Menavigasi Realitas Akademik Baru: Tata Kelola dan Etika AI Adaptif dalam Penerbitan Ilmiah Global - Herie Saksono (Research and Development Agency Ministry of Home Affairs, 2025)
The rapid proliferation of generative artificial intelligence (AI) and Large Language Models (LLMs) has fundamentally disrupted the landscape of global scientific publishing and public administration research. This disruption presents a problem characterized by the dichotomy between AI as an essential academic productivity catalyst and a significant threat to scientific integrity. This study addresses three specific research questions: (1) the operational efficacy and ethical risks of AI in scientific writing, (2) the evolution of editorial policies, and (3) the formulation of adaptive AI governance in decentralized public administration. The objective of the research is to critically analyze the transformation of scientific publications, identify emerging ethical and methodological risks, and develop an adaptive conceptual framework for AI governance. Utilizing a qualitative approach, the data collection technique involves Library Research by extracting and systematically synthesizing 101 contemporary high-impact literature (2018–2025) indexed in Scopus Q1 and Web of Science. The data analysis technique utilizes thematic analysis to map cross-disciplinary literature patterns, while data interpretation is operationalized through Critical Discourse Analysis (CDA) and cross-validity triangulation of institutional policies. Results and discussions reveal that AI significantly bridges cross-cultural linguistic barriers and accelerates productivity. However, the research findings confirm that AI simultaneously introduces unprecedented epistemic risks (algorithmic hallucinations), ethical biases, and legal accountability voids regarding authorship and peer-review evaluations. This study provides scientific novelty by reconstructing this paradigm and proposing the Human-AI Cognitive Synergy framework. It is concluded that there is an urgent need for adaptive AI governance within public institutions to regulate machine-assisted research without stifling innovation. Therefore, the study offers actionable recommendations emphasizing critical human oversight (human-in-the-loop validation), transparent disclosure policies, and sectoral regulatory audits for researchers, academic publishers, and government policy strategists to safeguard scientific integrity.