Responsible Research with AI: A Practical Guide
From Academy of Managment Community (April 15, 2026)
The Academy of Management’s practical guide on responsible AI use sets a standard every researcher should internalize:
AI is an assistant, not a co-author.
The boundaries are clear. Never upload unpublished manuscripts, datasets, or reviewer materials into AI systems.
Privacy and confidentiality are non-negotiable.
All arguments, interpretations, and conclusions remain the scholar’s full responsibility.
AI may support editing or organizing; it must never drive theory-building or analytical judgment.
Transparency is equally essential. Disclose the tool, its purpose, and its scope in your cover letter, acknowledgments, and methods section.
Verify everything AI touches — citations, summaries, logic chains — before submission. Fabricated references and unverified outputs are the researcher’s liability, not the platform’s.
For reviewers, the standard is the same: keep evaluations human, independent, and confidential. AI-assisted reviews compromise the integrity of the peer review process itself.
Scholarly rigor cannot be outsourced. AI raises the floor — it does not replace the mind.
Responsible Research with AI: A Practical Guide
https://journals.aom.org/doi/full/10.5465/amj.2026.4002
Data protection and privacy → “Never upload sensitive materials”
Do not input unpublished manuscripts, datasets, reviewer comments, or proprietary data into AI tools
Use only anonymized or non-sensitive text when necessary
Accountability for content → “You own everything in the paper”
Do not list AI as an author
Personally verify all arguments, analyses, and interpretations before submission
Transparency of AI use → “Disclose clearly and early”
State AI usage in the cover letter + acknowledgment + methods (if relevant)
Specify tool name and purpose (e.g., editing, summarizing, coding support)
Verification and accuracy → “Trust nothing without checking”
Double-check all citations (no fabricated references)
Re-run analyses and confirm logic of AI-generated summaries
Methodological rigor → “Make your process fully visible”
Provide clear descriptions of data, sampling, measures, and analysis steps
Include robustness checks and supplementary materials when needed
Role of AI → “Use AI as assistant, not decision-maker”
Use AI for efficiency (editing, organizing), not for theory building or interpretation
Ensure all key insights come from your scholarly reasoning
For reviewers → “Keep review human and confidential”
Do not upload manuscripts into AI tools
Do not use AI to generate or draft reviews
Evaluate using independent expertise and request clarification when needed
Prof. Dr. Jeonghwan (Jerry) Choi — Editor-in-Coordination, University of Maine at Presque Isle
Jeonghwan (Jerry) Choi, PhD is an Associate Professor of Business at the University of Maine at Presque Isle and Editor-in-Coordination of K-GSP Forum (contact: jeonghwan.choi@gmail.com). With over 25 years of industry and consulting experience, he specializes in leadership development, human resource management, organizational behavior, and social entrepreneurship. His research focuses on workforce resilience, organizational health, and self-directed leadership — bridging rigorous scholarship with practical insight to cultivate leaders who create meaningful, sustainable, and humane organizations .
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