Nathan,

> Admittedly, our abstract uses a colloquial definition of "AI" as the
> intended audience is K-12 educators who may not be technical experts
> in the field. The nuance you helpfully described would be difficult to
> fit into a 150 word abstract (and might even confuse the non-technical
> reader). Hence, we provide the following clarification early in the
> introduction:

Thanks for the rapid response. The concerns I raised had nothing to
do with "nuance", but rather with an unreasonably narrow definition
of AI in the abstract that will reinforce popular myths.

> "In general terms, AI refers to making computers do processes that
> humans normally do. Similarly, ML is a major subset of AI that
> involves computers learning from data and experience to perform a
> task. Furthermore, DL is a subset of ML that uses artificial neural
> networks, which are currently the mainstream of AI research (1,2).
> In this paper, we loosely refer to AI, ML, and DL as AI although it is
> important to know their nuances."

I'm glad that you expand the definition of AI in the introduction and
I hope you'll be able to do that in the abstract, too. But I would
question whether deep learning is currently the "mainstream".

Also, I encourage you to remove needless abbreviations. AI has been
established since the 1960s, but "ML" is recent and "DL" is confusing
because it also refers to the older area of "description logics".

> With regards to the scope of this article, we argue that teaching AI
> principles (particularly the supervised/self-supervised variety) can
> be creatively rewarding for students and educators. Indeed, we focus
> on machine/deep learning (as we make clear in the article) since
> many practical applications benefit from these tools, which is central
> to our argument.

Machine learning for classification is arguably the least exciting area
of AI research. Other branches focus on abilities that distinguish
humans from dogs and cats, such as problem solving and reasoning.

It's true that many successful applications focus on classification tasks.
But that also held for early applications of machine learning and even
expert systems, which implies that you should cover them as well.

Best, -Pat