learning-objectives

Measurable learning objectives aligned with Bloom's taxonomy and CEFR levels are generated by processing a topic description and producing 3-5 objectives with prerequisite analysis, assessment methods, and explicit success criteria. This ensures objectives are testable and progressively scaffolded for curriculum planning.

Yes, learning objectives for AI-native co-learning scenarios embed three-role AI partnership outcomes where learners practice working with AI as Teacher, Student, and Co-Worker. This ensures AI collaboration skills are explicitly testable within the generated objective set.

The best way to convert a chapter spec into validated learning objectives is to generate a compact set of 3-5 measurable objectives with Bloom's tags, CEFR mapping, and prerequisites, then run the included validation script to check measurability and conformity to curriculum standards.

Yes, the learning objective generation process outputs structured YAML data containing Bloom's level tags, CEFR mappings, prerequisites, assessment methods, and success criteria. This YAML output is directly compatible with the included validation script for automated measurability checking.

Yes, pyyaml is required as a dependency to parse and validate the YAML output containing the generated learning objectives. The validation script relies on this library to check objective measurability and conformity to Bloom's taxonomy and CEFR standards.

Curriculum learning objectives are often untestable due to vague phrasing lacking measurable verbs and success criteria. Fixing them involves applying Bloom's taxonomy tags, CEFR proficiency mappings, and explicit assessment methods to generate structured, validated objectives suitable for automated exercise generation.