Course Evaluation Evidence for ASIIN Accreditation: A Buyer's Guide

In short: ASIIN accreditation is built on ESG-compliant criteria, and several of those criteria require you to demonstrate that student feedback is systematically collected, analysed, acted upon, and fed back into programme development. The evidence that satisfies ASIIN reviewers is not a folder of raw survey exports — it is a documented quality loop: standardized data over time, thematic findings, the actions you took, and proof those actions changed something. This guide maps ASIIN and ESG expectations to concrete evaluation outputs, and shows where an AI-native tool like Koji helps and where your existing process may already be enough.

What ASIIN is, briefly

ASIIN is a German-based accreditation agency operating internationally — it has conducted roughly 5,000 accreditations in Germany and more than 700 across over 40 countries since 1999. It accredits degree programmes in engineering, informatics, the natural sciences, mathematics, medicine, economics, and interdisciplinary fields, and it awards the international EUR-ACE label for engineering and the Euro-Inf label for informatics. ASIIN offers both programme accreditation (the ASIIN Quality Seal for individual degree programmes) and institutional / system accreditation (assessing whether an institution's internal quality management system reliably assures good teaching and learning).

Crucially, ASIIN's evaluation criteria are learning-outcomes-oriented and aligned with the European Qualifications Framework and the European Standards and Guidelines (ESG). All criteria used in a Type 1 (ESG) evaluation must be ESG-compliant. That alignment is your roadmap: if your evaluation evidence satisfies the relevant ESG standards, it will speak the language ASIIN reviewers expect.

The ESG standards your evaluation evidence must serve

Three ESG Part 1 standards bear directly on course and student evaluation, and ASIIN's programme criteria operationalize them:

• ESG 1.3 — Student-centred learning, teaching and assessment. You must show that teaching responds to student needs and experience. Evidence: feedback that captures how students actually experience workload, teaching methods, and assessment — not just whether they were satisfied.
• ESG 1.7 — Information management. You must systematically collect, analyse, and use information about your programmes, including student feedback. Evidence: a reliable, repeatable data-collection process and real analysis, not ad-hoc surveys.
• ESG 1.9 — On-going monitoring and periodic review of programmes. Programmes must be regularly reviewed with students involved, and the results must lead to continuous improvement. Evidence: a closed loop — feedback in, decisions out, changes documented, students informed.

ASIIN layers discipline-specific criteria (its Subject-Specific Criteria, SSC 01-14, covering fields from mechanical engineering to life sciences) on top of these general requirements. The evaluation evidence you build should therefore be both standardized across the institution and adaptable to each programme's discipline.

Requirement-to-evidence mapping

ASIIN / ESG requirement	What auditors want to see	Concrete Koji output
ESG 1.3 — Student-centred learning	Evidence student experience shapes teaching and assessment	AI-interview themes on workload, teaching methods, assessment clarity — with the why behind each rating
ESG 1.7 — Information management	A systematic, repeatable collection and analysis process	Standardized AI-moderated interviews each term plus automatic thematic analysis across cohorts
ESG 1.9 — Monitoring and periodic review	Students involved; feedback leads to documented change	Built-in closing-the-loop action tracking from finding to action to status
Learning-outcomes orientation (ASIIN)	Evidence intended outcomes are perceived as achieved	Qualitative evidence on outcome attainment, gathered conversationally
Continuous-improvement / quality cycle	A you-said-we-did trail across review periods	Action log with owner, status, and follow-up captured per programme
Discipline-specific criteria (SSC)	Programme-appropriate, comparable feedback	Configurable interview guides per programme, comparable across terms
Institutional / system accreditation	A functioning, evidenced internal QA system	Longitudinal cohort reporting that demonstrates the system works over time

What reviewers actually look for

ASIIN panels are staffed by academics and practitioners who have seen every variety of evaluation theatre. They are not impressed by high response rates alone, nor by a stack of unread comment exports. What persuades them is a functioning system:

1. Standardization. Is the same, defensible method applied across programmes and across terms, so findings are comparable? Inconsistent, author-by-author surveys undermine this; standardized moderation strengthens it.
2. Genuine analysis. Did someone actually synthesize the qualitative data into themes, or did the comments sit in a spreadsheet? Automatic thematic analysis makes the analysis step demonstrable.
3. Closing the loop. Can you point to a specific change made because of student feedback, and show students were told? This is where most evaluation programs fail, and it is the heart of ESG 1.9. Action tracking is the evidence.
4. Longitudinality. Can you show a trend across cohorts — that a problem was identified, addressed, and measurably improved? One-off surveys cannot; a multi-term dataset can.

