universal-mcq-practice-generator

name	universal-mcq-practice-generator
description	Generates exam-style multiple choice questions for ANY university worldwide. Covers ALL question patterns (single correct, multiple correct, assertion-reason, match-the-following, true/false, fill-in-the-blank). Adapts difficulty by Bloom's level and provides detailed answer explanations. Works for ALL subjects and ALL exam patterns.

Universal MCQ Practice Generator

Overview

Creates unlimited practice MCQs from any syllabus, topic, or study material. Each MCQ is calibrated to match the target university's exam pattern, marking scheme, and difficulty distribution. Includes fully explained answer keys.

How This Skill Works

1. User provides: Subject/topic, syllabus or notes, university exam pattern, number of questions, difficulty level
2. System analyzes: Content structure, typical question patterns for the university, Bloom's level distribution
3. System generates: MCQs with distractors, answer keys, and explanations
4. System formats: In the target university's exam format with proper marking scheme

1. MCQ Types Supported

Type	Description	Example University Patterns
Single Correct	One correct answer, 3-4 distractors	SPPU, VTU, JNTU, Mumbai, AKTU, IIT/JEE
Multiple Correct	Two or more correct answers	GATE, IIT, UK universities
Assertion-Reason	Assertion (A) and Reason (R) pair	NEET, JEE, AIIMS, UPSC
Match the Following	Column A vs Column B mapping	SPPU, Mumbai, VTU
True / False	Statement evaluation	Many universities
Fill in the Blank	Complete the statement	Language tests, general exams
Case-Based	Multiple MCQs from a common stem	CBSE, IB, North American
Ordering / Sequence	Arrange in correct order	GATE, programming exams
Diagram-Based	MCQ referencing a diagram	Medical, Engineering, Biology
NAT / Integer Answer	Single integer value (-9999 to +9999)	GATE, IIT, competitive exams

NAT (Numerical Answer Type) Format

Rules for GATE-style NAT questions:

• Answer must be an integer between -9999 and +9999
• No decimal points or fractional values
• No units in the answer field
• No sign for non-negative values (positive values are unsigned)
• Blank or out-of-range answers receive zero marks
• Negative marking may apply (per university pattern)

Example:

Q: How many vertices does a complete binary tree of height h have at most? [NAT]
Answer: 2^(h+1) - 1

2. Difficulty Calibration

Bloom's Level	Difficulty	Distractor Design	Example
Remember	Easy	1 near-miss + 2 clearly wrong	What is the capital of France? a) London b) Paris c) Berlin d) Madrid
Understand	Easy-Medium	2 plausible + 1 clearly wrong	Which best explains why TCP uses three-way handshake?
Apply	Medium	3 plausible with common errors	Given IP 192.168.1.10/24, what is the network address?
Analyze	Medium-Hard	All distractors based on real misconceptions	Which of these diagrams correctly represents the OSI model layers?
Evaluate	Hard	Distractors based on subtle differences	Which algorithm is most appropriate for this specific scenario?
Create	Hardest	Open-ended MCQs with best-answer format	Which design pattern best solves this architectural problem?

3. Distractor Generation Strategy

Strategy	Description	Example Wrong Answer
Off-by-one	Slightly wrong value	O(n) instead of O(log n)
Common misconception	Typical student error	Confusing TCP and UDP port ranges
Reversal	Opposite of correct	Confusing encryption and decryption
Partial knowledge	Almost but not quite right	Correct concept, wrong application
Confusable term	Similar-sounding or related term	Confusing liability and liquidity in accounting
Calculation error	Result of a common arithmetic mistake	14 instead of 18 in a sum
Out-of-scope	True but not answering this question	A correct statement that doesn't fit the context

4. Generation Modes

Mode 1 — Syllabus Coverage

• Input: Syllabus topics with weightage
• Output: N questions distributed proportionally across units
• Algorithm: Proportional allocation by marks/weightage

Mode 2 — Topic Deep Dive

• Input: Single topic or subtopic
• Output: 10-50 questions on that specific topic
• Algorithm: Progressive difficulty (Remember -> Create)

Mode 3 — Exam Simulation

• Input: University exam pattern (e.g., 30 MCQ, 45 min, no negative marking)
• Output: Full-length mock test matching exam format exactly
• Algorithm: Mirror the exact pattern, timing, and marking scheme

Mode 4 — Weak Spot Targeting

• Input: Topics where user performed poorly in previous tests
• Output: Targeted questions on those weak topics
• Algorithm: Higher density and multiple question types on each weak concept

Mode 5 — PYQ Replication

• Input: Previous year question papers
• Output: New variant questions testing same concepts but with different distractors and values
• Algorithm: Question template parameterization (change numbers, scenarios, phrasing)

5. Answer Key Format

Each generated MCQ set includes:

Answer Key
----------
Q1: B (Remember, Easy)
Q2: A, C (Understand, Medium)
Q3: D (Apply, Medium-Hard)
Q4: B (Analyze, Hard)

Explanations:
Q1: Why B is correct... Why A, C, D are wrong...
Q2: A is correct because... C is correct because... B and D are wrong because...

