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IndustriesEducation2026-10-07

AI career advice is generic: local context covered only 35.4% of the time across ten countries

The “described correctly” problem applies to education too: models know the tech stack but frequently skip local ecosystems, language requirements and national policy.

Design: standardised prompts tested across six models (ChatGPT-4, DeepSeek, Gemini, Claude 3.5, Llama 3, Mistral) describing entry-level computing careers in ten African countries — 60 responses analysed against the Computing Curricula 2020 framework.

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IndustriesEducation2026-10-06

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.

Use case: admissions, visa, scholarship and eligibility questions — high-stakes study-abroad topics increasingly answered directly by LLMs, with unclear reliability beforehand.

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IndustriesEducation2026-10-06

Students are asking AI which major to choose: fix the bias first, then talk accuracy

The front door to admissions and academic advice is becoming a chat window, and AI answers can carry stereotypes — a risk and an opportunity for institutions.

Context: choosing a major is hard for incoming students, and traditional advising suffers from long waits, intimidating environments and limited personalisation — which is where chatbots step in.

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