Multilingual Course Evaluation Software: 2026 Buyer's Guide

Most course evaluation tools can display a survey in more than one language. Far fewer can analyse free-text feedback across languages in a way that produces one comparable, accreditation-ready picture of a multilingual cohort. That gap — between translating the questions and understanding the answers — is where multilingual evaluation projects succeed or quietly fail.

This guide is for QA directors, institutional-research leads, and heads of teaching and learning at European universities running programmes in more than one language: international master''s cohorts, English-taught programmes in non-English-speaking countries, bilingual regions, and transnational (TNE) or joint-degree provision. It compares how the major platforms handle the three distinct layers of "multilingual", represents each honestly, and shows where Koji''s conversational model changes the analysis problem.

The short answer

If you only need to present a fixed questionnaire in several languages and report the numeric results together, most established tools do this well — EvaSys, Explorance Blue and Qualtrics all support multilingual survey administration. Numeric items translate cleanly because a 4-on-a-5-point-scale is a 4 in any language.

If your evaluation''s value is in the open-text comments — and in most institutions it is — the picture changes. Static survey tools translate the question but then hand you a pile of comments in five languages that someone has to read, translate, and code by hand. Even the strongest built-in text analytics analyse each language separately, so you cannot easily compare themes across a multilingual cohort.

Koji takes a different route: AI-moderated conversational interviews that run and probe in the student''s own language, with automatic thematic analysis that surfaces comparable themes across the whole cohort rather than one silo per language.

The three layers of "multilingual" — and where tools diverge

Buyers conflate these three things in RFPs, and vendors are happy to let them. Separate them and the real differences appear.

Instrument translation (the questions). Can you present the same questionnaire in multiple languages, with proper right-to-left and character-set support and accessible rendering? This is the commodity layer — most tools handle it.
Response collection (the answers). Can students respond in their own language, and does the tool capture that cleanly with the right encoding and metadata? Also broadly solved for closed items and free-text capture.
Cross-language analysis (understanding). Can the platform turn open-text answers in Portuguese, German, Polish and English into one coherent, comparable set of themes — without an analyst manually translating and coding each language? This is the hard layer, and it is where platforms genuinely differ.

How the major platforms compare

Dimension	EvaSys	Explorance Blue	Qualtrics	Koji
Multilingual instrument	Yes — multilingual setup	Yes — multilingual surveys	Yes — professional, self, or auto (Google Translate API)	Interview runs natively in the student''s language
Open-text capture	Yes	Yes	Yes	Conversational, with adaptive follow-up probing
Built-in text analytics	AI open-text processing (word clouds, sentiment)	MLY text analytics	Text iQ (sentiment/topics)	Automatic thematic analysis
Cross-language theme comparison	Limited	By language	Topics are language-specific; cannot group topics across languages	Themes surfaced across the whole cohort
Sentiment language coverage	Not publicly specified in detail	Vendor-specified	~16 languages for sentiment; fewer for topic detection	Analysis works on the collected multilingual responses
Best fit	Standardized multilingual surveys incl. paper	Large deployments needing structured analytics	Institutions already standardized on Qualtrics XM	Multilingual cohorts where qualitative depth is the goal

Two honest caveats about the table. First, vendor capabilities change; confirm current language coverage directly with each supplier before you buy. Second, "supports multilingual" on a feature page almost always refers to layers 1 and 2 — instrument and collection — not layer 3.

The hard part: analysing open text across languages

Qualtrics is a useful, well-documented example of the layer-3 constraint. Its Text iQ sentiment model covers roughly sixteen languages, but topics are specific to the response language, and topics from different languages cannot be grouped or related to each other. In practice that means a German comment about "Prüfungsdruck" and an English comment about "exam pressure" land in two separate topic buckets. To get one cross-cohort view, an analyst still translates and re-codes by hand — exactly the manual work the software was meant to remove. This is not a Qualtrics failing so much as a limitation of topic-modelling static text: the same pattern shows up, in different forms, across survey-based tools.

