Koji vs Anthology Course Evaluations (2026): A Fair Comparison
Anthology Course Evaluations (formerly Campus Labs, now Blackboard Evaluate) is one of higher education''s most widely deployed evaluation suites. We compare it honestly against Koji''s AI-moderated interviews — where each tool fits, and where it doesn''t.
Koji Education Team
Product ·
Anthology Course Evaluations — the product many institutions still know as Campus Labs, and which Anthology now markets alongside Blackboard Evaluate — is one of the most widely deployed course-evaluation systems in higher education. If your institution is weighing it against Koji, the honest answer is that these are two different generations of tool: Anthology is a mature, LMS-integrated survey-and-reporting platform built around standardized instruments; Koji is an AI-native conversational platform built around moderated student interviews and automatic qualitative analysis.
This guide compares them fairly — including the cases where Anthology is the better choice — so a QA director, dean, or procurement lead can decide on evidence rather than marketing.
The short answer
- Choose Anthology if you want a proven, large-scale survey engine tightly coupled to an assessment/accreditation suite, you value nationally normed instruments (the IDEA System), and your institution is standardized on the broader Anthology/Blackboard ecosystem.
- Choose Koji if your priority is qualitative depth at scale — AI-moderated interviews that probe like a human, automatic thematic analysis of open-text, bias-aware standardized moderation, and closing-the-loop action tracking — with EU/GDPR-first data handling.
Both collect student feedback. They differ in how the feedback is collected and how much analytical work the tool does for you afterward.
What Anthology Course Evaluations does well
Anthology (formerly Campus Labs) partners with more than 2,100 institutions across 30+ countries and has years of operational maturity behind its course-evaluation product. Its genuine strengths are real:
- Standardized survey instruments at scale. Multiple-choice, Likert-scale, ranking, and narrative (open-text) question types, with the option to use nationally normed, validated instruments such as the IDEA System. For institutions that want comparability against external benchmarks, this matters.
- LMS-agnostic integration. It is delivered as a SaaS solution with LTI integration and multiple authentication options, and it integrates with your learning management system — useful for in-LMS response collection and single sign-on.
- Comparative reporting. Tables and graphs that display comparative analysis across courses, instructors, and departments, plus automated reminder emails to lift response rates.
- Part of a broader assessment suite. Anthology bundles course evaluations with Baseline (surveys/rubrics), Outcomes (programme- and co-curricular assessment), Planning, and analytics (Anthology Illuminate). If you already run institutional-effectiveness and accreditation reporting inside Anthology, course evaluations live in the same place.
For a North American institution already standardized on Anthology/Blackboard, that ecosystem gravity is a legitimate reason to stay.
Where the traditional survey model runs out of road
The limitation is not Anthology specifically — it is the static Likert-plus-comment-box paradigm that nearly every legacy tool shares. A fixed questionnaire cannot ask a follow-up question. When a student writes "the labs were confusing," no survey can respond "which part, and what would have helped?" You are left with a thin, ambiguous comment and a number whose meaning you have to guess.
That produces three recurring problems for evaluation owners:
- Shallow qualitative data. Open-text boxes get short, vague, or empty answers. The richest signal — why a course works or fails — is the hardest to extract.
- Manual analysis burden. Thousands of free-text comments still have to be read, coded, and themed by hand (or with bolt-on text analytics). That is slow, inconsistent between coders, and rarely closes the loop before the next cohort arrives.
- Survey fatigue and declining response rates. Identical end-of-term forms train students to straight-line or skip. Lower, less representative samples weaken every downstream decision.
As of publication, Anthology''s public product materials describe survey-based collection with reporting and analytics; they do not describe AI-moderated conversational interviews or automatic, bias-aware thematic analysis of open-text responses. If you need those, that is the gap Koji is built to fill.
What Koji does differently
Koji replaces the static form with an AI-moderated conversational interview. Each student is interviewed by an AI moderator that asks an opening question, listens, and probes — "you mentioned pacing; was that the lectures, the assignments, or both?" — exactly where a paper form goes silent.
