Create MCQs with ChatGPT in Scales

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You are an AI-powered json file generator that never make mistakes, and you are going to imagine 1000 MCQs about Astrophysics first in mind, then randomly give me 15 of them in this json form:

[
  {
    "order_number": "[Order Number]",
    "question": "[Question]",
    "type": "mcq",
    "option_a": "[Option A]",
    "option_b": "[Option B]",
    "option_c": "[Option C]",
    "option_d": "[Option D]",
    "answer": "[Correct Option in ABCD]",
    "explanation": "[Explanation of Why This Answer]"
  },
  {
    "order_number": "[Order Number]",
    "question": "[Question]",
    "type": "mcq",
    "option_a": "[Option A]",
    "option_b": "[Option B]",
    "option_c": "[Option C]",
    "option_d": "[Option D]",
    "answer": "[Correct Option in ABCD]",
    "explanation": "[Explanation of Why This Answer]"
  }
]

Please check the understanding of Learning objectives in your generated MCQs:

1. Describe the main objects comprising the universe.

2. Describe the nature of stars.

3. Understand astronomical distances.

4. Work with the method of parallax.

5. Define luminosity and apparent brightness and solve problems with these quantities and distance.

And these key concepts as well:

|Binary star|Two stars orbiting a common centre|
|:----|:----|
|Black dwarf|The remnant of a white dwarf after it has cooled down. It has very low luminosity|
|Black hole|A singularity in space time; the end result of the evolution of a very massive star|
|Brown dwarf|Gas and dust that did not reach a high enough temperature to initiate fusion. These objects continue to compact and cool down|
|Cepheid variable|A star of variable luminosity. The luminosity increases sharply and falls off gently with a well-defined period. The period is related to the absolute luminosity of the star and so can be used to estimate the distance to the star|
|Cluster of galaxies|Galaxies close to one another and affecting one another gravitationally, behaving as one unit|
|Comet|A small body (mainly ice and dust) orbiting the Sun in an elliptical orbit|
|Constellation|A group of stars in a recognizable pattern that appear to be near each other in space|
|Dark matter|Generic name for matter in galaxies and clusters of galaxies that is too cold to radiate. Its existence is inferred from techniques other than direct visual observation|
|Galaxy|A collection of a very large number of stars mutually attracting one another through the gravitational force and staying together. The number of stars in a galaxy varies from a few million in dwarf galaxies to hundreds of billions in large galaxies. It is estimated that 100 billion galaxies exist in the observable universe|
|Interstellar medium|Gases (mainly hydrogen and helium) and dust grains (silicates, carbon and iron) filling the space between stars. The density of the interstellar medium is very low. There is about one atom of gas for every cubic centimeter of space. The density of dust is a trillion times smaller. The temperature of the gas is about 100 K|
|Main-sequence star|A normal star that is undergoing nuclear fusion of hydrogen into helium. Our Sun is a typical main-sequence star|
|Nebula|Clouds of 'dust', i.e. compounds of carbon, oxygen, silicon and metals, as well as molecular hydrogen, in the space in between stars|
|Neutron star|The end result of the explosion of a red supergiant; a very small star (a few tens of kilometers in diameter) and very dense. This is a star consisting almost entirely of neutrons. The neutrons form a superfluid around a core of immense pressure and density. A neutron star is an astonishing macroscopic example of microscopic quantum physics|
|Planetary nebula|The ejected envelope of a red giant star|
|Red dwarf|A very small star with low temperature, reddish in color|
|Red giant|A main-sequence star evolves into a red giant - a very large, reddish star. There are nuclear reactions involving the fusion of helium into heavier elements|
|Stellar cluster|A group of stars that are physically near each other in space, created by the collapse of a single gas cloud|
|Supernova (Type la)|The explosion of a white dwarf that has accreted mass from a companion star exceeding its stability limit|
|Supernova (Type II)|The explosion of a red supergiant star: The amount of energy emitted in a supernova explosion can be staggering - comparable to the total energy radiated by our Sun in its entire lifetime!|
|White dwarf|The end result of the explosion of a red giant. A small, dense star (about the size of the Earth), in which no nuclear reactions take place. It is very hot but its small size gives it a very low luminosity]|

in Additional of:

1. Nuclear fusion provides the energy that is needed to keep the star hot, so that the radiation pressure is high enough to oppose further gravitational contraction, and at the same time to provide the energy that the star is radiating into space.

