Turning Every Comment into Insight: Why coverage and accuracy matter in AI feedback analysis
Student feedback has never been more important, or more abundant. Open-text responses offer rich, nuanced insight into the student experience, but historically they have also presented a major challenge: how do you analyse thousands, or tens of thousands, of comments quickly, consistently and accurately?
With the growing use of AI in feedback analysis, many providers claim to deliver rapid results. But speed alone is not enough. What truly matters is coverage, accuracy, and actionable insight.
The Challenge with AI Analysis: It’s not solely about speed
There is increasing noise in the market around AI tools that promise to reduce analysis time from weeks to minutes. While this sounds impressive, it raises an important question:
What happens to the comments that aren’t processed?
In real-world use, many AI tools struggle to categorise large portions of qualitative data. This results in:
When this happens, institutions are left with only partial insight, limiting the ability to gather the fuller picture and then act with confidence.
A Different Approach: evasys + Student Voice AI
Through the evasys partnership with Student Voice AI, a fundamentally different approach has been taken, one focused on depth, completeness and usability, while still delivering analysis fast. What underpins the performance of the tool is the strength and relevance of the underlying model.
Student Voice AI is not a generic AI summarisation tool. Founded by Dr Stuart Grey SFHEA, an academic who still teaches in UK higher education, Student Voice AI has been built around the realities of institutional reporting, quality enhancement and student experience work.
It is a purpose-built, deterministic machine-learning approach developed specifically for higher education feedback. The model has been trained on multiple years of UK sector datasets, including PTES, PRES, NSS, module evaluation, student experience and welcome surveys from more than 100 institutions.
This means it understands not just language, but context: how students actually express their experiences across different surveys and settings.
How important is sector-specific training for an AI tool?
Sector-specific training matters. Student comments are rarely neat, single-topic statements. One response may refer to teaching quality, assessment design, academic support and workload all at once. Student Voice AI analyses comments at sentence level and maps them to a stable, multi-dimensional category framework: 44 taught-experience categories and 26 research-experience categories, grouped into clear themes.
This approach ensures that comments are consistently mapped to meaningful, sector-relevant themes, giving institutions both breadth and clarity in their analysis, as well as confidence that the outputs genuinely reflect the nuances of the UK student voice.
The result is not just a faster version of manual coding. It is a more complete and consistent evidence base: every valid substantive comment is processed, non-substantive responses are clearly identified, sentiment is allocated consistently and outputs can be explored through evasys dashboards, Excel exports, benchmarking and narrative reporting.
What does this mean in practice?
From the 2025 PTES dataset:
For 2025 NSS dataset submitted by institutions, an average of 98% of all comments with substance were allocated to one or more categories, with all comments with substance allocated sentiment.
This level of coverage is critical. It means institutions are not working with fragments of data, they are working with the full student voice.
In comparison against other AI providers, open comment analysis using evasys and Student Voice AI provides confidence that a high percentage of the comments will be coded. In one instance with another AI analysis provider, only 53% of comments in a dataset were analysed and, of these, almost 30% were coded as “Other”. On sentiment analysis, only 43% of all comments were coded. The result? The institution was unable to do anything with the analysis received.
On the flip side, an institution using evasys and Student Voice AI confirmed a number of key benefits when using the two technologies combined. Firstly, when comparing key themes between previous manual coding and the AI-automated coding within Student Voice AI there was a very high degree of similarity, identifying the same themes and areas of focus at school level. The tool also accurately replicated the key themes identified in manual coding where, for example, NSS comment analysis would usually take 5-6 working days and Student Voice AI completed the same analysis in just 15 minutes.
With the ability to split by a larger number of categories than a manual coding exercise, Student Voice AI facilitates themes to be identified at a much more granular level, providing enhanced value for teams as they seek to make improvements based on the student voice.
All of this is delivered through intuitive, insightful evasys dashboards incorporating drill down features, exportable visuals, compelling reporting and exports to Excel.
From data to actionable insight
Another key differentiator is how insights are delivered. Rather than limiting results to dashboards alone (although, the dashboards are truly excellent 😉), evasys + Student Voice AI can also provide:
This combination ensures that insight is not only accessible to analysts, but also to academic teams, senior leaders and decision-makers.
The future of student feedback analysis
AI is transforming how institutions understand their students, but not all AI solutions are created equal. The future lies in solutions that deliver:
Most institutions already have the student voice. The problem is turning all of it into trustworthy evidence. evasys and Student Voice does that because it combines survey infrastructure, sector-specific categorisation, high coverage, benchmarking and usable reporting.
Through our partnership with Student Voice AI, evasys is proud to be setting a new standard, ensuring that every meaningful student comment contributes to real institutional insight.
Would you like to see what’s possible?
For more information on using AI comment analysis to get the very most from your student feedback, read ‘AI can help providers read and act on the student feedback they never usually get to’ by Daniel Robson from King’s College London and our Academic Lead, Dr Helena Lim, published 10th June 2026 by Wonkhe.
You can also sign up for Wonkhe’s upcoming ‘Closing the Loop’ webinar, where our Managing Director, Bruce Johnson, will be part of a panel having a discussion on building a feedback framework for student success.