A work-study Bachelor with Studi: my honest review

I did Studi's Full Stack Web Developer Bachelor as a work-study program, while working at SnowPact. Degree validated, defense passed on a project close to my heart. That's the outcome. The road there deserves a few details, the good parts and the less good ones.

Work-study is the right format

Studi also offers self-paced formulas, but I was on the work-study track: a calendar set by the school, alternating class periods and company periods. For someone who learns by doing, it's the right format. A module covered in class could be put to use the same week on a real project, and the problems met at work gave the lessons depth.

Content-wise: written and video lessons on the platform, live sessions I watched regularly (live or as replays), and capstone projects running through the curriculum. This is where I want to insist, because it's my real conviction after this program: you progress by building projects. Not by stacking up videos, not by re-reading a module three times. The curriculum's capstones, the work in the company, personal projects: that's what turns a lesson into a skill.

The defense: Signaleo

The degree is validated through a project portfolio and a defense in front of a jury. I presented Signaleo, an app for reporting issues in public spaces, and I presented it end to end, because it's a project close to my heart. We had started with a code sprint, where I worked on the back end, and picked the name as a team. In a later phase I built the public landing page on the front end. A team effort from start to finish, all the way to deployment.

Defending a real product in front of a jury, with its architecture choices, its trade-offs and its real users, is nothing like presenting a school exercise. If you get the chance to carry a project that matters to you all the way to the defense, take it: the motivation shows, and the jury's questions become a conversation rather than an exam.

AI during the program: yes, but not at the start

The topic is unavoidable in 2026, so let's address it. AI is a formidable tool, and a trap for a beginner. If it writes the code for you while you're learning, you'll never truly know how to read it.

My advice, lived: use it in moderation, and in the right places. To explain code you don't understand, to review your own the way a reviewer would, to rephrase a concept that didn't land. Not to generate the exercise's solution. The friction of the early days (searching, failing, fixing) is precisely where the learning happens. AI works best once you've first learned without it.

What I liked less

My experience, speaking for myself. Others may have lived it differently. First, advisor turnover: my contacts changed several times along the way, and each time you start over from zero. Second, exam questions: it was hard to ask a precise question about exam logistics and get a reliable answer on the first try. Frustrating when the stakes are the diploma. Finally, the follow-up: it's not great. You have to be genuinely autonomous, hunt down information yourself, follow up again and again.

Nothing disqualifying. But if you expect to be hand-held, this is not the format for you.

Verdict

Degree in hand, what I keep fits in three lines: work-study is the right format because it connects lessons to real projects; you progress by building, not by consuming content; and autonomy is not optional, it's the prerequisite. If you check that box and you have a project you care about to defend, the rest follows.