How to Do a Modern Literature Review with AI Tools: Step-by-Step Guide Using Semantic Scholar, NotebookLM & Consensus

 Modern Literature Review: AI Tools That Save Hours

If you've ever stared at a mountain of research papers and felt completely overwhelmed, you're not alone. While literature reviews are a critical part of any academic project (research paper, thesis, or grant proposal), the traditional approach of manual searching and endless reading can take weeks or even months.

But what if there was a better way?

The good news is that modern AI-powered tools have fundamentally transformed how researchers approach literature reviews. Instead of drowning in papers, you can now find what you need, synthesise complex ideas across multiple sources, and identify research gaps in a fraction of the time. In this guide, we'll walk you through a three-step workflow using three powerful tools: Semantic Scholar, NotebookLM, and Consensus.

🎥 Want to See It in Action?

Check out our video tutorial on using Semantic Scholar, NotebookLM, and Consensus to find papers, synthesise ideas, and identify research gaps faster than ever.

Understanding the Modern Literature Review

Before delving into the tools, let's clarify what we're actually doing. A literature review isn't just a summary of papers you've read. It's a deep, critical, and systematic analysis of everything that's already been researched in your field. It shows what we already know, helps you identify where your work fits in, and most importantly, it uncovers research gaps; those unexplored territories where you can make a real contribution.

The modern approach to literature reviews makes use of three key advantages: natural language search (no more keyword guessing), AI-powered synthesis (asking questions instead of manually reading), and scientific consensus validation (ensuring you're on the right track).

Step 1: Find Your Foundation with Semantic Scholar

The first step is finding your seed papers. These are 5-10 crucial  papers that form the foundation of your literature review.

Why Semantic Scholar? With over 200 million research papers in its database, Semantic Scholar is practically a guarantee that you won't miss critical papers. More importantly, it's designed for discovery, not just search.

Here's how to use it effectively:

Start by typing your entire research question in plain English. Instead of guessing keywords, just describe what you're researching. Semantic Scholar's natural language processing understands context, so you get relevant results without the keyword frustration.

Smart Filters: Apply filters like "Has PDF" to avoid paywalls and "Last 5 years" to find recent research. These two filters alone will save you hours of frustration.

Finding Related Papers: Once you land on a promising paper, Semantic Scholar shows you related papers and highly cited works with a single click, making discovery intuitive and fast.

Your Goal with Semantic Scholar: Don't try to read everything. Download about 5-10 of the most relevant and influential papers. You're gathering materials, not exhaustively reviewing yet.

Step 2: Synthesize and Find Gaps with NotebookLM

Now comes the part that transforms literature reviews: synthesising all those papers with AI.

Think of NotebookLM as your personal team of AI research assistants. You hand them your stack of papers, and they read, analyze, and synthesise the entire collection for you; extracting patterns, identifying themes, and finding gaps that would normally take days of painstaking work.

How to Use It: Upload your papers and start asking questions. For example:

• "What are the most common methods used to address the question in these papers?"

• "What limitations and research gaps are mentioned?"

• "What are potential research questions based on these gaps?"

The tool doesn't just summarise, it becomes your research partner. When you ask about research gaps, it identifies concrete, actionable limitations.

The Real Power: NotebookLM can even generate potential research questions for you based on the gaps it identifies, helping you sharpen your focus .

Step 3: Refine Your Understanding with Consensus

If Semantic Scholar was your microscope (deep focus into specific papers), Consensus is your telescope (bird's-eye view of the entire field).

Consensus answers the fundamental question: What does most of the research actually say? It scans millions of papers (it has access to 250+ million across multiple fields) to synthesise scientific consensus on your topic and provides direct citations to the most influential studies supporting that consensus.

How to Use It: Turn your research question into a question for Consensus. It will analyse the broader research landscape and give you an overview of what the scientific community generally agrees on. This is perfect for double-checking your findings and making sure you're on the right track.

The Three-Step Framework: Find, Synthesise, Refine

Here's the complete modern workflow:

1. Find your foundational papers using Semantic Scholar's natural language search and smart filters

2. Synthesise their ideas and identify gaps using NotebookLM's AI analysis

3. Refine your big-picture understanding using Consensus to validate against scientific consensus

This framework transforms you from a passive reader buried under papers to an active director of knowledge. You're using these powerful tools to move fast, extract actionable insights, and focus on what really matters: creating something new.

Getting Started

Ready to try this workflow? Here's what to do:

1. Go to Semantic Scholar and search for your research topic in plain English

2. Apply the "Has PDF" and date range filters to narrow results

3. Download 5-10 of the most promising papers

4. Upload them to NotebookLM

5. Ask NotebookLM to identify research gaps and limitations

6. Use Consensus to validate your findings

The entire process, which used to take weeks, now takes hours (or even minutes if your research scope is narrow).

Have you used Semantic Scholar, NotebookLM, or Consensus before? Share your experience in the comments below, or watch our full video tutorial for a complete walkthrough of this workflow.

For more research tips and biotech tutorials designed at an advanced undergraduate level with expert insights, subscribe to Adwoa Biotech.

Google's Free AI Tools for Scientists & Researchers 2025

 Your Secret Weapon: Google's Hidden AI Arsenal for Scientists (And It's All Free)

Are You Still Working Like It's 2019?

Picture this: You're drowning in PDFs from your literature review, spending hours formatting figures for that conference poster, and somehow still expected to maintain an active social media presence to boost your research impact. Sound familiar?

Here's the plot twist: while you've been grinding away with traditional tools, Google has quietly assembled an entire suite of AI-powered research aides that could cut your workload in half. And we're not talking about basic chatbots. We're talking about tools that understand your research, create professional visuals, generate video content, and even help you build custom applications, no coding required.

The best part? Every single tool we're about to show you is completely free.

🎥 Want to See It in Action?
Check out our video tutorial on Google's AI Tools for Researchers on the Adwoa Biotech YouTube Channel.

Part 1: Supercharge Your Research Brain

NotebookLM: Your AI Research Assistant That Actually Gets It

Let's be honest, traditional note-taking apps are basically glorified filing cabinets. You dump information in, and when you need it later, you're stuck searching through digital haystack to find that one needle of a citation.

NotebookLM changes everything.

Think of it as your personal research assistant that doesn't just store information, it comprehends it. Here's how it works:

What You Can Feed It:

• PDF journal articles

• YouTube lecture links

• Website URLs

• Your own research notes

• Literally any text-based source

What It Does With That Information:

Once you upload your sources, NotebookLM builds a private knowledge base (think of it as a mini-brain) trained exclusively on your materials. This means you can:

1. Ask Questions Directly: Instead of Ctrl+F searching through 50 papers, just ask, for example "What are the main limitations of CRISPR-Cas9 mentioned across my sources?"
   
   

2. Generate New Formats Instantly: This is where things get wild. That dense 50-page master's thesis you need to present? Ask NotebookLM to create a 2-minute video pitch script that extracts only the key findings. Need to review your notes while commuting? Convert them into a conversational podcast format.
   
   

3. Cross-Reference Automatically: It identifies connections between different sources that you might have missed.
   
   

Pro Tip: Use NotebookLM during your literature review phase. Upload all relevant papers, then ask it to identify gaps in the current research or conflicting findings. It's like having a senior researcher looking over your shoulder.

AI-Powered Google Search: From Library Card to Exact Answer

Remember the old research workflow? Google something, get 10 blue links, open tabs until your browser crashes, skim through irrelevant content, repeat. Exhausting.

Google's AI Mode flips this entirely. Instead of handing you a library card and saying "good luck," it synthesizes information from multiple sources and delivers:

• A direct answer to your specific question

• Cited sources for verification

• Related follow-up queries you might need

The Difference:

Old Search Method	AI-Powered Search
Returns list of websites	Returns synthesized answer
You do the analysis	AI does initial synthesis
Time: 20+ minutes	Time: 2 minutes
Requires multiple searches	Anticipates follow-up questions
Sources scattered	Sources clearly cited

Safety Alert: Always verify AI-generated answers with primary sources, especially for critical research decisions. Think of it as a starting point, not the endpoint.

Part 2: From Knowledge to Creation—Build Your Visual Arsenal

Text-to-Image: Skip the Photoshop Learning Curve

Let's talk about a common pain point: You need to create a graphical abstract for your paper, but you're a scientist, not a graphic designer. Learning Photoshop or Illustrator? That's a semester-long commitment you don't have time for.

Google's AI image tools let you describe what you want in plain English, and the AI handles the technical execution.

How Simple Is It?

Instead of mastering layers, masks, and blend modes, you just type something like:

"Create a scientific illustration showing a lipid bilayer with embedded proteins, clean style, suitable for journal publication"

That's it. No technical jargon. No tutorial videos. Just conversational instructions.

Pro Tip: For research integrity, always disclose when images are AI-generated or AI-enhanced in your figure legends. Transparency is paramount in scientific communication.

