Analysis and Prediction of “AI + Education” Attention Based on Baidu Index—Taking Guizhou Province as an Example

Round 1

Reviewer 1 Report

The paper is relevant and interesting.

I would suggest to improve the explanation of the 2017-2018-2019 sequence of Figure 2 and to what extend is relevant or irrelevant to the conclusions. 

Also, a map and a geographical analysis of point (2)  would help to improve conclusions.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The work is organized in a preliminary work and a subsequent analysis. The preliminary work analyzes the keywords "artificial intelligence + education" in Baidu from January 1, 2013 to October 31, 2020 from a specific Chinese region (Guizhou Province and around) populated by minorities. Minorities for China are a very relevant treasure but by nature underrepresented and any work on them should be welcome and is of relevant interest for international research. The work significance lays in this user target.
The subsequent analysis uses national economic data and student data of each city of the province from the Annual Statistical Yearbook released by the Bureau of Statistics of Guizhou Province, and the national financial education funds from the Statistical Yearbook released by the National Bureau of Statistics.

The paper is well presented but requires an extensive review of the English language. Besides seeking help from a mother language, I suggest www.deepl.com and www.grammarly.com as useful web-based free tools, if available in the authors' country.

The data, that were analyzed with different measures, show the trend in interest in the topic of artificial intelligence associated with education in the province compared to the nation as a whole.

Authors properly discuss the analysis and try to forecast the short-term evolution with the ARIMA model, comparing the predictions with data from 2016 to 2020 as a knowledge base.

Some figures like figure 8 present the Legenda over the data and it would be better to fix it.

Statistical analysis, models and methods are correctly used. 

The reference list has a serious geographical bias that must be corrected: it presents only Asian research works, while it should analyse the global State of the Art. Authors should add relevant recent articles using the same measures, methods, and similar studies balancing them, including at least Russian, European, Indian, and American studies.

We can suggest some readings, which are only suggestions and not enough themselves:

About web-based keyword analysis:

• Valentina Franzoni, Alfredo Milani: A Semantic Comparison of Clustering Algorithms for the Evaluation of Web-Based Similarity Measures. ICCSA (5) 2016: 438-452
• James Pustejovsky, Sabine Bergler, Peter G. Anick: Lexical semantic techniques for corpus analysis. Comput. Linguistics 19(2): 331-358 (1993)
• Daria Maltseva, Vladimir Batagelj: Towards a systematic description of the field using keyword analysis: main topics in social networks. Scientometrics 123(1): 357-382 (2020)

About AI in Education:

• Daniel Schiff: Out of the laboratory and into the classroom: the future of artificial intelligence in education. AI Soc. 36(1): 331-348 (2021)
• Todd W. Neller: AI education matters: 2022 EAAI mentored undergraduate research challenge: AI-assisted game design. AI Matters 6(3): 8-10 (2020)
• André Renz, Swathi Krishnaraja, Elisa Gronau: Demystification of Artificial Intelligence in Education - How much AI is really in the Educational Technology? Int. J. Learn. Anal. Artif. Intell. Educ. 2(1) (2020)

 

 

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Authors present a study where the joined consideration of AI (Artificial Intelligence) + Education is considered. It is quite interesting to take into account how the relationship between these concepts can be reflected as an internet joined search, in order to indicate that common people consider the importance of AI at the education process. This idea was not significant 10 years ago, but nowadays is quite important to consider how the common people are concerned with AI at real life and in such a way it could constitute a common solution for the problems. 

One question that has to be solved better is the explanation of the choice of the chinese region and the possibility that internet make produce a reduction of differences between regions (in China and all over the world). The prediction process is presented in a solid way but it could be interesting that authors make a most proof discussion about the selected temporal limit (why don't they consider a larger interval?) or about the region size.

Anyway, the idea is quite interesting, but with this result, which concrete actions would be desirable to take for the institutions? Any idea could be wellcome.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf