How Many Student Inquiries Should a University Expect Per Day?
Daskalos Consulting's own demand model for daily student and admissions inquiries, by institution size.
An institution with under 3,000 students can expect approximately 41 student and admissions inquiries per day. An institution with more than 25,000 students can expect approximately 796, of which 66.7% arrive through digital channels: email, phone, web chat, social media. Based on our own internal performance benchmark, a well-configured AI assistant can independently and without escalation answer approximately 85% of these digital inquiries, proportionally, at a similar rate across every institution size. Below, we show where these figures come from and how they can be applied to a given institution's size.
How many inquiries should an institution expect per day?
| Student headcount | Total daily inquiries | Digital daily inquiries | In-person daily inquiries | Daily administrative time |
|---|---|---|---|---|
| < 3,000 | ~41 | ~28 | ~14 | ~4.1 hours |
| 3,000–10,000 | ~151 | ~101 | ~50 | ~15.1 hours |
| 10,000–25,000 | ~417 | ~278 | ~139 | ~41.5 hours |
| > 25,000 | ~796 | ~531 | ~265 | ~79.4 hours |
These four bands are not an arbitrary division. The estimate scales directly in proportion to student headcount: an institution twice as large can expect roughly twice the daily inquiry volume. "Digital inquiries" cover the email, phone, web chat, and social media channels. "In-person inquiries" cover the remaining cases that require physical presence only.
How much of this can an AI assistant take on?
The "digital daily inquiries" column above shows the load arriving through channels that an AI assistant can, in principle, handle. Based on our own internal performance benchmark, a well-configured AI assistant can independently and without escalation answer approximately 85% of these inquiries. The remaining 15% represents complex, individual cases that are still best directed to a human administrator.
Because the estimate scales proportionally with student headcount, this ratio can be interpreted the same way across every size band. A larger institution has a larger absolute inquiry volume, but the same proportional relief can be expected.
On a monthly and annual scale
| Student headcount | Monthly digital inquiries (via AI assistant) |
|---|---|
| < 3,000 | ~992 |
| 3,000–10,000 | ~3,626 |
| 10,000–25,000 | ~10,001 |
| > 25,000 | ~19,112 |
These are annual average figures. Our earlier research shows that the load is not evenly distributed across the year: respondents identified course registration (83.8%), enrollment (78.4%), the exam period (73.0%), and the admissions period (62.2%) as the times of greatest pressure. An institution may therefore face a daily inquiry volume well above, potentially several times above, the monthly average shown here during peak periods, while falling below it during quieter weeks of the academic year.
The basis of the model
The estimate draws on two sources. Handling time per inquiry is based on the weighted average time from our own survey (n=37), approx. 9 minutes per inquiry, reflecting the proportion of routine, simple, and medium-complexity cases. Daily inquiry volume is based on the measured average monthly conversation volume of a real, public AI assistant, projected onto student headcount, and applied equally to both student- and admissions/prospective-inquiry-type questions.
How does this apply to your own institution?
If you know your own student headcount, selecting the appropriate band above allows you to estimate your own daily workload. The model scales continuously, so an institution of an intermediate size, for example 7,000 students, can also be estimated precisely based on the internal proportions of the 3,000–10,000 band.
If you would like an estimate tailored to your own, precise student headcount, or would like to learn the full methodology behind the model, please feel free to contact us. Ahead of introducing Emily, the AI assistant built for Hungarian higher education, we are happy to prepare a detailed estimate tailored to your institution as well.
About the research: Daskalos Consulting's national survey, based on responses from staff at 37 Hungarian higher education institutions, conducted over 2025–2026, over a period of nearly eight months, supplemented with traffic data measured from a real, public AI assistant. The full methodology is available upon request.