Where traditional SET surveys fall short for ASIIN

Standard Student Evaluation of Teaching surveys often satisfy ESG 1.7 on paper (data is collected) while quietly failing ESG 1.9 (nothing visibly changes). The recurring gaps:

• Shallow qualitative data. Open-text boxes produce thin, hard-to-act-on comments, so the why behind a low score is missing — exactly what reviewers probe.
• Manual analysis that does not scale. Hundreds of comments per programme are rarely coded systematically, so the analysis step is weak or absent.
• A broken loop. Feedback is collected and reported, but the documented chain from insight to action to communicated change is incomplete.

How to build the evidence trail with Koji

Koji for Education is designed around the closed loop ASIIN expects. Each term, students complete an AI-moderated conversational interview that probes the reasons behind their experience. Koji performs automatic thematic analysis across the cohort, surfacing the issues that matter for each programme and discipline. Teaching teams record the actions they take in response, and Koji tracks those actions to completion — producing a documented, longitudinal you-said-we-did record. For institutional or system accreditation, that cohort-over-cohort reporting is direct evidence that your internal quality management system functions as required. The same AI interview engine also powers general user and customer research on the main platform at koji.so, so research teams beyond the QA office can reuse the method.

When your existing tools may be enough

In the interest of honesty: ASIIN does not mandate any specific software, and a small, well-run programme can meet the criteria with a solid SET survey, disciplined manual analysis, and a carefully maintained action log. If you have only a handful of programmes, low evaluation volume, and staff who genuinely close the loop by hand, you may not need a new platform — you need to document what you already do. Tools like Koji earn their place when scale, standardization, qualitative depth, and a defensible, automatically maintained evidence trail become hard to sustain manually — which, for most multi-programme institutions facing periodic review, is the realistic situation.

Institutional and system accreditation: the extra evidence bar

Programme accreditation asks whether a single degree meets the criteria. ASIIN's institutional and system accreditation asks something harder: whether your internal quality management system reliably produces good teaching across every programme, term after term, without ASIIN having to check each one. The evidence bar rises accordingly. Reviewers want to see that evaluation is not a heroic effort by a few committed coordinators but an embedded, standardized process with consistent data, consistent analysis, and a consistent route from finding to action. This is precisely where fragmented, author-by-author survey practices struggle — and where a standardized method with central reporting demonstrably helps. Cohort-over-cohort dashboards that show issues identified, actions logged, and outcomes improved are strong evidence that the system, not just individual goodwill, is working.

A practical term-by-term evidence cycle

A defensible ASIIN evidence trail is built in repeatable cycles, not assembled in a panic before a site visit. A workable rhythm looks like this:

1. Collect (each term). Run a standardized evaluation across programmes so data is comparable. Capture the why, not only ratings.
2. Analyse (within weeks). Synthesize qualitative feedback into themes per programme and discipline, so findings are demonstrable rather than buried in exports.
3. Decide and act. Programme teams agree concrete responses to the top themes, with an owner and a deadline.
4. Communicate. Tell students what changed because of their feedback — the you-said-we-did step that ESG 1.9 explicitly expects.
5. Track and review. Log each action to completion and revisit next term to check the issue improved.

Done consistently, this produces exactly the longitudinal, closed-loop record ASIIN reviewers reward — and it makes the eventual self-evaluation report a matter of compiling evidence you already have rather than reconstructing it. Whether you run this cycle in spreadsheets or in a dedicated platform, the discipline of the cycle is what earns the seal.

Related resources

• Course evaluation evidence for German accreditation
• Course evaluation evidence for EUR-ACE engineering accreditation
• Course evaluation evidence for FIBAA accreditation
• Turning student feedback into ESG accreditation evidence
• Course evaluation evidence for AACSB and EQUIS

Next step: Book a Koji for Education demo and see how a single term of AI-moderated interviews maps to your ASIIN and ESG evidence requirements.