2. The power radiated by a star is known in astrophysics as the luminosity. It is measured in watts.

3. The unit of apparent brightness is W m^{−2}.

You must use a variety of multiple-choice question types:

1. [Background] + what is ...

2. What is true about ...

3. Which ... according  ...

4. Which ... can be ...

5. What is ... from ...

6. What is ...

7. Which ... is correct

8. Which statement ...

9. Which statement justify ...

10. Which is not correct ...

Do not repeat on one type of sentence.

Primary Order:

1. Make all MCQs base on above information and your knowledge if needed. When there are conflict between your knowledge and the above information. You should use your knowledge to add more detail and background to produce MCQs.

2. Be creative, change the perspective of your questions randomly, not necessarily from nouns to explanations, but from explanations to nouns, or ask questions based on the relationship between nouns, or ask questions in conjunction with examples; your goal is to help students fully understand the topic from all angles.

3. Only reply the MCQs and nothing else, do not write explanations. Make sure your answer has right json grammar.

4. Use english quotation marks or any other english punctuation only.

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[
  {
    "order_number": "1",
    "question": "What is a galaxy?",
    "type": "mcq",
    "option_a": "A collection of planets",
    "option_b": "A collection of stars",
    "option_c": "A collection of asteroids",
    "option_d": "A collection of comets",
    "answer": "B",
    "explanation": "A galaxy is a collection of a very large number of stars mutually attracting one another through the gravitational force and staying together."
  },
  {
    "order_number": "2",
    "question": "What is a main-sequence star?",
    "type": "mcq",
    "option_a": "A star that has run out of fuel",
    "option_b": "A star that is undergoing nuclear fusion of hydrogen into helium",
    "option_c": "A star that is about to explode",
    "option_d": "A star that is very small and has low temperature",
    "answer": "B",
    "explanation": "A main-sequence star is a normal star that is undergoing nuclear fusion of hydrogen into helium."
  }
]

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[
  {
    //Something
  },
  {
    //Something
  },
  {
    //Something
  }
]

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{
“order_number”: 1,
“question”: “What is an astronomical unit (AU)?”,
“type”: “mcq”,
“option_a”: “The average distance between the Earth and the Sun.”,
“option_b”: “The radius of the Earth.”,
“option_c”: “The distance traveled by light in one year.”,
“option_d”: “The mass of the Earth.”,
“answer”: “The average distance between the Earth and the Sun.”,
“explanation”: “An astronomical unit or AU is a unit of measurement equal to the average distance between Earth and the Sun. It is commonly used to measure distances within our solar system.”
},
{
“order_number”: 2,
“question”: “Which of the following is not a type of galaxy?”,
“type”: “mcq”,
“option_a”: “Elliptical”,
“option_b”: “Spiral”,
“option_c”: “Irregular”,
“option_d”: “Spherical”,
“answer”: “Spherical”,
“explanation”: “The three main types of galaxies are elliptical, spiral, and irregular. There is no galaxy type called spherical.”
},

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import json
from collections import OrderedDict

# Open the JSON file and load the data as a list of dictionaries
with open('data.json', 'r') as f:
    data = json.load(f)

# Create an ordered dictionary to store the objects by their order_number
data_dict = OrderedDict()
for obj in data:
    if 'order_number' in obj:
        order_number = obj['order_number']
        if order_number in data_dict:
            data_dict[order_number].append(obj)
        else:
            data_dict[order_number] = [obj]

# Create a new list of dictionaries with the objects ordered by their order_number
data_sorted = []
for order_number, objs in data_dict.items():
    for i, obj in enumerate(objs):
        obj['order_number'] = str(len(data_sorted) + i + 1)
    data_sorted.extend(objs)

# Write the modified list of dictionaries back to the JSON file
with open('question_bank.json', 'w') as f:
    json.dump(data_sorted, f, indent=2)

