It also depends on the person who is using it. How far can he/she explore the capabilities, and integrate AI in research and analysis, like Qualitative analysis in biology, which involves interpreting complex information, synthesizing literature, and generating new ideas. Stay up-to-date on the latest Literature and gather Information to start with... AI can also help you "brainstorm" ideas and get new insights. Likewise, in Quantitative research, automating tasks with AI Agents can significantly improve our speed.

AI can meaningfully enhance both quantitative and qualitative research by improving speed, accuracy, and depth of analysis across large and complex datasets.

For quantitative research, AI can automate data collection, clean large datasets, detect patterns, and run complex statistical models or simulations faster than traditional methods. Machine learning algorithms can identify hidden trends, forecast outcomes, and optimize decision-making in fields like economics, healthcare, and finance. Tools like natural language processing (NLP) also allow researchers to analyze large volumes of numerical and textual data together, revealing multi-layered insights.

For qualitative research, AI can assist in transcribing interviews, coding themes, and analyzing sentiment or tone across massive amounts of text, audio, or video. NLP models can help identify recurring narratives, uncover subtle emotional cues, or even simulate participant perspectives to aid in theory development. While AI cannot fully replace human interpretation, it amplifies researchers’ ability to process rich, unstructured data more efficiently and rigorously, enabling deeper exploration and more nuanced conclusions.

Li Na

"Li Na, China University of Petroleum" (https://www.researchgate.net/profile/Li-Na-127) took publications from indonesian researcher Lina from Tarumanagara University in Jakarta, and photo of Dr Lina Khatib, Associate Fellow at Chatham House, as an avatar. And "Lila Jia, China University of Geosciences"...

I am back to the question dear Ripon Kumar Sarkar

Artificial Intelligence (AI) for Research Lifecycle: Challenges and Opportunities

"This article aims to review the progress of AI technologies concerning their potential impact on academia, research processes, scientific communication, and libraries...

AI has become a driving force nowadays, creating both opportunities and challenges. Transformative AI-powered tools, exemplified by advanced models like ChatGPT, Llama-2, Google Bard, Microsoft Bing, and Jasper Chat, among others, find versatile utility across a broad spectrum of contexts, extending their impact to research process and publishing, as well as to librarianship. The enthusiastic embrace of AI in research is tempered by a pervasive concern over the potential for data fabrication, which can significantly compromise ethical standards and academic integrity. There is an urgent need to understand corresponding opportunities, challenges, and dangers. Some aspects of the use of AI tools for different stages of the research lifecycle are considered, and the main advantages and risks are analyzed. AI has the potential to drive innovation and progress in a wide range of fields and possesses significant potential to propel academia and librarianship into both exhilarating and challenging new frontiers. While AI-powered tools represent major advancements and potential to significantly impact academia, scholarly research, publishing, and university libraries. Privacy and bias are just two examples of the ethical considerations that need to be made."

Article Artificial Intelligence (AI) for Research Lifecycle: Challen...