AI use in accreditation can help, but we still need humans

Artificial intelligence is increasingly present in higher education, including in the field of study programme accreditation. From organising large datasets to generating preliminary summaries, AI promises efficiency, speed and convenience. However, when it comes to preparing self-assessment reports for accreditation, a crucial principle must be emphasised: AI can assist in presenting evidence, but it cannot generate evidence itself.

Study programme accreditation is fundamentally evidence-based. Institutions must demonstrate compliance with defined quality standards through verifiable documentation: course syllabi, student feedback, research outputs, governance structures and more.

AI can help structure these materials, highlight patterns or point out potential gaps. Yet, no algorithm can replace the requirement for solid evidence. A report polished by AI, without substantiated data behind it, is merely an attractive format, not a guarantee of quality.

Accreditation also relies on human reflection and dialogue. Self-assessment is not only about collecting documents; it is a process in which faculty, students and administrators critically assess practices and engage in quality culture.

AI may summarise data or suggest improvements, but it cannot replicate the insights, judgment or discussions that emerge during site visits and peer review meetings. True quality culture requires active engagement and critical thinking elements that cannot be automated.

At the same time, AI should not be dismissed. Used wisely, it can significantly support institutions in self-assessment. For example, AI can detect inconsistencies in documentation before submission, analyse large datasets on student outcomes or help simulate scenarios to anticipate accreditation risks. In these ways, AI functions as a valuable tool, one that enhances efficiency and clarity while the underlying evidence remains human-generated.

To illustrate, consider a university preparing for programme accreditation. Its faculty members collect years of student evaluation data, often thousands of survey responses across different semesters. Traditionally, this data might be sampled selectively due to time constraints. With AI, however, the institution can process the entire dataset, identifying recurring themes in student satisfaction, pinpointing course-level issues and comparing results across cohorts.

The result is not only a more comprehensive analysis, but also a stronger foundation for reflection. Yet the insight still requires human interpretation: faculty must ask why certain issues persist and how they can be addressed.

Another example comes from curriculum review. An institution may wish to evaluate the alignment between intended learning outcomes and assessment methods across multiple courses. AI tools can quickly scan syllabi, mapping learning outcomes against exams, projects and assignments. Gaps such as an outcome that is declared but not properly assessed can be flagged automatically. This saves time and reduces oversight, but the decision about how to close those gaps remains an academic judgment.

Dangers of over-reliance

The real challenge, therefore, is not access to AI but its responsible use. Access today is relatively easy and affordable; the true difference lies in how institutions apply these tools.

A university that integrates AI thoughtfully, checking consistency, highlighting trends and supporting reflection will benefit. Another that uses the same tools to cut corners, generate generic text or avoid meaningful discussion risks undermining its credibility. In other words, the divide is not technological but cultural: between those who see AI as a partner in reflection and those who treat it as a shortcut.

Over-reliance on AI brings additional risks. Data privacy is one concern, especially if sensitive student records are uploaded to external platforms without proper safeguards. Uniformity of style is another: if too many institutions rely on similar AI systems, self-assessment reports may begin to look alike, reducing the sense of institutional identity and authenticity.

Furthermore, excessive dependence on AI could weaken internal engagement. Faculty and students may feel detached from the accreditation process if much of the work appears to be outsourced to algorithms, undermining the very culture of quality the process seeks to strengthen.

It is also worth noting that international accreditation bodies are increasingly aware of AI's role. Review panels are trained to recognise the difference between authentic, evidence-based reflection and text that merely looks polished. A report filled with elegant AI-generated phrasing but lacking verifiable data will not meet standards. On the contrary, it may damage the institution's reputation if reviewers suspect over-reliance on AI.

The credibility of accreditation lies not in eloquent wording but in demonstrated evidence and integrity. Therefore, institutions should treat AI as a support mechanism, not as an author. 

Institutional policies

Faculty and administrators remain the primary actors who provide the substance of self-assessment: the evidence, the analysis and the commitment to improvement. AI can help them communicate this material more effectively, but it cannot replace the intellectual and ethical responsibility involved in self-assessment.

In practice, this means developing institutional policies for AI use in accreditation processes. Universities could establish guidelines on how AI tools may assist, for example, in formatting documents, identifying gaps or cross-checking consistency while making clear that evidence and analysis must originate from the academic community itself. Transparency about AI's role will also be important in maintaining the trust of external reviewers.

Looking ahead, the role of AI in accreditation is likely to grow, but its limits will remain clear. One potential future lies in continuous quality monitoring. Instead of waiting for accreditation cycles every two or six years, AI could help track key indicators annually, alerting institutions early to issues in student outcomes, faculty workload or research productivity.

Another possibility is international benchmarking: AI could allow institutions to compare themselves more easily with peers abroad, promoting transparency and mutual learning. These are promising developments, but they will succeed only if embedded in a culture of responsibility and academic freedom.

In conclusion, AI has an important role in study programme accreditation, particularly in the preparation and presentation of self-assessment reports. However, the fundamental principle remains: accreditation is about presenting evidence of quality, not replacing it. AI can help organise and communicate that evidence effectively, but it cannot create it.

The future of accreditation lies in the strategic combination of human expertise and AI tools, where humans ensure integrity and AI enhances efficiency.

https://www.universityworldnews.com/post.php?story=20250923143814369