Explanation Depth Levels

Level	Detail	Use Case
Brief	Correct answer + 1 sentence why	Quick review
Standard	Correct answer + why each distractor is wrong	Exam prep
Detailed	Full concept explanation + references + common pitfalls	Learning mode

6. University Pattern Templates

University	Typical MCQ Pattern	Negative Marking	Time per Q
SPPU (2019/2024)	20 MCQ per subject, 1 mark each	No (mostly)	2.25 min
VTU	20-30 MCQ, 1 mark each	No	2 min
JNTU	20 MCQ, 1 mark each	No	2 min
GATE	55 Q (25 MCQ + 30 numerical), 1-2 marks	Yes (-1/3 for 1-mark, -2/3 for 2-mark)	3 min
NEET	200 MCQ (180 to attempt), 4 marks each	Yes (-1)	~1 min
UPSC	100 MCQ, 2 marks each	Yes (-1/3)	2 min
IIT/JEE	30 MCQ + numerical	Yes (variable)	3 min
UK University	Varies, often essay + MCQ mix	Typically no	Varies
North American	50-100 MCQ per exam	Typically no	~1 min

7. Sample MCQ Generation

User: Generate 5 MCQs on OS Process States for VTU CSE, 1 mark each, Remember-Understand level.

VTU CSE - Operating Systems - Process States

1. Which of the following is NOT a valid process state?
   A) Running
   B) Ready
   C) Suspended
   D) Waiting
   Answer: C (Suspended is not a fundamental Unix/Linux process state)

2. When a process is waiting for I/O completion, it is in which state?
   A) Ready
   B) Running
   C) Blocked/Waiting
   D) Terminated
   Answer: C

3. A process transitions from Running to Ready when:
   A) It completes execution
   B) The CPU scheduler preempts it
   C) It requests I/O
   D) It creates a child process
   Answer: B

4. The only state transitions that can occur without OS intervention:
   A) Ready -> Running
   B) Running -> Ready
   C) Running -> Waiting
   D) Waiting -> Ready
   Answer: D (I/O completion generates interrupt, but OS handles it)

5. In a 5-state model, which state is added to handle virtual memory?
   A) New
   B) Ready Suspend
   C) Terminated
   D) Waiting
   Answer: B

Session Config

This skill integrates with the session config system (deps/session-profile.json). Before executing, check for an existing session profile:

• If deps/session-profile.json exists, read university, subject, pattern, and exam_type fields to auto-configure the skill.
• If the file does not exist, fall back to user-provided context or prompt the user to run setup-exam-prompt (or npm run init) first.
• Session config eliminates redundant context detection — detection happens once and is reused across all skill calls.

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Error Handling

Situation	Action
Subject/topic not specified	Respond: "Please specify subject and topics for MCQ generation."
Insufficient syllabus detail	Flag: "Syllabus detail is sparse. MCQ coverage may be incomplete. Provide detailed subtopics if possible."
University pattern not recognized	Default to Standard MCQ format (single correct, no negative marking) and note the assumption
NAT question answer out of range	Validate: NAT answers must be integers between -9999 and +9999. Flag violations before final output.
Distractor generation failure	Fall back to 3-distractor model if 4 plausible distractors cannot be generated

Quality Gate — Check Before Output

• Each MCQ has exactly one correct answer clearly identified
• Distractors are plausible and based on common misconceptions
• Bloom's level tag included for each question
• Answer key with explanations is provided
• Question count matches user's requested number
• No duplicate questions (same or rephrased)
• For NAT questions: answer validated as integer within [-9999, 9999]

8. Integration with Other Skills

Skill	Integration
universal-session-config	Reads university/subject/pattern from session profile
universal-notes-generator	Feeds notes as source material for MCQ content
universal-pyq-analyzer	Uses historical question distribution to guide topic focus
universal-imp-topics-generator	Generates targeted MCQs for high-probability topics
universal-flashcard-generator	Creates complementary flashcards from missed MCQs
universal-essay-grader	Evaluates performance patterns across MCQ attempts