GMAT Score Analysis: Beyond Prediction, Towards Diagnosis

I am honored to share my latest research findings with you. Simply put, what I have always pursued is “scientific rigor” and “logical thinking.” I absolutely cannot accept anything that lacks logic.

I’ve noticed that many GMAT score report analyses tend to be overly simplistic, often boiling down to advice like “just practice more.” That’s not particularly helpful. Any study plan should be based on evidence and rigorous reasoning, like a detective uncovering all skill deficiencies and systematically filtering them based on evidence to ultimately pinpoint your real problems.

But I always wondered if I could make my analysis process more efficient and accurate. How could I do better?

Throughout the process of building this system, I found myself thoroughly enjoying it. It was then that I realized why I was able to score highly on the GMAT myself back then. It’s due to my personality traits:

From “Experience” to “System”: My Seven Steps to Building a Diagnostic Framework

So, I started the process of standardizing, modularizing, parameterizing, and ultimately systematizing my ten years of experience coaching thousands of students, with the help of AI.

1. Step 1: Extracting Experience. I transcribed many consultation videos where I analyzed students’ GMAT scores. Then, I used AI to identify the key logical points and analytical patterns in my process.

1. Step 2: Completing the Logic. After reviewing these patterns from the consultation videos, I added my full analysis steps and logic, then had AI integrate them into a draft.
2. Step 3: Clarifying Definitions. I asked AI to find any vague or subjective terms in the draft and replace them with clear, specific definitions. For example, what exactly counts as “high” time pressure? Less than how many minutes? And what is “too fast”? Below what second count?
3. Step 4: Parameterization and Functionalization. I had AI define all elements as parameters and the logical relationships between them as functions. This built a complete analytical system, like a natural language code script.

1. Step 5: Ensuring Logical Closure. I had AI check the entire system for contradictions, undefined elements, unused definitions, and whether the process led to meaningful conclusions. The goal was a logically sound system.
2. Step 6: Preliminary Validation. I fed GMAT score report data into a reasoning AI to test the system. Comparing the AI’s analysis with my own, I was surprised to find over a 90% similarity rate!

1. Step 7: Calibration and Alignment. I then asked AI to analyze the remaining 10% of differences, trace the rules causing them, and make adjustments to improve accuracy and ensure it aligned perfectly with my analytical logic.

Research Findings: A Technical Document and an Automated Analysis Website

After continuous design, testing, and calibration, I’ve created a detailed technical document and an automated score analysis website (currently in a closed beta).

This system will allow students to perform self-analysis and free up educators like me to focus more on strategic planning for students.

You can explore the full research output here: my GitHub.

This Isn’t Just a Method, It’s My Persona

Building this system was surprisingly enjoyable. It made me realize I have:

• A strong preference for systematic thinking, valuing structure, logic, and verifiable results.
• An openness to complex new ideas, readily embracing and working with abstract concepts and structural changes.
• The ability not just to tolerate, but to actually enjoy complex reasoning that demands high cognitive effort.
• A dislike for subjective descriptions, preferring to use data to analyze situations objectively.

This process also highlighted my aversion to the “trial-and-error” and purely experience-based approaches common in some educational and consulting fields.

In my view, a method that is obscure, unclear, and cannot be verified and reproduced is not a good method. What I dislike is not experience itself; what I dislike is the betrayal of the essence of knowledge—relying solely on experience without scientific rigor, evolution, or accountability.

More importantly, this project has shown me that my ambitions go beyond just the GMAT. I believe I can contribute more significantly to the future of education. What I hope to see is an educational consulting system that can be proven, challenged, and scientifically improved, rather than becoming someone who relies on fame, intuition, and anecdotal experiences to sustain themselves while refusing to evolve.

Let’s keep pushing each other forward!

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