In recent years, choosing a university has become more complex: it’s not enough to “follow your passion,” because enrollment numbers change, labor markets change, and opportunities for international mobility change. In this article we look at what the drop in enrollments means and how to use AI in a practical (and critical) way to truly understand what to study.
Enrollments down in 2026: what’s changing (and why it matters to you)
When we talk aboutuniversity enrollments 2026, many analyses point to a decline of around 10,000 new enrollments compared to the previous year. The point isn’t just the number: thedecline in university enrollments Italytends to amplify differences between degree programs, territories, and student profiles. Some faculties hold steady or grow, others empty out; some cities become more attractive thanks to services and opportunities, others struggle to retain students. And in parallel, thegender disparities in Italian universitiesremain open (or widen): not only in terms of enrollments, but also in the choice of fields (STEM, healthcare, humanities), salary expectations, and access to professional networks.
For you, this means something very concrete: choosing a program becomes more “strategic.” Not in the sense of choosing “what pays the most,” but of evaluating more clearly: your likelihood of completing the program, your chances of working in Italy or abroad, costs and timelines, and how transferable a given profile is across sectors and countries. In a more competitive and unequal context, getting good guidance is a real advantage.
Choosing what to study: 4 concrete criteria beyond “passion”
Passion is a great start, but it’s often generic (“I like psychology,” “I’m interested in economics”). To turn it into a solid choice, try thinking through four criteria. The goal isn’t to find “the perfect answer,” but to reduce the risk of a random choice and dropping out in the first year.
- Real (not ideal) interests: what topics keep you focused for hours? What books, podcasts, or videos do you look up spontaneously?Measure curiosity over time, not one week’s enthusiasm.
- Skills and study style: do you do better with math and problem-solving, or with reading and argumentation? Do you like working in a team or alone? This also includes therisk of dropping out: if you hate the idea of daily exercises, a highly quantitative path could become heavy.
- Costs and time: tuition, rent, transportation, materials, but also “hidden” time (commuting, bureaucracy, internships). Consider whether you can afford a university far from home and whether scholarships or housing exist. Location matters: differences between geographic areas affect costs and your network of opportunities.
- Career outcomes and transferability: don’t ask only “what job will I do?”, but “what marketable skills will I have?”. Considertransferability abroad(language, degree recognition, availability of courses in English, exchanges) and differences between universities: same program name, but quality and network can vary a lot.
If you put these criteria into a table (even a simple one), you get a more honest snapshot: not “what I like,” but “what I can handle, what makes sense for me, what opens doors.” This is where AI can become useful: not to decide for you, but to help you explore more options in less time.
How to use AI to explore programs and career paths: summaries, comparing curricula, and fact-checking
The idea ofchoosing a university with artificial intelligenceworks only if you use a method. AI is excellent at summarizing and comparing texts, but it can get data wrong, invent sources, or reflect bias (for example about roles “suited” to a gender). So: use AI to speed up analysis, and then verify with official sources (university websites, PDF curricula, Almalaurea, calls for applications).
Practical method in 3 steps:
- Break down the program: paste the curriculum and ask for a summary by year/semester, highlighting implicit prerequisites (e.g., “Calculus 1” before “Physics”). Then ask: “What practical skills do I gain from these courses?”.
- Compare alternatives: have it compare two or three universities offering the same program (e.g., Computer Science in different cities). Ask about differences in labs, elective exams, internships, percentage of “hands-on” credits. This helps you see that “same name” doesn’t mean the same path.
- Fact-check career outcomes: ask AI to list 10 typical roles and 10 sectors, but force it to separate “hypotheses” from “data” and to suggest which sources to verify. Then you verify (even just 2–3 key numbers) on reliable sources. If AI can’t cite, treat it as brainstorming, not as truth.
One important tip: when you analyze trends and careers, explicitly ask it to considergender disparities(access, progression, stereotypes) without turning them into self-fulfilling prophecies. They’re meant to prepare and inform you, not to limit you.
Really test whether it’s for you: quizzes, oral-exam simulations, and mini-projects with AI
The most underestimated part of guidance is this: trying it out. Before enrolling, you can simulate a small “typical week” of the first year. AI helps you create realistic materials and measure how you react: do you get bored? do you get lost? do you light up? do you feel like going deeper?
- Targeted quizzes: ask for a 20-question quiz on typical first-year topics (with explained solutions). Do it in 30 minutes and evaluate not only the score, but how heavy the effort felt.
- Oral exam simulation: ask AI to act like a professor and ask you questions of increasing difficulty. Then ask for feedback on clarity, structure, and gaps. It’s a great test for programs with lots of speaking (law, literature, political science) but useful for everyone.
- Mini-projects: choose a “real” task that takes 3–5 hours. Examples: analyze a simple dataset (economics/statistics), write a short literature review (psychology/social sciences), design a thought experiment (philosophy), build a small prototype (computer science/engineering). AI can give you an outline, evaluation criteria, and a checklist.
This approach reduces the “idealization” effect: it shows you the day-to-day work behind the program title. And it often clarifies one thing: you may love a topic, but not love the way it’s studied at university. Better to find out before enrollment than after.
University guidance with StudierAI: from the study planner to the “pre-enrollment” trial
If you want to turn guidance into a guided trial,university guidance with AIbecomes much more effective when you have a single flow: materials, verification, and planning. WithStudierAIyou canstudy degree programs with StudierAIstarting from real content (handouts, syllabi, notes) and turning it into summaries, quizzes, and oral simulations. In practice, you don’t “imagine” what that program is like: you try it.
A simple way to use it for guidance is this: choose 2–3 candidate programs, retrieve the first-semester syllabi, create quizzes and an oral simulation for each, then plan a “taster” week with a planner. At the end, compare results and feelings: perceived difficulty level, consistency, curiosity, and how capable you feel of improving. If you want to try it right away, you canstart for freeand build your trial before enrolling.
In a period when enrollments are shifting and differences between paths matter more, the goal is to make an informed and sustainable decision. AI doesn’t replace your choice, but it can reduce noise and increase the quality of the tests you run on yourself. If you’re interested in understanding the approach and the project, take a look atwho we areand use guidance as it should be: a reasoned choice, not a leap in the dark.
