Judging from the Redbook's current framework and content, the authors have built a very complete and forward-looking system of AI general education, with cognitive autonomy as its core goal and a historical positioning benchmarked against the Harvard Redbook, combining theoretical depth with practical applicability.
I strongly endorse the book's central idea. Cognitive autonomy, as the core proposition running through the whole book, goes to the heart of current AI education — at present most training teaches "how to use", and few teach "how to think".
The seven real-world mismatches listed in Chapter 4 cover nearly all the main problems in current AI general education practice. "Instrumentalization" and "ethics as an add-on" are the most prominent: many schools turn AI classes into operation training, and ethics is just one extra lesson at the end with a few slogans, never truly woven into teaching. The book shows a clear understanding of this problem and argues it systematically.
A few questions I care about:
First, how to assess. Section 7.8 of Chapter 7, "Evidence for Assessment: How to Observe the Four Capacities", is only a single subsection and is not fully developed. Section 4.4.6 of Chapter 4 criticizes "superficial assessment" but offers no solution. If this problem is not solved, schools won't know how to examine, education bureaus won't know how to supervise, and in the end we will go back to the old road of testing knowledge points and operational skills. I suggest making the assessment system a chapter of its own, with indicators that can actually be put into practice.
Second, how to teach. Section 4.4.7 of Chapter 4 raises "the tendency toward superficial teacher training", and it is quite right. At present many primary and secondary schools have no full-time AI teachers, and many existing information technology teachers are still finding their own way. How should teachers' own capacity for cognitive autonomy be developed? I suggest adding a chapter on teachers' professional development and a teacher's guide in the appendices, to help grassroots teachers find the right direction for professional growth.
Third, support for lifelong learning "for the whole of society" could be strengthened. The Overview speaks of "serving all citizens and the future intelligent society", but the lifelong learning part in Chapter 16 and the lifelong learning appendix are short compared with those for schools and universities, and lack concrete scenarios. We serve not only students but also parents, communities, government officials, and others, whose need for cognitive autonomy in relation to AI is also pressing — for example, an AI guide for parents. This area needs to be opened up.
All in all, the book's theoretical foundation is very solid. I suggest completing the two areas of assessment and teachers' professional development before publication.