Does Artificial Intelligence Promote or Inhibit On-the-Job Learning? Human Reactions to AI at Work
Round 1
Reviewer 1 Report
The manuscript contains interesting insight into the effect of AI on on-the-job learning, especially when the authors addressed the heterogeneity effects. Nevertheless, this manuscript can be further improved based on the following suggestions, as found in the attachment.
Comments for author File: 
Comments.pdf
Author Response
Dear Reviewer:
We would like to express our sincere gratitude and appreciation to you for your valuable comments that allowed us to greatly improve the quality of this manuscript. We agree with all your comments and corrected the manuscript point by point accordingly. Considerable efforts have been made to take into account all the helpful suggestions. Meanwhile, we asked a professional English editor to conduct a careful proofreading and entirely edit the manuscript for publication. We hope that these revisions would facilitate the decision on the publication of this paper in the journal. In any case, we are open to any further comments on this manuscript.
Responses to the specific comments of the reviewer are as follows. The reviewers’ comments are in black text and the responses in red. In the revised manuscript, we use the “Track Changes” function, such that any changes can be easily viewed by the editor and reviewer.
Please find the responses in the attached file.
Many thanks!
Sincere regards,
All the Authors
Author Response File: Author Response.pdf
Reviewer 2 Report
1. What is the main question addressed by the research?
Authors consider the impact of AI at work. Six hypotheses are proposed concerning three aspects of AI’s influence on on-the-job learning (Section 2):
Hypothesis 1a: The pessimistic expectations caused by AI have a stimulation effect and thus promote on-the-job learning.
Hypothesis 1b: The pessimistic expectations caused by AI have a burnout effect and 137 thus inhibit on-the-job learning.
Hypothesis 2a: AI’s complementarity and cost-saving effects increase income and thus promote on-the-job learning.
Hypothesis 2b: AI’s replacement and mismatch effects reduce income and thus inhibit on-the-job learning.
Hypothesis 3a: AI’s deskilling effect increases working hours and thus inhibits on-the-job learning.
Hypothesis 3b: AI’s productivity and replacement effects reduce working hours and thus promote on-the-job learning.
2. Do you consider the topic original or relevant in the field? Does it address a specific gap in the field?
This paper’s subject is interesting and relevant, it agrees with the journal’s subject.
3. What does it add to the subject area compared with other published material?
These hypotheses have been evaluated and outputs have been introduced and discussed. These outputs are interesting (Section 4). As the authors declare in the abstract “In the context of the fourth technological revolution driving forward the intelligent transformation, findings of this paper have important implications for enterprises to better understand employee behaviors, as well as to promote them to acquire new skills to achieve better human-AI teaming.”
4. What specific improvements should the authors consider regarding the methodology? What further controls should be considered?
I’d like to recommend considering as a possible alternative methodology for the analysis of human factors, in particular, the impact of AI on human activities and further improve their skills through on-the-job learning to enhance competitiveness
5. Are the conclusions consistent with the evidence and arguments presented and do they address the main question posed?
The paper has a good and clear presentation. The methodology for the analysis is acceptable. I’d like to recommend considering in the conclusion some alternative approaches for the evaluation. As one of them can be human reliability analysis, which allows evaluation of causes and impact of human error https://www.mdpi.com/2073-8994/12/1/93
6. Are the references appropriate?
I’d like to add to the analysis of alternative methodology study(s) in human factor analysis, in particular, human reliability analysis.
7. Please include any additional comments on the tables and figures.
All figures and tables have reflected the results specifically and help in the understanding of the paper’s context.
Author Response
Dear Reviewer:
We would like to express our sincere gratitude and appreciation to you for your valuable comments that allowed us to greatly improve the quality of this manuscript. We agree with all your comments and corrected the manuscript point by point accordingly. Considerable efforts have been made to take into account all the helpful suggestions. We hope that these revisions would facilitate the decision on the publication of this paper in the journal. In any case, we are open to any further comments on this manuscript.
Responses to the specific comments of the reviewer are as follows. The reviewers’ comments are in black text and the responses in red. In the revised manuscript, we use the “Track Changes” function, such that any changes can be easily viewed by the editor and reviewer.
Please find the responses in the attached file.
Many thanks!
Sincere regards,
All the Authors
Author Response File: Author Response.pdf
