Course Evaluation Evidence for Danish & Icelandic Accreditation (Akkrediteringsinstitutionen & the Icelandic Quality Board)

Denmark and Iceland both moved their quality assurance away from approving individual programmes and toward judging whether an institution''s internal quality system actually works in practice. That shift changes what you have to prove. It is no longer enough to show you collect student feedback — you have to show the feedback is systematic, that it is analysed, that it leads to action, and that the loop closes. This guide maps what the Danish Accreditation Institution (Danmarks Akkrediteringsinstitution) and the Icelandic Quality Board (now the Icelandic Agency for Quality Assurance) actually look for, and shows concretely how course-evaluation data becomes accreditation-ready evidence — including where a conversational, AI-moderated approach like Koji strengthens the evidence and where it does not replace the wider quality system.

Scope note. This is general guidance for procurement and QA teams, not legal or accreditation advice. Always work from the current ministerial orders, the Accreditation Institution''s published guides, and the Quality Board''s framework documents, and confirm details with your agency contact.

Denmark: institutional accreditation, not programme approval

Denmark established its accreditation system in 2007 and, with the Accreditation Act of 2013, shifted from accrediting individual programmes to accrediting whole institutions. Two bodies share the work: the Accreditation Council (Akkrediteringsrådet) is the decision-making authority, and the Danish Accreditation Institution (Akkrediteringsinstitutionen) is the operator that runs the assessments.

Institutional accreditation asks one central question: does the institution have a systematic and well-functioning quality assurance system, anchored at management level and supporting an inclusive quality culture, that secures the quality and relevance of programmes before, during, and after accreditation? The first round of institutional accreditation used five criteria; the current model is simplified, but the substance is the same — quality work has to be real and demonstrable in practice, not just documented on paper.

The outcomes are consequential:

• Positive — the system is well-described, well-argued, and well-functioning in practice. The institution can establish new programmes through the pre-qualification (prækvalifikation) process administered by the Danish Agency for Higher Education and Science.
• Conditionally positive — the system is reasonably well-functioning but has noted deficiencies. New programmes require external accreditation until the issues are resolved.
• Refusal — significant shortcomings. The institution cannot establish new programmes, and existing programmes are accredited on a set schedule.

Accreditation panels include a student member and international members, and they probe whether quality processes are lived, not just written. Student course evaluation is one of the most direct pieces of evidence that the "during" part of the cycle functions.

Iceland: the Quality Enhancement Framework

Iceland''s system is enhancement-led. An independent Quality Board, established in 2010 and comprising international experts (renamed the Icelandic Agency for Quality Assurance in 2024), organises and carries out all external quality assurance, operating autonomously from the ministry. Its Quality Enhancement Framework (QEF) integrates internal and external processes into one model, explicitly aligned with the Standards and Guidelines for Quality Assurance in the European Higher Education Area (ESG).

Key features for evidence planning:

• Institutions are reviewed every five years, on a three-year operational plan set by the Ministry of Culture, Innovation and Higher Education. All external evaluation is conducted in English.
• Reviews combine institution-led self-evaluation with external expert panels that include at least one international member and a student representative, plus a site visit; findings are published.
• Under the Higher Education Act, every institution must carry out internal quality assurance — and the enhancement focus means panels want to see that student feedback drives genuine improvement, not just compliance reporting.

Because the QEF is enhancement-led and ESG-aligned, the evidence that scores well is evidence of a learning system: feedback collected, analysed, acted on, and reviewed for whether the action worked.

The shared requirement: a closed, demonstrable loop

Strip both systems to their core and the same demand appears, and it is the same one the ESG / ENQA standards and NVAO make across the EHEA: show the loop closing. Static evaluation data — a folder of 1-to-5 averages and unread free-text comments — is weak evidence because it documents collection without demonstrating analysis or action. Panels in both countries are explicitly looking for whether the system functions in practice.

