%0 Conference Paper %1 ls_leimeister %A Feind, Eileen %A Leimeister, Jan Marco %B Academy of Management Annual Meeting (AOM) %C Philadelphia, Pennsylvania %D forthcoming %K AI GenAI itegpub journals peer_review pub_jml review_process %T Has AI reviewed this? A state-of-the-art of integrating Generative Artificial Intelligence in Review Proces %X Generative artificial intelligence (GenAI) can significantly improve the scope and depth of reviews and editorial tasks. However, its use raises fundamental questions about the methodology, quality and ethical impact for reviewing. While prior research, especially in the Information Systems (IS) field, has extensively explored the capabilities and limitations of GenAI, there remains a gap in understanding its implications for authors, reviewers, and editors. In response, this paper focuses on analyzing what reviewing tasks should GenAI be able to do, regardless of its current technical capabilities. First, we analyze the state of the art of review processes and associated quality standards in Management and IS, as defined by leading journals. We then synthesized a unified process and a set of standards that not only supports a comprehensive understanding of existing review processes but also serves as a basic framework for potential GenAI integration. Based on ethical, methodological, and quality criteria we outline specific scenarios for incorporating GenAI into this framework and identify situations in which its use may be inappropriate, considering guidelines from publishers, journals and scientific bodies. Finally, we outline role-specific guidelines for reviewers, authors and editors including acceptable and unacceptable practices for using GenAI in reviewing processes.

@inproceedings{ls_leimeister, abstract = {Generative artificial intelligence (GenAI) can significantly improve the scope and depth of reviews and editorial tasks. However, its use raises fundamental questions about the methodology, quality and ethical impact for reviewing. While prior research, especially in the Information Systems (IS) field, has extensively explored the capabilities and limitations of GenAI, there remains a gap in understanding its implications for authors, reviewers, and editors. In response, this paper focuses on analyzing what reviewing tasks should GenAI be able to do, regardless of its current technical capabilities. First, we analyze the state of the art of review processes and associated quality standards in Management and IS, as defined by leading journals. We then synthesized a unified process and a set of standards that not only supports a comprehensive understanding of existing review processes but also serves as a basic framework for potential GenAI integration. Based on ethical, methodological, and quality criteria we outline specific scenarios for incorporating GenAI into this framework and identify situations in which its use may be inappropriate, considering guidelines from publishers, journals and scientific bodies. Finally, we outline role-specific guidelines for reviewers, authors and editors including acceptable and unacceptable practices for using GenAI in reviewing processes.}, added-at = {2026-08-05T13:20:09.000+0200}, address = {Philadelphia, Pennsylvania}, author = {Feind, Eileen and Leimeister, Jan Marco}, biburl = {https://www.bibsonomy.org/bibtex/2e820d688387647a09d87f74da27d50bc/ls_leimeister}, booktitle = {Academy of Management Annual Meeting (AOM)}, eventdate = {31-04 Aug 2026}, eventtitle = {Academy of Management Annual Meeting (AOM)}, interhash = {f62a160027101be47ece61669c261743}, intrahash = {e820d688387647a09d87f74da27d50bc}, keywords = {AI GenAI itegpub journals peer_review pub_jml review_process}, timestamp = {2026-08-05T13:20:09.000+0200}, title = {Has AI reviewed this? A state-of-the-art of integrating Generative Artificial Intelligence in Review Proces}, venue = {Philadelphia, Pennsylvania}, year = {forthcoming} }