This question risks being opinion-based, so I'll try to be really brief with my opinion, then give you a book suggestion. Sometimes it's worth taking a particular approach because it's the approach that a particularly good book takes.
I would agree that Bayesian statistics are more intuitive. The Confidence Interval versus Credible Interval distinction pretty much sums it up: people naturally think in terms of "what is the chance that..." rather than the Confidence Interval approach. The Confidence Interval approach sounds a lot like it's saying the same thing as the Credible Interval except on general principle you can't take the last step from "95% of the time" to "95% chance", which seems very frequentist but you can't do it. It's not inconsistent, just not intuitive.
Balancing that out is the fact that most college courses they will take will use the less-intuitive frequentist approach.
That said I really like the book Statistical Rethinking: A Bayesian Course with Examples in R and Stan by Richard McElreath. It's not cheap, so please read about it and poke around in it on Amazon before you buy. I find it a particularly intuitive approach that takes advantage of the Bayesian approach, and is very hands-on. (And since R and Stan are excellent tools for Bayesian statistics and they're free, it's practical learning.)
EDIT: A couple of comments have mentioned that the book is probably beyond a High Schooler, even with an experienced tutor. So I'll have to place an even bigger caveat: it has a simple approach at the beginning, but ramps up quickly. It's an amazing book, but you really, really would have to poke through it on Amazon to get a feel for its initial assumptions and how quickly it ramps up. Beautiful analogies, great hands-on work in R, incredible flow and organization, but maybe not useful to you.
It assumes a basic knowledge of programming and R (free statistical package), and some exposure to the basics of probability and statistics. It's not random-access and each chapter builds on prior chapters. It starts out very simple, though the difficulty does ramp up in the middle -- it ends on multi-level regression. So you might want to preview some of it at Amazon, and decide if you can easily cover the basics or if it jumps in a bit too far down the road.
EDIT 2: The bottom line of my contribution here and attempt to turn it from pure opinion is that a good textbook may decide which approach you take. I'd prefer a Bayesian approach, and this book does that well, but perhaps at too fast a pace.