What problem does it solve?
Between acceptance and the first day of class, students face a string of administrative hurdles: financial aid forms, verification documents, immunization records, placement tests, housing and registration. Students without someone to guide them can stall at any one of these steps, and some simply never show up, a pattern known as summer melt. Georgia State University describes it as a problem for at risk students, especially those from urban school districts.
Admissions and student service offices cannot answer thousands of repetitive questions at the moment students ask them, often in the evening and at weekends, and mass emails go unread. The same pattern continues after enrollment: students do not know which office to contact, deadlines pass, and staff spend their time on questions a published policy already answers instead of on the students who need a person.
- Georgia State University, listing Lindsay Page and Ben Castleman's book Summer Melt (2014) among its sources, reports that as many as 20 percent of students from urban school districts who are admitted to college and confirm their intent to enroll never attend any post secondary institution.Reduction of Summer Melt (Internet Archive snapshot) (2014)
How does it work?
- Know each student's open tasks. The assistant reads from the student information system which steps each admitted or current student has still to complete: aid documents, deposits, immunizations, orientation, registration.
- Nudge at the right time. It sends short, personal reminders by text message before each deadline, and short surveys (for example intent to enroll) whose answers flow back to staff.
- Answer questions around the clock. Students reply or ask in their own words; answers come from the institution's approved policies, deadlines and office information.
- Route what needs a person. Questions about a student's own aid package, a crisis, wellbeing or anything the knowledge base does not cover go to the right office with the conversation attached.
- Learn from the questions. Staff review the most common questions and failed answers to fix confusing web pages and processes, not only the bot.
- Audience
- Customer facing
- Autonomy
- Supervised agent
- Adoption
- Early adopters
- Channels
- SMS, Web chat, WhatsApp, Mobile app
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Interactions handled | Not pooled | 81,167 to 200,000 | 2 | 1 organization, 1 vendor |
Value drivers: Inclusion and access, Customer experience, Employee productivity, Lower cost to serve.
Indicative value
A public university with 30,000 students and 6,000 new students a year
USD 25,000 to USD 240,000
Staff time released for student support per year
How this is calculated
Formula: questions * handledShare * minutesPerMessage / 60 * staffCostPerHour. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Student questions and replies per year across admissions and student services questions, messages per year | 100,000 | 200,000 | Editorial assumption. For scale, Georgia State reports more than 200,000 answers to incoming students in one summer, and Mainstay reports 81,167 messages handled in a year by Adelphi University's assistant. |
| Share of messages the assistant handles without staff handledShare, fraction of messages | 0.5 | 0.8 | Editorial assumption, replace with your own data after a first term. |
| Staff minutes per message minutesPerMessage, minutes per message | 1 | 2 | Mainstay's case study for Adelphi University assumes approximately one minute per message; the high value allows for emails and calls that take longer. Editorial assumption, replace with your own. |
| Fully loaded cost of a staff hour staffCostPerHour, USD per hour | 30 | 45 | Editorial assumption, replace with your own cost. |
What it leaves out: Counts staff time only. It leaves out the larger effect that institutions such as Georgia State report, more admitted students actually enrolling, and the cost of the platform, integration and content upkeep.
Who already uses it?
3 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Georgia State University
United States · Education · 2016
Georgia State University combined a new student portal, which guides incoming students through the steps needed before the first day of classes (such as financial aid documents, immunization records, placement exams and class registration), with "Pounce", an AI enhanced chatbot that answers their questions around the clock by text message. The assistant vice president of undergraduate admissions said every interaction was tailored to the specific student's enrollment task. In its first summer (2016) Pounce delivered more than 200,000 answers and the university, with the portal and the chatbot together, reduced summer melt by 22 percent, an additional 324 students in class on the first day. Separately, in a randomized control trial the university saw a four percent overall decrease in the share of confirmed freshmen who did not enroll, and it says those gains came from the students who had access to Pounce. The same executive said the university would otherwise have needed 10 more full time staff to handle the volume of messaging.
- Interactions handled: at least 200,000, first summer of implementation, 2016
"In 2016, during the first summer of implementation, Pounce delivered more than 200,000 answers to questions asked by incoming freshmen, and the university reduced summer melt by 22 percent."
Claimed by: organization
Adelphi University
United States · Education · 2022
Adelphi University launched Adele, a conversational AI assistant on the Mainstay platform, on its website in January 2022 and extended it to two way text messaging for about 6,000 current students in March 2023. Adele sends reminders about academic and financial deadlines, answers questions from a shared knowledge base, enabled generative AI in July 2025 and requests a human from the right office by email when needed. A cross office task force coordinates campaigns, and the university adopted a policy for text messaging in June 2024. The vendor reports 81,167 messages handled by the bot in the past year. Assuming approximately one minute per message, the vendor estimates this at 1,353 staff capacity hours; that is a modelled figure, not a measured saving, so it is not recorded as a metric.
- Interactions handled: 81,167, in the past year, per the case study
"81,167 messages handled by the bot"
Claimed by: vendor
Austin Peay State University
United States · Education · 2017
Austin Peay State University introduced a Mainstay text message chatbot, "The Gov", in 2017 to send incoming students orientation information and nudges, and later interactive surveys. Its first intent to enroll campaign in 2019 received a 40% response rate within a day, which Mainstay's case study says would have taken more than a month by paper survey, and showed staff which students still planned to attend. According to the case study (about 2020), the chatbot was also used for housing, advising and event updates.
