A fine-tuned Mistral-7B model specialized for evaluating university admission essays and video interview transcripts. Built for the admissions committee.
Unlike general-purpose sentiment analyzers, this model identifies high-value behavioral patterns and distinguishes authentic personal experiences from generic or AI-generated responses.
"When I was 17, the water pump in our village broke and no one came to fix it for three months. I started mapping wells in a 10km radius using Google Maps and a bicycle. I helped 12 families. No one asked me to. When I showed the map to the local council, they actually used it. That's when I understood that data can change decisions. inVision U's systems design track is the only program I found that teaches exactly this."
Why it scores high: specific age, concrete problem, measurable impact (12 families, 10km), unsolicited initiative, explicit alignment with inVision U's specific program.
Low-scoring input (20–35)
"Throughout my academic journey, I have always been passionate about making a positive impact. From a young age, I recognized the importance of education. Furthermore, my leadership experiences have taught me collaboration. In conclusion, I am committed to bringing my unique perspective to your prestigious institution."
Why it scores low: no specific details, generic prestige language, 6+ GPT markers, structure applicable to any university.
The training dataset consists of interview answers labeled as strong or weak based on the inVision U selection rubric. Strong answers contain concrete stories with numbers, names, and real actions. Weak answers contain generic phrases that could apply to any institution.
Limitations & Ethical Disclosure
Decision support only. This model is intended to highlight high-potential candidates — not as a sole instrument for automatic rejection. All final decisions remain with the admissions committee.
English only. The model is optimized for English-language inputs. inVision U requires IELTS or TOEFL, so all interviews are conducted in English.
Human-in-the-loop required. Every AI recommendation can be overridden by committee members. All overrides are logged to an audit trail.
No PII stored. Candidate names, emails, and demographic data are excluded from model inputs. Only anonymized IDs are used.
Bias guard. A separate BiasGuard module runs before and after scoring to detect and flag potential demographic bias in outputs.
Citation
Built with Mistral-7B + Unsloth LoRA + llama-cpp-python