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python /path/to/the/qbReorder.py

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cd /the/path/to/working/folder

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python qbReorder.py

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python3 /qbReorder.py

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import argparse
import json
import random
from docx import Document
from docx.shared import Pt
from docx.enum.text import WD_ALIGN_PARAGRAPH

# Define command-line arguments
parser = argparse.ArgumentParser(description='Generate test papers, answer sheets, and marking scheme.')
parser.add_argument('-q', '--question-bank', type=str, required=True, help='Path to JSON file containing question bank.')
parser.add_argument('-n', '--number-question', type=int, required=True, help='Number of questions to include in the test paper.')
args = parser.parse_args()

# Open JSON file and load data
with open(args.question_bank, 'r') as f:
    data = json.load(f)

# Select random questions
selected_questions = random.sample(data, args.number_question)

# Create Test Paper, Answer Sheet, and Marking Scheme
test_paper_doc = Document()
answer_sheet_doc = Document()
marking_scheme_doc = Document()

# Set font
font = test_paper_doc.styles['Normal'].font
font.name = 'Arial'
font.size = Pt(12)

# Add test paper title
test_paper_title = test_paper_doc.add_paragraph('Test Paper', style='Title')
test_paper_title.alignment = WD_ALIGN_PARAGRAPH.CENTER
test_paper_doc.add_paragraph()

# Add answer sheet title
answer_sheet_title = answer_sheet_doc.add_paragraph('Answer Sheet', style='Title')
answer_sheet_title.alignment = WD_ALIGN_PARAGRAPH.CENTER
answer_sheet_doc.add_paragraph()

# Add marking scheme title
marking_scheme_title = marking_scheme_doc.add_paragraph('Marking Scheme', style='Title')
marking_scheme_title.alignment = WD_ALIGN_PARAGRAPH.CENTER
marking_scheme_doc.add_paragraph()

# Loop through selected questions and add to documents
for i, question in enumerate(selected_questions):
    # Test Paper
    test_paper_doc.add_paragraph(f'{i+1}. {question["question"]}')
    test_paper_doc.add_paragraph(f'     A. {question["option_a"]}')
    test_paper_doc.add_paragraph(f'     B. {question["option_b"]}')
    test_paper_doc.add_paragraph(f'     C. {question["option_c"]}')
    test_paper_doc.add_paragraph(f'     D. {question["option_d"]}')
    test_paper_doc.add_paragraph()

    # Answer Sheet
    answer_sheet_doc.add_paragraph(f'{i+1}. {question["question"]}')
    answer_sheet_doc.add_paragraph(f'     A. {question["option_a"]}')
    answer_sheet_doc.add_paragraph(f'     B. {question["option_b"]}')
    answer_sheet_doc.add_paragraph(f'     C. {question["option_c"]}')
    answer_sheet_doc.add_paragraph(f'     D. {question["option_d"]}')
    answer_sheet_doc.add_paragraph(f'Answer: {question["answer"]}')
    answer_sheet_doc.add_paragraph(f'Explanation: {question["explanation"]}')
    answer_sheet_doc.add_paragraph()

    # Marking Scheme
    marking_scheme_doc.add_paragraph(f'{i+1}. {question["question"]}')
    marking_scheme_doc.add_paragraph(f'Answer: {question["answer"]}')
    marking_scheme_doc.add_paragraph()

# Save documents
test_paper_doc.save('test_paper.docx')
answer_sheet_doc.save('answer_sheet.docx')
marking_scheme_doc.save('marking_scheme.docx')

1

pip install python-docx

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python qbBuilder.py -q question_bank.json -n 30

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usage: qbBuilder.py [-h] -q QUESTION_BANK -n NUMBER_QUESTION

Generate test papers, answer sheets, and marking scheme.

options:
  -h, --help            show this help message and exit
  -q QUESTION_BANK, --question-bank QUESTION_BANK
                        Path to JSON file containing question bank.
  -n NUMBER_QUESTION, --number-question NUMBER_QUESTION
                        Number of questions to include in the test paper.

1

python qbBuilder.py -q [Name of Question Bank File] -n [Number of Questions You Want to Include]