No outcome disclosed.
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- Current, owned content per office (admissions, aid, registrar, housing, bursar)
- Each student's open enrollment tasks and deadlines from the student information system
- Mobile numbers with consent to receive text messages
Systems to integrate
- Student information system and admissions CRM
- Messaging (SMS, WhatsApp) and web chat on the institution's site
- Ticketing or case routing to each student service office
- Single sign on for questions about a student's own record
Complexity: Medium
Answering general questions is straightforward. The value comes from personal nudges, which need a clean feed of each student's open tasks from the student information system, consent for text messaging and a working handover to several offices.
- 1
Map the drop off points
List the steps between acceptance and the first day (or between terms) where students stall, and the questions they ask at each. Use last year's data on who did not show up.
- 2
Clean up the content first
Give each office ownership of its answers with a review date. The assistant is only as good as the policies and deadlines behind it.
- 3
Design nudges with the offices
Agree the reminder calendar and wording with admissions, aid and the registrar, keep messages short and personal, and respect quiet hours and opt outs.
- 4
Define the handover
Decide which topics go to which office and within what time, and pass the conversation along so students do not repeat themselves.
- 5
Measure against a comparison group
Where possible, compare enrollment and task completion with students who did not receive the assistant, as Georgia State did in a randomized trial, rather than counting messages alone.
Guardrails
- Answers only from approved institutional content, with a refusal and a route to staff otherwise
- No decisions on admission, aid or placement; the assistant informs and reminds
- Opt in and opt out for text messaging, with quiet hours
- Crisis and wellbeing keywords routed to trained staff immediately
- Personal data minimized in messages and masked in logs
KPIs to instrument
- Enrollment or task completion rate versus a comparison group
- Messages handled without staff and handover rate by topic
- Response rate to reminders and surveys
- Student satisfaction with answers
- Opt out rate from text messaging
Human in the loop
Staff in each office own their content and handle every case that concerns a student's own record, money or wellbeing. A cross office group decides campaign strategy, timing and wording, as the task force Adelphi University set up does. Someone should also review unanswered questions and handovers every week.
Common failure modes
- Nudges that become noise
- Too many or generic messages lead to opt outs, and billing reminders crowd out help. Coordinate campaigns centrally and keep them relevant to each student's open tasks.
- Stale answers
- Deadlines and policies change each term. Give every answer an owner and a review date.
- Bot as a wall
- Students with urgent or personal problems cannot reach a person. Make handover easy and fast, and monitor repeat questions.
- Counting messages, not outcomes
- Message volume says little about whether more students enrolled or completed tasks. Measure outcomes against a comparison group.
What are the risks and rules?
EU AI Act
Depends on design
An assistant that answers questions and sends reminders falls under the transparency duty of Article 50. It becomes high risk under Annex III point 3(a) if it is used to determine access or admission or to assign students to institutions, and under point 3(c) if it assesses the level of education a student will receive. Keep admission and placement decisions with staff.
Rules that apply
Guidance
- Annex III, high risk AI systems (point 3, education and vocational training) (European Union, Europe). Lists AI that determines access or admission to educational institutions as high risk.
- Article 50, transparency obligations for providers and deployers of certain AI systems (European Union, Europe). People must be informed that they are interacting with an AI system unless this is obvious from the context.
- Protecting Student Privacy (US Department of Education, Student Privacy Policy Office, North America). Guidance and resources on student privacy law (FERPA), relevant when the assistant uses education records to personalize messages.
Controls to put in place
- AI disclosure in the first message and on the chat widget
- Consent records for text messaging and an easy opt out
- Content ownership and review dates per office
- Data protection impact assessment covering education records used for personalization
- Weekly review of unanswered questions and handovers
Frequently asked questions
- Can a chatbot really reduce summer melt?
- Georgia State University reports that in the first summer its Pounce chatbot delivered more than 200,000 answers to incoming students, and that a new student portal and Pounce together reduced summer melt by 22 percent, an additional 324 students in class on the first day. Separately, in a randomized control trial the university saw a four percent overall decrease in the share of confirmed freshmen who did not enroll, and it says those gains came from the students who had access to Pounce.
- Do students actually respond to text message nudges and surveys?
- Mainstay's case study for Austin Peay State University reports that the university's first intent to enroll campaign by text, in 2019, received a 40% response rate, and that staff could see which students planned to attend within one day. The vendor says the same task would have taken over a month by paper survey. It gives no enrollment figures, so this shows engagement, not outcomes.
- How much staff time does it save?
- Mainstay's case study for Adelphi University reports 81,167 messages handled by its assistant Adele in the past year, which it estimates at 1,353 staff hours assuming approximately one minute per message. That is the vendor's estimate, not a time study. Georgia State's assistant vice president of undergraduate admissions said the university would have needed 10 more full time staff to handle the volume without Pounce.
- Is an admissions chatbot high risk under the EU AI Act?
- Not if it only informs and reminds; then the transparency duty applies. It is high risk under Annex III point 3(a) if it determines access or admission, so admission decisions should stay with admissions staff.
How to cite this page
Blits.ai AI Use Case Library, "AI assistant for student enrollment and student services", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/student-enrollment-and-services-assistant. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published