• Dr. Kolahalam Ramkishore

Artificial Intelligence (AI) has emerged as a transformative force across academic disciplines, including Political Science research. This article critically examines the effectiveness, reliability, and ethical integration of AI-assisted writing tools in Political Science. It evaluates how AI technologies—particularly large language models and data analytics systems—enhance literature review synthesis, data coding, hypothesis generation, and drafting processes. Simultaneously, it interrogates methodological risks such as algorithmic bias, hallucination, epistemic homogenization, data privacy concerns, and the erosion of scholarly originality. Using Social Sciences research methodology frameworks, this paper adopts a qualitative-analytical approach supported by secondary data, scholarly literature, and case illustrations. The study argues that while AI significantly improves efficiency and accessibility in research writing, its integration must be governed by clear ethical protocols, transparency standards, and human oversight to preserve academic integrity. The paper concludes by proposing a structured model for responsible AI integration in Political Science research, balancing technological innovation with epistemological rigor and ethical accountability.

Artificial Intelligence, Political Science Research, Research Methodology, Academic Integrity, Large Language Models, Algorithmic Bias, Scholarly Communication.

"ARTIFICIAL INTELLIGENCE – ASSISTING WRITING IN POLITICAL SCIENCE RESEARCH: EFFECTIVENESS, RELIABILITY AND ETHICAL INTEGRATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 2, page no.418-421, February-2026, Available :http://www.jetir.org/papers/JETIRHJ06081.pdf

"ARTIFICIAL INTELLIGENCE – ASSISTING WRITING IN POLITICAL SCIENCE RESEARCH: EFFECTIVENESS, RELIABILITY AND ETHICAL INTEGRATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 2, page no. pp418-421, February-2026, Available at : http://www.jetir.org/papers/JETIRHJ06081.pdf