Introduction to Research Methodology and AI Literacy

Video Transcript: Introduction to Research Methodology and AI Literacy

Hello everyone, and welcome to this session on Research Methodology and AI Literacy.
Whether you’re a graduate student or an active researcher, this session is designed to set the stage for how research and artificial intelligence are shaping the future of knowledge creation.

[Slide: “What is Research Methodology?”]
Let’s start with the basics.
Research methodology is the structured process we use to ask questions, collect data, analyze results, and build knowledge.
Traditionally, we’ve followed a systematic cycle:

• Identify a problem or hypothesis

• Design experiments or studies

• Collect and analyze data

• Draw conclusions and share findings

This cycle has been the foundation of academic and applied research for decades.
But today, there’s a major shift: AI is becoming part of every step of this process.

[Slide: “AI Literacy: Why It Matters”]
AI Literacy means understanding how artificial intelligence works, how to use it responsibly, and how it can support your work.
Just like being able to search a database or design an experiment, being AI-literate is quickly becoming a core skill for researchers.

AI tools are not here to replace researchers—they’re here to amplify our capabilities.

[Slide: “Where AI Fits in the Research Cycle”]
Let’s walk through some examples:

1. Literature Review:
   Tools like Semantic Scholar, Elicit, or even ChatGPT can summarize hundreds of papers, identify trends, and save hours of manual reading.

2. Data Collection & Cleaning:
   AI-driven platforms like OpenRefine or Python libraries with machine learning modules can clean massive datasets automatically.

3. Analysis:
   AI models can detect patterns that humans might miss—whether it’s analyzing medical images, modeling ecosystems, or predicting market shifts.

4. Writing & Visualization:
   Tools like Notion AI, ChatGPT, or Canva AI can help generate draft reports, graphs, and even visual abstracts, freeing your time for deeper thinking.

[Slide: “Ethics & Best Practices”]
Now, here’s the critical part: just because we can use AI doesn’t mean we should, at least not blindly.

• Always verify AI-generated content—these tools are powerful but not infallible.

• Maintain academic integrity: AI should support your originality, not replace it.

• Respect data privacy: never upload sensitive datasets into unverified platforms.

• Keep human oversight central. AI should complement, not substitute, your expertise. You should apply critical thinking skills to correct correct generated by AI. And reappropriate your own voice.

[Slide: “How to Build AI Literacy”]
Here’s a quick roadmap to grow your AI literacy as a researcher:

1. Experiment: Start small—try using AI to search for relavant materials and summarize them for further research 

2. Understand the Basics: Learn how AI models are trained and their limitations.

3. Stay Updated: Follow research tech trends—AI is evolving rapidly.

4. Develop a Critical Eye: Question AI outputs the same way you’d question a peer-reviewed paper.

In short, research methodology is timeless: it’s about curiosity, rigor, and evidence. But mostly proposing a original research topic that has not been investigated or already published. Even if AI will suggest a topic, based on thousands of already available studies, you need to propose a research that is unique. What’s changing is the toolbox we have at our disposal.

AI isn’t the future—it’s the present. And your ability to use it thoughtfully will set you apart as a next-generation researcher.

Thank you for joining this introduction. In the next sessions, we’ll dive deeper into practical demonstrations of these tools and how to integrate them into your own workflow.