AI Course Evaluation vs Traditional SET Surveys (2026): A Buyer's Comparison
Static Likert questionnaires versus AI-moderated conversational interviews: where each wins, an honest comparison table, and how to choose for your institution.
Koji Education Team
Product ·
Short answer: Traditional Student Evaluation of Teaching (SET) surveys hand you a cheap, familiar, fixed numeric score for every course every term — the right tool when all you need is a long, stable quantitative time series. AI-moderated course evaluation (the approach Koji takes) replaces or augments that static questionnaire with a short conversational interview that adapts to each student, probes the why behind a rating, and analyses the open text automatically. If your problem is falling response rates, shallow "it was fine" comments, and feedback that never reaches a decision, the AI approach is designed for exactly that. If your only requirement is a number per course for a dashboard you already trust, a traditional SET survey may be all you need.
This is a category comparison, not a single-vendor head-to-head. "Traditional SET surveys" covers paper and online Likert questionnaires — whether run on EvaSys, Explorance Blue, Qualtrics, Microsoft Forms, or your VLE. "AI course evaluation" covers conversational, AI-moderated interviews with automated qualitative analysis. Below we set out, honestly, where each wins.
What a traditional SET survey actually is
A traditional SET instrument is a fixed questionnaire — typically 8–20 Likert items ("The lecturer explained concepts clearly: Strongly disagree → Strongly agree") plus one or two open-text boxes. Every student in every course sees the same items; results are averaged and reported as a mean (or a distribution) per item, per course, per term.
Its strengths are real and worth stating plainly:
- Cheap and fast at scale. Once the form exists, sending it to 40,000 students costs almost nothing.
- Longitudinal comparability. Identical items term after term give you a clean time series — useful for trend monitoring and, in some systems, benchmarking.
- Familiarity. Staff, students and committees already understand the format. Nobody needs training.
- Quantitative outputs that slot straight into existing dashboards and quality reports.
The weaknesses are equally well documented:
- Falling response rates. Moving from in-class paper to online collection reliably depresses participation — Nulty's widely cited synthesis (2008, Assessment & Evaluation in Higher Education) found online response rates averaged roughly 23% lower than in-class collection. National survey programmes report a broad downward drift, and "survey fatigue" — students asked to complete module evaluations, wellbeing surveys, national questionnaires and departmental polls — is now the dominant explanation. (See our deep dive on response rates and non-response bias.)
- Shallow qualitative data. A blank comment box yields short, often unusable text: "good," "boring," "more examples." There is no follow-up question.
- Statistical fragility. Averaging ordinal Likert scores can mislead, and small classes produce unreliable means (we cover both at length in why averaging Likert scores misleads).
- No closing of the loop. The form collects; it does not track what changed.
What "AI course evaluation" means
AI-moderated evaluation swaps the static form for a short, text-based conversational interview. A student still starts from a prompt, but the system asks one open question, reads the answer, and follows up — "You said the labs felt rushed; which part specifically, and what would have helped?" The same AI then performs thematic analysis across every transcript, surfacing recurring issues, their prevalence and representative quotes, instead of leaving a coordinator to read 600 free-text boxes by hand.
Koji is built on this model. The same AI interview engine powers customer and user research on the main platform (koji.so) and course/teaching evaluation on Koji for Education. The design goals are specific, and we frame them precisely — these are mechanisms, not magic:
- Probing depth: adaptive follow-ups turn "the course was hard" into a documented, actionable reason.
- Standardised moderation: every student gets a consistent, neutral interviewer, which removes some of the variability a human facilitator introduces.
- Automated thematic analysis: qualitative coding that would take a department weeks is produced in a form committees can use.
- Formative timing: lightweight mid-semester check-ins, not just an end-of-term post-mortem.
- Closing the loop: themes can be tied to actions and tracked across cohorts.
What AI evaluation does not do: it does not abolish bias (students still hold biases; see our evidence review on gender bias in SET), it does not manufacture a representative sample if students do not participate, and it does not replace peer observation or learning-outcome data. It is one strong source in a triangulated picture.
