Before handing study-abroad advice to AI: separate “wrong” from “said too much”
For high-stakes admissions, visa, scholarship and eligibility questions, models tend to under-cover or over-reach when domains intersect — so evaluation has to score accuracy and hallucination together.
Key numbers
- Use case: admissions, visa, scholarship and eligibility questions — high-stakes study-abroad topics increasingly answered directly by LLMs, with unclear reliability beforehand.
- Question sets: drawn from the real advising workflows of an education technology platform that supports students from discovery to enrolment, and categorised by domain scope — single-domain questions versus multi-domain ones that must integrate admissions, visa and scholarship evidence.
- Rubric: answers are graded correct, partial or wrong, and the rubric is domain-coverage aware — addressing only a subset of required domains counts as under-coverage while introducing extra, unnecessary domains counts as over-scoping, reflected in reduced relevance or hallucination scoring.
- Metrics: alongside accuracy the study reports faithfulness, answer relevance and an aggregate hallucination score, with all models tested on the same questions for a fair comparison.
Method and sample
arXiv paper “Domain-Grounded Evaluation of LLMs in International Student Knowledge” (2025-10-07): realistic questions from study-abroad advising workflows are used to evaluate multiple LLMs on both accuracy (is the information correct and complete) and hallucination (content unsupported by the question or domain evidence), with a reusable audit protocol.
What it means for brands
In study-abroad and visa services the risk is not only saying something wrong but omitting what should be said and adding what should not. The same applies to how AI describes an institution: admissions requirements, visa conditions and scholarship eligibility have to be explicit and verifiable, or the model fills the gap with irrelevant material.
How SourceSight handles it
For education projects SourceSight uses the questions students and parents actually ask (which to choose, what are the requirements, how much, how does the visa work), reports mentions and position per engine, and structures admissions facts so models have less room to improvise.
Limits
The public abstract describes the evaluation design and metric framework without per-model figures; the sample comes from one platform's advising workflow and does not represent every country or institution.
References
Run it on your own project
What the same prompts return across 10 engines is only knowable by running them.
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