This is where the design of your evaluation instrument matters for accreditation, not just for teaching. The harder problems are: (1) standardisation, so evidence is comparable across programmes and years; (2) genuine qualitative depth, so "why" is captured, not just "how satisfied"; and (3) traceable action, so you can show a panel the path from a cohort''s feedback to a concrete change to a re-evaluation.

Requirement-to-evidence mapping

The table below maps common requirements in the Danish and Icelandic systems to concrete course-evaluation outputs. The Koji column shows where AI-moderated evaluation produces the artefact directly; it is one evidence source within a triangulated system, not a substitute for peer review, external examiners, or programme-level governance.

Accreditation / QA requirement	What a panel wants to see	Concrete course-evaluation output	How Koji produces it
Systematic, institution-wide quality work (DK criterion; ESG 1.1)	Consistent process across all programmes	Standardized evaluation run every term, comparable across units	Standardized AI-moderated interviews; consistent moderation logic across cohorts
Programmes informed by feedback (DK "during"; QEF enhancement)	Evidence feedback is analysed, not just stored	Thematic analysis of qualitative responses with ranked issues	Automatic thematic analysis across all transcripts, with representative quotes
Closing the loop / acting on feedback (ESG 1.9; QEF)	Documented actions traced to specific feedback	Action log linking themes to changes and owners	Built-in action tracking from theme to decision
Quality culture & student voice (DK; QEF student rep)	Students engaged as partners, depth of voice	Rich, probed qualitative evidence beyond Likert scores	Adaptive follow-up questions that probe vague answers into specifics
Longitudinal improvement (5-year IS cycle, DK re-review)	Trends and whether actions worked	Cohort-over-cohort theme tracking	Longitudinal cohort reporting on recurring vs resolved themes
EU data protection (both, ESG-aligned)	GDPR-compliant handling of student data	EU-hosted, GDPR-documented processing	EU/GDPR data handling

Map your institution''s exact criteria from the current Danish ministerial order and the Quality Board''s QEF documents; the rows above summarise recurring expectations, not verbatim criteria.

Where AI-moderated evaluation helps — and where it does not

Honestly: accreditation is never won on a single instrument. Both the Danish and Icelandic systems judge the whole quality system — governance, programme design, external examining, staff development, and the student voice together. Course evaluation is one strand. What a conversational, AI-moderated tool changes is the quality of that strand: instead of handing a panel a spreadsheet of means and an inbox of comments, you can show standardized, probed, thematically analysed evidence with a documented action trail — exactly the "functions in practice" demonstration both agencies ask for. It does not replace the rest of the system, and any vendor claiming otherwise should be treated with suspicion by a PhD-literate QA team.

Koji''s evaluation engine is the same AI interview technology used for customer and user research on the main Koji platform; the education product applies it to the quality cycle with EU/GDPR data handling. If your current evaluation evidence is thin precisely because it is static and qualitative data goes unread, that is a fixable, instrument-level constraint.

A practical checklist before your next review

1. Can you produce a standardized evaluation report comparable across programmes and years?
2. Is your qualitative evidence analysed into themes, or is it a pile of comments?
3. Can you trace a specific action back to specific student feedback, and forward to a re-evaluation?
4. Do you have longitudinal evidence that actions worked, not just that surveys ran?
5. Is your data handling GDPR-documented and EU-hosted?

If you answered "no" to two or more, the gap is not effort — it is the instrument.

Related Resources

• Turning Student Feedback into ESG / ENQA Accreditation Evidence
• Turning Course Evaluations into NVAO Accreditation Evidence (Netherlands & Flanders)
• Course Evaluation Evidence for Nordic Accreditation (NOKUT, UKÄ & FINEEC)
• Course Evaluation Evidence for Baltic Accreditation: HAKA, AIKA & SKVC
• Programme Review & Revalidation: Turning Course Evaluation into Accreditation Evidence

Next step

Want your Danish or Icelandic review evidence to show a loop that visibly closes? See how Koji turns course evaluation into accreditation-ready evidence — standardized interviews, automatic thematic analysis, and action tracking your panel can follow end to end.