Real-World Applications:

• Graphical abstracts for papers

• Social media graphics for research promotion

• Presentation slides and posters

• Educational materials for teaching

Veo: Professional Video Creation for Scientists

Here's a truth bomb: As modern researchers, we're expected to be science communicators too. Your brilliant findings need to reach beyond peer-reviewed journals—they need to live on Twitter, LinkedIn, YouTube, and institutional websites.

But who has time to learn video editing?

Enter Veo, Google's text-to-video model that generates high-quality video clips from simple descriptions or even a single reference image.

Creating a 15-second animation showing how your novel drug delivery system works? Just describe it:

"Smooth animation of nanoparticles targeting tumor cells, professional medical style, suitable for conference presentation"

Within minutes, you have publication-ready footage. No After Effects degree required.

The Quality Standards:

• Professional-grade output

• Subtle and appropriate for academic contexts

• Properly timed for various platforms

• Ready for website banners, presentations, or social media

Use Case: Imagine converting your research findings into a 30-second explainer video for your university's social media. What used to take a videography team and a week now takes you one coffee break.

Audio Generation: Podcasts and Voiceovers Without the Studio

Need a professional voiceover for your online poster presentation? Want to create a podcast explaining your research to a general audience? Google's audio tools let you:

• Generate realistic voices from text scripts

• Create multi-speaker podcast dialogues

• Produce professional voiceovers

• Develop audio abstracts for accessibility

Part 3: Build Your Own AI Tools (No Computer Science PhD Required)

This is where things get genuinely revolutionary. What if you could build custom applications tailored to your specific research workflow - without writing a single line of code?

Your Two Options: Opal vs. AI Studio

Feature	Opal	AI Studio
Complexity	Quick & easy	More robust
Best For	Fast prototypes	Scalable applications
Learning Curve	Minutes	Hours
Use Case	Testing ideas	Production-ready tools
Scalability	Limited	Can be commercialized

Real Examples Built by Researchers:

1. Interactive Quiz Builder

• Create Kahoot-style educational games for teaching

• Test comprehension of complex protocols

• Student engagement tool for graduate seminars

3. Laboratory Protocol Assistant

• Upload your standard operating procedures

• Ask questions about specific steps

• Get troubleshooting suggestions based on your lab's methods

Pro Tip: Start with Opal to prototype your idea. Once you've validated it works, rebuild in AI Studio for a more permanent, shareable solution.

The Complete Toolkit: Your Three-Stage AI Journey

Think of these tools not as isolated gadgets, but as an integrated workflow:

Stage 1: Knowledge Acquisition

• Use NotebookLM for literature synthesis

• Employ AI Search for targeted information gathering

• Build your foundational understanding

Stage 2: Creation & Communication

• Generate visuals with text-to-image tools

• Create videos with Veo

• Produce audio content for accessibility and engagement

Stage 3: Innovation & Automation

• Build custom tools with Opal or AI Studio

• Automate repetitive research tasks

• Create solutions unique to your workflow

The Power of Integration: Imagine uploading all your background research to NotebookLM, asking it to identify the most impactful findings, then using those insights to generate a video abstract with Veo, and finally building a custom quiz in Opal to test understanding. That's the entire research communication pipeline, powered by AI, executed in an afternoon.

Conclusion: The Democratization of Research Tools

For decades, sophisticated research tools were gatekept behind expensive software licenses, steep learning curves, or simply didn't exist. The playing field was never level.

That era is over.

Google has democratized access to AI-powered tools that rival what was previously available only to well-funded labs. 

The question isn't whether you can use them, they're free and accessible. The real question is: What will you create?

References

1. Anil, R., Borgeaud, S., Alayrac, J. B., Yu, J., Soricut, R., Schalkwyk, J., . . . Vinyals, O. (2023). Gemini: A family of highly capable multimodal models. arXiv. https://doi.org/10.48550/arXiv.2312.11805

2. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., . . . Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html

3. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., . . . Sutskever, I. (2021). Zero-shot text-to-image generation. In Proceedings of the 38th International Conference on Machine Learning (pp. 8821–8831). PMLR. http://proceedings.mlr.press/v139/ramesh21a.html

4. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html

5. Villalobos, P., Sevilla, J., Heim, L., Besiroglu, T., Hobbhahn, M., & Ho, A. (2022). Will we run out of data? An analysis of the limits of scaling datasets in machine learning. arXiv. https://doi.org/10.48550/arXiv.2211.04325

6. Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., . . . Wen, J. R. (2023). A survey of large language models. arXiv. https://doi.org/10.48550/arXiv.2303.18223