologically primary and secondary knowledge. Critically, Kalyuga et al. (2003) identified the "expertise reversal effect" — instructional techniques that reduce load for novices (worked examples, integrated diagrams) can actually increase load for advanced learners by requiring them to process redundant information. This means cognitive load analysis must always consider learner expertise.
Input Schema
The teacher must provide:
- Task description: The learning task, instruction, or resource to be analysed. e.g. "Students read a 2-page text about osmosis while completing a diagram labelling activity and answering comprehension questions simultaneously" / "Solve quadratic equations by completing the square — worksheet with 20 problems"
- Student level: Age/year group and expertise level. e.g. "Year 10, first encounter with this topic (novice)" / "Year 12, revising for exam (advanced)"
Optional (injected by context engine if available):
- Task materials: The actual text, worksheet, or instructions being used
- Student profiles: Working memory profiles, known learning difficulties, prior knowledge data
- Lesson context: What happens before and after this task
Prompt
