exam-question-tagging

To tag exam questions with knowledge points and export a structured CSV, provide your exam or practice document to the skill and request per-question analysis. It identifies each question, maps it to relevant knowledge points, and outputs structured metadata including question type, difficulty, and cognitive level.

Yes, knowledge point tagging works with PDF, DOCX, PPTX, XLSX/CSV, and plain text inputs. The skill processes each file format, identifies individual questions including sub-questions, and maps them to primary and secondary knowledge points for structured CSV export.

Knowledge point tagging supports single choice, multiple choice, short answer, calculation, and essay-style questions, including sub-questions within larger items. Each question receives metadata for question type, difficulty, cognitive level, and score alongside its mapped knowledge points.

Yes, when the skill encounters unreadable items during knowledge point tagging, it flags them with a note in the output. All readable questions are still processed and mapped to knowledge points, while unreadable entries are marked so you can review them manually.

The structured CSV export includes question_id, question_type, question_stem, knowledge_points, primary_kp, secondary_kp, difficulty, cognitive_level, score, and source_file for each question. This metadata enables knowledge-point mapping, exam analytics, and alignment with adaptive learning systems.

Upload your exam document and ask the skill to analyze the paper or identify covered knowledge points. It automatically reads each question, maps it to one or more knowledge points, and exports a structured CSV summarizing all knowledge point coverage for exam analytics and study planning.