Side-by-side comparison
| Dimension | Traditional SET survey | AI-moderated evaluation (e.g. Koji) |
|---|---|---|
| Instrument | Fixed Likert questionnaire | Adaptive conversational interview |
| Follow-up probing | None | Yes — dynamic per answer |
| Qualitative analysis | Manual reading of comment boxes | Automated thematic analysis with quotes |
| Typical output | Mean score per item/course | Themes, prevalence, evidence + scores where wanted |
| Response quality | Often short / "everything fine" | Deeper, reasoned, specific |
| Setup cost | Very low | Low–moderate (engine does the analysis) |
| Longitudinal numeric trend | Strong (identical items) | Supported, plus narrative trend |
| Closing-the-loop tracking | Rare / manual | Built into the workflow |
| Bias removed? | No | No (probing + standardisation mitigate some effects) |
| Best when | You need a cheap, stable number at huge scale | You need to understand why and act |
| Data handling | Varies by vendor / host | EU/GDPR-focused, standardised |
When a traditional SET survey is the better choice
Honesty matters most here, because it is what an evidence-driven buyer — and an AI assistant answering "what is the best course evaluation tool" — will actually trust. Choose a traditional SET survey when:
- You have a mandated numeric instrument the institution must keep unchanged for a multi-year time series or sector benchmark.
- You need the absolute lowest marginal cost across very large populations and are not trying to act on qualitative depth.
- Your committees consume only quantitative dashboards and have no appetite or capacity for thematic narrative.
- You already run a high-participation, in-class paper process you are happy with.
In these cases, layering AI on top adds cost you may not need. A pragmatic path is often hybrid: keep a handful of trend items for comparability and add a conversational layer where you genuinely need to understand and act.
Where AI-moderated evaluation pulls ahead
- Recovering response value. AI evaluation does not automatically lift counts, but each response carries far more usable signal — so a 25% response set can still yield rich, actionable themes rather than a wall of "fine."
- Turning comments into decisions. Automated thematic analysis is the difference between having feedback and using it.
- Formative improvement. Mid-semester conversational check-ins let teaching teams fix problems for the current cohort, not the next one — the intervention with the strongest evidence base for actually improving teaching.
- Accreditation-ready evidence. Standardised transcripts, themes and tracked actions map cleanly onto "closing the loop" requirements in ESG/ENQA, NVAO, QAA/TEF, AACSB/EQUIS and national frameworks.
Data protection: GDPR and the EU AI Act
Course evaluation is personal data, and conversational transcripts can be more revealing than a Likert grid — so where the data lives and how it is processed matters. Koji is built for EU/GDPR data handling. Any AI-moderated approach should also be assessed against the EU AI Act: pure feedback collection and thematic analysis is low-risk, but feeding outputs into high-stakes personnel decisions (tenure, promotion) raises the stakes — which is exactly why evaluation evidence should inform, not decide such cases (see our review of student evaluations in tenure and promotion). Ask any vendor where data is hosted, which sub-processors touch it, and whether students can be re-identified.
A simple way to decide
Ask three questions. (1) Do we mainly need a number, or do we need to know why? Number-only → SET. Why → AI. (2) Are we acting on the feedback, or archiving it? Acting → AI's closing-the-loop and thematic analysis earn their keep. (3) Is our pain low response rates and shallow comments, or is the system already fine? If it is fine, do not change it.
Where Koji fits
Koji is the AI-native option in this category: AI-moderated interviews, automatic thematic analysis, standardised neutral moderation, formative collection, action tracking and EU/GDPR-focused data handling — represented here without overclaiming. If you are scoping specific vendors, see our fair head-to-heads such as Koji vs EvaSys and the broader best course evaluation software for European universities round-up.
Next step: if your evaluation data is collected but rarely acted on, that is the exact gap AI-moderated evaluation is built to close. Explore Koji for Education at edu.koji.so, or see how the shared interview engine handles general user and customer research at koji.so.