Human-Centered Generative AI Governance in Multidisciplinary Research Environments

Amelia Brooks

Human-Centered Generative AI Governance in Multidisciplinary Research Environments

Author(s)	Dr. Amelia Brooks
Country	United States
Abstract	The rapid integration of generative artificial intelligence into scholarly research has created opportunities for literature exploration, analytical assistance, coding, language refinement, visualization, knowledge synthesis, and administrative efficiency. At the same time, its use introduces complex concerns involving factual reliability, fabricated references, confidentiality, intellectual property, bias, authorship accountability, methodological transparency, disciplinary variation, and excessive delegation of scholarly judgment. These challenges become particularly significant in multidisciplinary research environments because acceptable uses, evidentiary standards, confidentiality requirements, and consequences of erroneous outputs vary substantially between health sciences, engineering, social sciences, humanities, and policy-oriented research. This study develops a human-Centered Generative Artificial Intelligence Governance Framework (HCG-AIGF) intended to preserve researcher responsibility while enabling proportionate and transparent use of generative systems throughout the research lifecycle. The study combines structured evidence mapping with design-science methodology and a reproducible scenario-based simulation. Governance principles were synthesized from authoritative and scholarly sources addressing human oversight, transparency, research integrity, confidentiality, verification, accountability, and organizational governance. The resulting framework comprises six operational safeguards: human accountability, disclosure and provenance, independent verification, privacy and confidentiality protection, discipline-sensitive review, and auditable escalation. To evaluate framework behavior without misrepresenting simulated observations as institutional evidence, 1,200 synthetic research tasks were distributed equally across five disciplinary clusters and assessed under fragmented governance and proposed framework conditions. The simulated mean governance compliance index increased from 60.8% under fragmented governance to 93.0% under the proposed framework, while unresolved high-risk events declined from 32.9 to 3.8 per 100 modeled tasks. Improvements appeared across every disciplinary cluster, although differences in risk profiles demonstrated that governance should remain context-sensitive rather than uniformly prescriptive. The study concludes that responsible generative AI governance in research should not be based principally on prohibition or unrestricted adoption. Instead, research institutions require human-centered governance architectures that define permissible assistance, preserve intellectual responsibility, protect confidential information, document technology use, verify consequential outputs, and adapt oversight to disciplinary context.
Keywords	generative artificial intelligence; human-centered governance; research integrity; multidisciplinary research; responsible AI; scholarly accountability; human oversight; research ethics; AI transparency; academic governance
Field	Engineering
Published In	Volume 1, Issue 1, January-February 2020
Published On	2020-01-07

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American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Human-Centered Generative AI Governance in Multidisciplinary Research Environments

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