Automatic translation helps but introduces its own risk. Machine-translating comments before analysis (Qualtrics can auto-translate via the Google Translate API) flattens nuance and can mistranslate discipline-specific or culturally-specific phrasing — the very detail that makes qualitative feedback worth collecting. And translating identifiable free text through a third-party service raises a data-protection question you must answer before, not after, procurement.

Koji''s conversational model reframes the problem. Because an AI moderator conducts the interview in the student''s language and probes for specifics in that language, the raw evidence is richer to begin with — a vague "the labs were confusing" becomes a concrete, themed observation. The thematic analysis is then designed to surface comparable themes across the full cohort rather than producing one disconnected topic set per language. The same interview engine powers general multilingual customer and user research on the main Koji platform.

Multilingual is also an equivalence problem

Translating a question is not the same as asking the same question. A Likert item that reads neutrally in English can read as strongly worded once translated, and international students responding in a second language may interpret scale anchors differently — a genuine measurement-equivalence issue, not a translation typo. If you compare mean scores across language versions of a fixed instrument, you are assuming an equivalence you have probably not tested. We cover this in depth in Language Bias in Course Evaluation. Conversational, probing formats are more forgiving here because meaning is clarified in the exchange rather than fixed in a single translated stem — though no format removes the underlying challenge entirely.

GDPR and multilingual data handling

Two data-protection points specific to multilingual evaluation:

Translation routing. If open-text responses are auto-translated through an external service, student comments (potentially identifiable) leave your processing boundary. Confirm where translation happens and under what agreement.
Hosting and DPA. For European institutions, EU hosting and a current Data Processing Agreement remain the baseline regardless of how many languages you run. See our GDPR-compliant course evaluation guide.

When a competitor is the better choice

EvaSys if you run multilingual evaluations that include paper collection — its ICR scanning of handwritten forms across languages is a capability online-first tools do not replicate.
Explorance Blue if you need multilingual administration inside a large, structured, heavily-integrated deployment and your analytics needs are met per-language.
Qualtrics if your institution is already standardized on Qualtrics XM and you can live with per-language topic analysis (or resource the manual cross-language coding).

Choose Koji when your programmes are genuinely multilingual, the qualitative comments are where the value is, and you want comparable themes across the whole cohort without an analyst translating and coding five languages by hand.

Five questions to put in your multilingual evaluation RFP

Feature pages say "multilingual support". A procurement process needs sharper questions that separate the commodity layers from the one that matters. Put these to every shortlisted vendor and ask for a live demonstration, not a checkbox:

Can students respond in their own language and can I see a single, comparable set of themes across all languages — without an analyst manually translating and re-coding? Ask them to demonstrate it on a genuinely mixed-language sample. This is the layer-3 test, and it is the one most tools quietly fail.
If open text is translated for analysis, where does that translation happen, through which service, and under what data-processing terms? You need this answer for your DPIA before you sign, not after.
Which languages are supported for sentiment and topic analysis specifically — not just for displaying the survey? Coverage for instrument display is near-universal; coverage for analysis is far narrower and varies by vendor.
How do you handle right-to-left scripts, non-Latin character sets, and accessible rendering (WCAG 2.1 AA) in every supported language? Relevant for Arabic, Greek, Cyrillic and CJK cohorts, and for meeting accessibility obligations.
How do you keep numeric comparisons defensible across language versions of the same instrument? A vendor that acknowledges the measurement-equivalence problem is more trustworthy than one that pretends translation makes it disappear.

The pattern across these questions is deliberate: layers one and two are commodity, so spend your scrutiny on layer three and on data protection, because that is where multilingual projects actually succeed or fail.

Related resources

Running programmes in more than one language? The question to ask every vendor is not "do you support multiple languages" but "can you give me one comparable set of themes across all of them, without manual re-coding." See how Koji handles multilingual cohorts.