The author claims to have "sufficient evidence", but the robustness of the study depends on the duration of the research and the sample size. A two-year investigation involving 234 students was superior to a 12-week investigation involving only 83 students, but it still did not constitute a large-scale study. The research results may not be applicable to other universities, other countries, or different programming languages. The author has acknowledged this limitation in the section on limitations, but the conclusion sounds more certain than what the actual evidence supports.

The author uses the Self-Determination Theory (SDT) to explain why the use of artificial intelligence programming can enhance learning motivation. According to SDT, students need autonomy, competence, and a sense of belonging to generate intrinsic motivation. The AI assistant supports autonomy (students can explore independently) and competence (confidence is gained through successful interactions). This theoretical framework helps explain the mechanism behind the quantitative results - not only does artificial intelligence improve performance, but it also meets basic psychological needs. This makes an important contribution to the research.

The authors concluded that artificial intelligence has not yet fully replicated human social emotional collaboration. But is this a limitation of current artificial intelligence technology, or a fundamental limitation of artificial intelligence itself? As artificial intelligence becomes increasingly advanced in areas such as speech recognition, emotion recognition, and memory functions, will it eventually reach a level comparable to human social existence? This study cannot answer this question. However, it raises a larger issue: Should artificial intelligence attempt to replace human social interactions, or should it focus on making up for its deficiencies?

The author believes that artificial intelligence can alleviate programming anxiety because it is "non-judgmental", allowing students to make mistakes without worrying about social evaluations. This aligns with the research of Guo et al. (2026), where one student (S15) pointed out that teachers cannot provide one-on-one attention to each student, while artificial intelligence can do so. Both studies point to the same underlying mechanism: artificial intelligence provides a low-stress, readily available support system, thereby reducing students' psychological burdens. The difference lies in that Fan et al. focused on the alleviation of anxiety, while Guo et al. focused on personalized scaffolding support.

The author found that compared to solo programming, pair programming assisted by artificial intelligence significantly enhanced the sense of collaboration and social presence. But how did the artificial intelligence partner create this sense of social existence? Artificial intelligence has no emotions, no shared experiences, and no relationships. I want to understand the underlying mechanism - was it because the students personified the artificial intelligence, or because the responses from the artificial intelligence felt like a conversation?

The author employed the forward-backward translation method to adapt the questionnaire to a version suitable for the Chinese context. This is a rigorous methodological choice that helps enhance the credibility of the research. Many cross-cultural studies simply translate the questionnaire without verifying its equivalence, which may lead to measurement errors. By inviting bilingual experts to conduct the backward translation, the author ensured that the questionnaire met the requirements in both language and culture. This detail is often overlooked by many studies.

This study employed three sets of designs: artificial intelligence-assisted dual programming, human-to-human pair programming, and individual programming. This design is highly scientific because it separates two variables - collaboration (pair programming vs. individual programming) and artificial intelligence assistance (AI vs. no AI) - respectively. By comparing these three scenarios, the authors can determine whether the advantage of artificial intelligence stems from the technology itself or from the collaborative factors. Therefore, the research results are more profound and layered than a simple "with AI vs. without AI" comparison.

This is in line with the research conducted by Guo et al. (2026), who only studied an 8-week intervention program. While Fan et al.'s two-year design is more powerful in establishing long-term effects, this also raises a question: If artificial intelligence technology changes so rapidly (GPT-3.5 → Claude 3), and when AI itself is a constantly evolving target, can any research truly capture "the impact of artificial intelligence"?

The author describes artificial intelligence as a "collaborator in the learning process" in higher education. But what about the primary school stage? If preschool children use artificial intelligence before mastering basic skills, will this promote or hinder their learning? This is a developmental issue that has not been addressed in research yet. Future studies should explore how age affects the effectiveness of artificial intelligence.

The author mentioned "cognitive substitution" - that is, students bypassing deep thinking. They studied 15-year-old high school students. But what about younger students? If children start using artificial intelligence at the age of 8 or 10, could they possibly develop the habit of deep thinking? The author did not address the answer to this question and it requires long-term research to determine whether early reliance on artificial intelligence will hinder cognitive development.

This study used Python for text programming. However, Python is renowned for its concise syntax and readability, and is often recommended as the first programming language. Do these findings also apply to Java or C? The author believes that artificial intelligence can alleviate cognitive burden, but if the programming language itself is more difficult, artificial intelligence may not be capable.

The student's quote reveals a real problem in the field of education: teachers are unable to provide individualized attention to each student. Could artificial intelligence also serve as a solution for other subjects? This raises a question: Should artificial intelligence replace the attention of teachers, or should it allow teachers to free up time for more in-depth interpersonal interactions?

Guo et al. found a relatively large effect size, but their sample consisted only of 83 students from one high school. The study by Fan et al. (2025) involved 234 students from a university, with a similarly limited sample size. Both of these studies share similar limitations in terms of generalizability. This indicates a pattern in the research on AI-assisted programming: small, single-site studies have dominated the field.

I'm still not quite clear about what the Bebras CT challenge is. What types of questions does it contain? Are they multiple-choice questions, programming questions, or something else? The author should provide a clearer explanation for readers who are not familiar with CT assessment tools on this point.

This research was conducted in China, but the Chinese education system differs significantly from those in other countries. Can these findings be generalized to other cultural contexts?

Yilmaz and Karaoglan Yilmaz (2023a) conducted a study that yielded some positive results, but their research only lasted for 5 weeks. Was this sufficient time to observe significant changes in computational thinking? The study by Guo et al. lasted for 12 weeks and seems to be more convincing.

This assumes that students will actually utilize the released cognitive resources for higher-level thinking. But what if they simply use artificial intelligence to completely avoid thinking?

The author claims that the complexity of the grammar is the fundamental cause of frustration. Has the author procided any data on how many students are truly struggling woth grammer and other issues?

This sample size(83) is small. Can these findings be generalizede to situations outside this specific context?