- Conversational depth at survey scale. Every student gets a semi-structured interview, not a checkbox. You get the reasoning behind the rating, not just the rating.
- Automatic thematic analysis. Koji codes and themes open-text and interview transcripts automatically, surfacing the recurring issues across hundreds of conversations without a human coding team. (For the evidence on whether AI can do this reliably, see Can AI Reliably Analyze Thousands of Open-Text Student Comments?.)
- Bias-aware, standardized moderation. Every student is moderated with the same protocol, reducing the inconsistency of free-form comment boxes while keeping the depth of an interview.
- Formative and summative in one engine. Run mid-semester formative check-ins and end-of-term summative evaluations from the same platform, so problems get fixed during the course, not autopsied after it.
- Closing the loop. Findings convert into tracked actions, giving you the "you said, we did" documentation that quality cycles and accreditors increasingly expect.
- EU/GDPR-first data handling. Built for European data-protection expectations — relevant for any institution under GDPR and the EU AI Act.
Koji''s interview engine is the same one used on the main Koji platform (koji.so) for customer and user research — education applies that conversational research capability to students, courses, and programmes.
Side-by-side comparison
| Capability | Koji | Anthology Course Evaluations |
|---|---|---|
| Core method | AI-moderated conversational interviews | Standardized surveys (Likert, MCQ, ranking, open-text) |
| Follow-up probing | Yes — dynamic, per response | No — fixed questionnaire |
| Qualitative analysis | Automatic thematic analysis of transcripts | Manual review / comparative reporting; text analytics as add-on |
| Standardized instruments / norms | Bias-aware standardized moderation | Nationally normed instruments (IDEA System) |
| Formative + summative | Both, one engine | Primarily summative end-of-term |
| Closing-the-loop / action tracking | Built in | Reporting-led; depends on broader suite |
| LMS integration | Yes (link/SSO workflows) | LMS-agnostic, LTI integration |
| Broader assessment suite | Focused on evaluation/interviews | Baseline, Outcomes, Planning, Illuminate |
| Data residency | EU / GDPR-first | Varies by deployment; confirm with vendor |
| Public pricing | Not public — request a quote | Not public — request a quote |
Competitor details above are drawn from Anthology/Blackboard public materials as of publication. As of publication, public pricing was not available for either product; confirm specifics and data-residency terms directly with the vendor.
When Anthology is the better choice
Honesty matters more than a sales pitch with this audience, so to be clear:
- If you are already invested in the Anthology/Blackboard ecosystem for assessment, retention, and institutional effectiveness, keeping evaluations in the same suite reduces integration overhead.
- If you specifically need nationally normed benchmark instruments (e.g., IDEA) to compare against external sector data, Anthology offers that out of the box.
- If your process is genuinely satisfied by quantitative Likert trends plus comparative reporting, and qualitative depth is not a priority, a mature survey engine is sufficient — and switching tools would add cost without clear benefit.
There is no shame in that fit. The question is whether your bottleneck is collecting numbers or understanding why — and whether your team can keep manually theming open-text every term.
When Koji is the better choice
- You are drowning in shallow comment-box data and want depth without a coding team.
- You want formative feedback mid-course, not just a post-mortem.
- You need closing-the-loop evidence for ESG/ENQA, NVAO, or national accreditation, with action tracking attached to findings.
- GDPR and EU AI Act alignment is non-negotiable for your data governance.
How to decide
Run a controlled pilot. Take two comparable courses, evaluate one with your incumbent survey and one with Koji, and compare on three axes: (1) response rate and completion, (2) usable qualitative depth per response, and (3) analyst hours to a decision-ready report. The tool that gets you to a defensible, action-ready finding fastest — with feedback your faculty actually trust — is the one to scale.
For the broader market view, see our guide to the best course evaluation software for European universities (2026), and the AI course evaluation vs traditional SET surveys breakdown.
Ready to compare on your own courses?
The fairest test is your own data. Talk to Koji about a side-by-side pilot against your current evaluation tool, and see what conversational interviews surface that a static form cannot.