American Journal of Advanced Multidisciplinary Research and Innovation

E-ISSN: XXXX-XXXX   •   Impact Factor: -

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

Call for Paper Volume 8, Issue 5 (September-October 2026) Submit your research before last 3 days of October to publish your research paper in the issue of September-October.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 5
September-October 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Generative Artificial Intelligence in Scientific Research: Opportunities, Ethical Considerations, and Future Applications

Author(s) Tommi Jaakkola
Country United States
Abstract Generative Artificial Intelligence (GenAI) has rapidly emerged as a transformative technology capable of reshaping scientific research across multiple disciplines. Powered by large language models (LLMs), generative adversarial networks (GANs), diffusion models, multimodal AI, and foundation models, GenAI assists researchers in literature review, hypothesis generation, experimental design, data analysis, scientific writing, programming, simulation, image generation, drug discovery, and knowledge synthesis. By automating repetitive research tasks and enhancing human creativity, Generative AI significantly improves research productivity, interdisciplinary collaboration, and innovation. However, alongside these opportunities, the widespread adoption of GenAI introduces important ethical, legal, technical, and societal concerns related to research integrity, bias, hallucinations, reproducibility, copyright, authorship, transparency, data privacy, cybersecurity, and responsible AI governance.
This study investigates the role of Generative Artificial Intelligence in scientific research using a qualitative and analytical research methodology based on secondary data collected from peer-reviewed journals, international policy reports, research ethics guidelines, and multidisciplinary case studies. The study examines applications of GenAI across healthcare, engineering, environmental science, education, social sciences, chemistry, manufacturing, and computational research. Furthermore, it evaluates ethical considerations, governance frameworks, explainable AI, responsible AI principles, human-AI collaboration, and future research opportunities while identifying implementation challenges and policy implications.
The findings indicate that Generative AI significantly enhances literature discovery, scientific communication, computational modelling, code generation, predictive analytics, experimental simulation, and interdisciplinary knowledge creation. The integration of explainable AI, retrieval-augmented generation (RAG), digital research assistants, federated learning, blockchain-based provenance systems, and human-in-the-loop validation further strengthens transparency, reliability, and trustworthiness. Nevertheless, challenges including hallucinated information, algorithmic bias, privacy concerns, intellectual property disputes, cybersecurity threats, unequal access to advanced AI systems, and the need for robust ethical governance remain significant.
The study concludes that Generative Artificial Intelligence represents a paradigm shift in scientific research. Responsible implementation, transparent governance, interdisciplinary collaboration, and continuous human oversight are essential to maximise the benefits of GenAI while protecting scientific integrity and public trust.
Keywords Generative Artificial Intelligence, Large Language Models, Scientific Research, Research Ethics, Explainable AI, Responsible AI, Human-AI Collaboration, Scientific Writing, Research Integrity.
Field Engineering
Published In Volume 1, Issue 1, January-February 2019
Published On 2019-02-26
View / Download PDF File

Share this



E-ISSN XXXX-XXXX
CrossRef

CrossRef DOI prefix of AJAMRI is 10.00000/AJAMRI

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About AJAMRI
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +1-872-302-3851 editor@ajamri.com