When ChatGPT arrived, many universities’ first reaction was alarm. Students could ask it to write an essay in seconds, and AI-detection tools proved unreliable. Some institutions restricted or banned generative AI outright.
That moment has passed. AI is now part of how students study and how employers work, so most universities have moved from banning it to deciding how to use it well. The question for faculty and leadership is no longer whether students will use AI, but how to use it to improve learning and prepare students for work.
The shift also changes what universities need to teach. When AI can produce a written answer instantly, the skills that matter more are the ones it can’t do for students: critical thinking, judgment, and communicating in person, in interviews, presentations, negotiations, and difficult conversations.
This guide covers the benefits of AI in higher education, real examples, what our data from 24,913 student practice sessions shows, and how to introduce AI responsibly.
Benefits of AI in higher education
For students
- Personalized learning. AI adapts explanations, practice and pace to each student, rather than one approach for a whole lecture hall.
- Instant answers and feedback. Students get help at the moment they need it, including shy students who might not ask in class.
- Practice for skills that need repetition. AI roleplays let students rehearse interviews, presentations and professional conversations as often as they need, with feedback each time.
- Spotting learning gaps. AI can analyze a student’s work and highlight what they haven’t yet understood.
- Accessibility. Support in more languages, at any time, and in formats that suit neurodivergent students and those studying remotely.
- Readiness for work. Students who learn to use AI well, and to check it critically, are better prepared for employers who expect both.
For faculty and staff
- Time saved on routine tasks such as drafting materials, first-pass feedback and administration, so more time goes to teaching and mentoring.
- Better insight into how students are progressing across a cohort, so interventions happen earlier.
- Scale for one-to-one activities. Mock interviews, presentation rehearsals and feedback conversations used to depend on staff time. AI makes them available to every student.
- More engaging teaching, through simulations, interactive exercises and immersive formats such as VR.
Examples of AI in higher education
| Area | How AI is used | Example |
|---|---|---|
| Tutoring and study support | AI tutors answer questions and explain concepts step by step | Khan Academy’s AI tutor; university-licensed AI assistants |
| Language learning | Conversation practice with AI characters, with feedback | Duolingo’s AI roleplay features |
| Career services and employability | AI mock interviews built on a real job description and the student’s resume | Career services running AI mock interviews before recruitment season |
| Business schools | Practice negotiations, pitches, client meetings and leadership conversations with AI roleplays | Business schools embedding AI and VR practice in compulsory modules |
| Healthcare education | Nursing and medical students practice patient conversations, such as delivering difficult news | Clinical communication roleplays before placements and exams |
| Teacher education | Trainee teachers practice conversations with parents and pupils | Roleplays built by education faculties |
| Assessment and feedback | First-pass feedback on presentations, speaking and written work | AI scoring of practice presentations against set criteria |
| Admissions | Practice for admissions and scholarship interviews | Higher-education interview roleplays |
Many of these examples come from a single trend: universities using AI to give every student practice that used to depend on a scarce member of staff.
What our data shows about AI practice at universities
In 2026, students at 155 universities and colleges completed 25,000 AI roleplay sessions on VirtualSpeech. A few patterns stand out:
- Students practice the conversations that matter for work. A quarter of sessions were leadership, feedback and teamwork conversations, and 22% were job or admissions interviews. Presentations, pitches and debates made up another 12%.
- Universities build their own scenarios. About a third of student sessions were on scenarios institutions created themselves, tailored to their courses.
- AI practice isn’t only in English. 41% of student sessions were in another language.
- Practice improves performance. When students repeated a scenario, 66% scored higher on their second attempt. Students who practiced the same scenario five or more times went from an average of 5.7 out of 10 on their first try to a best score of 7.4.
- But most stop early. The typical student account ran just two sessions. Programs that assign practice, rather than offering it as optional, get far more use.
- Students struggle most with self-presentation. Across all scenarios, showing the impact of their actions averaged 4.9 out of 10, and in interviews, knowing the organization averaged just 2.7. See our report on what students get wrong in job interviews.
How to introduce AI responsibly
- Set a clear policy, covering when students may use AI, how they should disclose it, and how assessment adapts. Involve faculty and students in writing it.
- Redesign assessment where needed. Put more weight on in-class work, oral presentations, vivas and practical tasks that show what a student can actually do.
- Start with clear, low-risk wins: practice and feedback tools that help students prepare, rather than tools that make decisions about them.
- Build AI into the curriculum, not just as an optional extra. Optional tools reach few students.
- Teach AI literacy: how to use AI well, check its output, and recognize its limits and biases.
- Check privacy, security, and fairness. Ask vendors where student data is stored, who can see it, and how scoring has been tested across accents and languages.
- Plan for regulation. Under the EU AI Act, AI that evaluates learning outcomes or steers students’ learning is classed as high-risk from December 2027. Practice tools that only give developmental feedback carry less risk than tools whose results go on a student record, but take advice for your own context.
- Measure the results, in skills and outcomes, not just usage.
Frequently asked questions
How is AI used in higher education?
For tutoring and study support, personalized feedback, practice roleplays for interviews and professional conversations, course content creation, learning analytics and administration.
What are the benefits of AI in education?
Personalized learning, instant feedback, practice at scale, earlier insight into struggling students, better accessibility, and time saved for faculty.
Should universities ban AI?
Most have moved away from bans. Detection tools are unreliable, and students will use AI at work, so the focus has shifted to clear policies, redesigned assessment and teaching students to use AI well.
Does AI practice improve students’ skills?
In our 2026 data, 66% of students who repeated a scenario scored higher on their second attempt, and students who practiced five or more times raised their best score from 5.7 to 7.4 out of 10.
How we did this analysis
We analyzed 24,913 AI roleplay sessions completed by students at 155 universities and colleges on VirtualSpeech, from January 1 to October 13, 2026. Improvement figures use only students with their own account, because some institutions share logins on VR headsets. We excluded anonymous free-trial sessions, internal and test accounts, and sessions with no valid score. Scores come from AI, not human assessors. All data was anonymized and aggregated.
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