Page Comparison

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1. telemetry event structures (during curation)
   1. to better the auto curation process
2. telemetry event structures (during set acceptance/modification)
   1. to better query fulfilment
3. intent specification schema
   1. similar to Plug-n-Play analytics JSON-ified filtering criteria

05th June Scope

1. Preparatory Question Sets and Exam Question sets will be made available to
   1. Grades 9 and 10
   2. Seven Subjects (Maths, Science, Social Science, Hindi, English and Sanskrit)
   3. Coverage and quality will vary depend on the curation effort required, and quality of the data
2.  Auto Curation (Validation) metrics will be developed to reduce effort by the Human-in-the-Loop. This is seen as a general ML infrastructure capability (in particular a Reinforcement Learning Environment)
   1. Few rules will be developed to identify quality tags (Manual effort)
   2. Few algorithms will be developed to identify quality tags
3. Alignment between extracted Taxonomy terms from CBSE Question  Bank and NCERT will be aligned (Manual effort)
4. Sample Question Set Intents will be created (Manual effort)
5. Auto Textbook Creation: Given Textbook Spine and list of Blueprints, Textbook creation will be automated
6. Ability to modify the Intent, and select the result set will be developed
7. Support for passive consumption of Question-Answer pairs. There will not be any evaluation of answers, or interactions on the questions. They will be treated like normal resources types (not assessment resources)

Critical To Have:

1. doc to html to QML
2. renderer

Good To Have:

1. auto curating tags

Beyond June Scope

1. 16 core subjects,  k1-12 grades
2. QML implementation, support additional interaction types
3. Support for Exemplary Answers (actual Answers written by students, and taken as OCR images)
4. Support for ingesting previous exam paper questions