Multilingual Assessments for Indian Languages
Assess candidates and employees in Hindi, Tamil, Telugu, Kannada, Malayalam, and Bengali. AI-powered translation with native script, Roman transliteration, and mixed-language scoring.
Key Features
6 Indian Languages Supported
Hindi, Tamil, Telugu, Kannada, Malayalam, and Bengali. Native script and Roman transliteration for every language — candidates choose how they want to read.
AI-Powered Translation
Production-grade AI translation that adapts tone to the script. Formal phrasing for native script, conversational phrasing for Roman script — matching how candidates actually communicate.
Transliteration & Roman Script
Candidates choose native script, Roman script, or both. Hindi in Devanagari or Roman letters — same content, candidate's preferred reading format.
Mixed-Language Scoring
AI evaluates content quality regardless of language. Hinglish, code-switching, and mixed-language responses are scored without penalty or back-translation.
Translation Glossary Management
Org-level glossary with per-language term overrides. Mark terms to keep in English. Ensures domain-specific vocabulary stays consistent across translations.
Per-Assessment Language Config
Configure language availability per assessment. Set script preferences, enable transliteration, and let candidates pick their language on the welcome screen.
From English content to multilingual assessment in three steps
Author once in English. AI handles the rest.
Build in English
Create your assessment content in English as usual — case studies, SJT scenarios, MCQ questions, artifact review materials. No changes to your content workflow.
AI Translates
In the Translations step, select target languages. AI translates content with register-appropriate phrasing — formal for native script, colloquial for Roman. Review and edit translations before publishing.
Candidates Choose
On the assessment welcome screen, candidates pick their language and script preference. The full assessment — questions, instructions, and UI — appears in their chosen language.
Why language accessibility matters for fair assessment
India's workforce is multilingual by default. Most candidates think in their regional language even if they can read English. When assessments are English-only, you're measuring English proficiency as a confounding variable — not the job competencies you actually care about.
The challenge is deeper than translation. The same concept reads differently in formal Devanagari Hindi versus colloquial Roman Hindi. A direct translation can feel stilted and unnatural — creating cognitive friction that disadvantages candidates who would otherwise perform well. Kaairo handles all three dimensions — language, script, and tone — so every candidate gets content that feels natural to read.
Mixed-language responses are the norm, not the exception. In Indian workplaces, professionals routinely code-switch between English and their regional language — especially for technical concepts. Kaairo's scoring evaluates the quality of thinking, not linguistic purity. A candidate who answers a case study in Hinglish is scored on problem framing, creative breadth, and solution feasibility — not on whether they wrote in pure Hindi or pure English.
The result: assessments that measure what candidates can actually do, in the language they think best in, without compromising scoring rigour.
Frequently asked questions
Which languages are supported?
Currently: Hindi, Tamil, Telugu, Kannada, Malayalam, and Bengali. Each language supports native script, Roman transliteration, or both. We are expanding to additional Indian and South Asian languages.
Does translation affect scoring accuracy?
No. The AI evaluates content quality regardless of which language a candidate writes in. For MCQs and SJTs, scoring is based on which option the candidate selected — language has zero impact on scores.
Can candidates mix languages in their responses?
Yes. Mixed-language responses (e.g., Hinglish — Hindi and English together) are scored without penalty. The AI evaluates the quality of thinking, not linguistic purity.
How does the translation glossary work?
Organisations can define per-language term overrides. For example, you can mark 'machine learning' to stay in English rather than being translated. This ensures domain-specific vocabulary remains consistent across all translations.
Do AI Voice Interviews support Indian languages?
Currently, AI voice interviews are conducted in English. Hindi voice interview support is on the roadmap. Written assessments (Case Study, SJT, MCQ, Artifact Review) fully support all 6 languages today.
Ready to assess in local languages?
Reach candidates who think best in their native language. Request a demo to see multilingual assessments in action.
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