So, now that r/ICSE has even changed its description, I guess we have to do it.

A few years ago, I wrote a program to predict our school’s question paper using old exam papers and the syllabus, with the help of ChatGPT. The predictions weren’t very accurate—only 5 or 6 questions were correct—but it was still an improvement. I left the project at that point because I didn’t have the knowledge, time, or resources to take it further.

But now, "Predicting the question paper" is an official goal of r/ICSE, so we have to make it happen. With the right amount of data and computing power, we can actually do it.

Methods for Prediction

1. Previous Year Paper Analysis

• Identify frequently asked questions and repeated patterns.
• Check for concepts that appear every year.
• Analyze the weightage given to each topic.

2. Syllabus Weightage & Blueprint Analysis

• Official syllabus documents sometimes indicate chapter-wise marks distribution.
• Prioritize high-weightage chapters.

3. Trend-Based Prediction

• Subjects like Mathematics and Science follow cyclic trends (e.g., if a chapter had long questions last year, it might have short ones this year).
• English & Humanities subjects often rotate between themes.

4. Teacher & Expert Insights

• Experienced teachers often predict likely topics based on curriculum changes and past trends.

5. AI/ML-Based Analysis (Advanced Approach)

• A machine learning model can be trained on past papers to identify patterns in question types, topics, and frequency.

Limitations

• Board exams often introduce surprise elements to discourage rote memorization.
• Changes in syllabus or exam format can disrupt predictions.
• No prediction method can guarantee 100% accuracy.

Final Thoughts

Yeah yeah, I know this is just a meme post… BUT HEAR ME OUT.

With enough data, computing power, and a little bit of science, we might actually crack the code. Will it be 100% accurate? No. Will it give us an edge? Probably.

At the very least, we’ll look like big-brained geniuses while doing it.