Dear Atif Shahzad That’s a very interesting area. From my experience, some effective methodologies include:

• AI-driven literature review (semantic search, citation mapping).
• Data analysis and preprocessing with ML/NLP models.
• Assisted writing and editing while ensuring academic integrity.
• Knowledge management by integrating AI into reference and project tools.

I also believe developing discipline-specific case studies can show how AI tools improve efficiency and reproducibility in research workflows, as per my research paper mentioned below, you can see how it is.

Check my paper and give your valuable review:

Article Management in Healthcare Sector: Artificial Intelligence Met...

Dear Atif Shahzad ,

Academic writing is a key aspect of research and education, involving a structured method of expressing ideas. It is commonly used by researchers and educators in scholarly works to present data-driven arguments and logical reasoning. This form of writing helps readers to understand a topic thoroughly. It allows authors to deeply analyse concepts, leading to a well-explained theory or conclusion. Different fields use academic writing for various purposes. For example, scientists use it to explain their research and findings, while literary analysts use it to create fact-based critiques

Article Using Artificial Intelligence in Academic Writing and Resear...

Regards,

Shafagat

Map your workflow – identify repetitive tasks (literature search, data cleaning, analysis, writing).

Choose task-specific tools – e.g., Elicit/Semantic Scholar for literature, ChatPDF for document analysis, AutoML or Python for data, LLMs for drafting.

Use human-in-the-loop – AI supports, but researchers validate outputs.

Ensure transparency – document tool use, disclose in publications, never list AI as author.

Apply ethics and data governance – respect privacy, IP, and journal guidelines.

Maintain reproducibility – track datasets, tool versions, and results.

Train your team – build AI literacy and review processes.

Monitor continuously – evaluate accuracy, bias, and efficiency gains.