Most researchers spend 40+ hours reading papers but can barely remember what they read last week.

After diving deep into how to take notes for my courses,
I found this 4-step system that turns reading into permanent knowledge.

Here's how to never forget what you read again: Most of us treat note-taking like passive transcription.

We:

• Can't connect ideas across sources
• Never revisit our notes
• Copy quotes verbatim
• Highlight everything

Result? Our notes become information graveyards.

The 3-2-1 Writing Clarity Rule that changed how I approach every academic paper:

Most academics think complex ideas need complex sentences.

They're wrong.

Here's how the 3-2-1 rule works: 3 Types of Unnecessary Words to Cut:

Redundant pairs
("completely eliminate," "exact same")

Vague time markers
("nowadays," "at this point in time")

Meaningless intensifiers
("very unique," "quite significant")

I believed these 11 lies about literature reviews until I knew better

Don't let these myths hold you back.

The honest truth about literature reviews: 🗣️ "A literature review is just a summary of sources"

How I went from 12 citations to 39,286 by changing how I wrote.

Not what I researched.

My biggest struggles as a researcher were:

* Staying motivated
* Getting published
* Being cited

The one thing I learned:
Writing a paper isn’t hard. Writing a readable one is.

Successful research papers are 60% science, 40% packaging. Here's my 7-step framework for writing papers
people actually want to read:

1. Abstract = Your 30-second pitch

Answer simply: "What problem did I solve and why should anyone care?"

Your abstract is your elevator pitch.
Most of you are still writing disclaimers.

The biggest mistake researchers make with AI?

Using it to confirm what they already believe.

Don't turn AI into an echo chamber instead of an adversary.

The lazy way to use AI in academia:

• Polish existing arguments
• Speed up writing papers
• Format citations faster

Using AI just to speed up, not to question methods.
But aren't we already going fast enough, folks? AI's real power is in its ability to disagree with you

Try these 3 approaches instead:

1. Force AI to argue against your hypothesis

Make it play devil's advocate with cherished research assumptions. The counterarguments it generates might shed light on things you've missed.

Most PhD students work 10x harder than needed.

I see it daily - smart students drowning in manual tasks tools could handle.

(This kills your research productivity)

8 use cases where you can cut your workload in half: 1. Literature reviews made simple

• ConnectedPapers
• Research Rabbit
• Consensus
• SciSpace
• Litmaps
• Scite
• Elicit

These tools you discover relevant research papers effortlessly.

Visualize your references and stay updated with the latest publications.

Here's the perfect formula to write a literature review paragraph.

A great literature review paragraph needs exactly 2 components.

Most students think every paper needs its own paragraph.

Completely off the mark.

The secret? Combine synthesis + evaluation:

• Find papers with similar findings
• Group them under one theme
• Connect everything together
• Add critique for each study

Most people approach critical thinking wrong.

They focus on individual skills:
• Problem-solving ability
• Decision making
• Logical reasoning

But critical thinking has 3 deeper layers: 1. Question Everything

Ask "why" before accepting claims
Challenge your own assumptions
Seek evidence beyond opinions

2. Build Connections

Link knowledge to experience
Find patterns in complex data
Connect seemingly unrelated ideas

3. Stay Open-Minded

Listen to opposing views
Update beliefs with new evidence
Focus on learning, not being right

The difference between good and great thinkers?

A systematic review requires exhaustive, comprehensive searching with quality assessment criteria, while a rapid review can be completed with time-limited formal quality assessment. The difference is months of work.

According to this paper, 14 literature review types exist.

If you get started, focus on 2 main types:

Systematic reviews → exhaustive analysis
Rapid reviews → quick assessment Systematic reviews:

• Quality assessment required
• Comprehensive searching
• 6-24 months timeline
• Tabular presentation
• Narrative synthesis
• Formal protocols

Rapid reviews:

• Limited quality checks
• Time-bound searching
• Evidence summary
• Basic presentation
• 1-6 month timeline
• Flexible protocols

The distinction?

Speed vs. thoroughness.

The human mind, heart, and body are more complex than we think.

(This will change how you see decision-making forever)

Most people view the mind as a simple input-output machine. But it's actually a sophisticated system of interconnected layers: Core

• Raw existence (energy, matter, time)
• Basic survival instincts
• Pure consciousness

Personal

• Your unique experiences
• Individual behaviours
• Personal values

Environment

• Cultural institutions
• Power structures
• Economic forces

Each layer influences every decision you make.

But you have to understand which layer drives specific choices.

Want to change behaviour for good?
Target the correct layer.

Chat PDF in Paperpal just changed how I read research papers.

Just uploaded a systematic review paper and my jaw dropped.

Here's what happened: 1. Instant paper breakdown

• Title, authors, DOI extracted automatically
• Smart summary generated in seconds
• Key sections identified and linked
• Research questions pulled out precisely

2. Smart paper connections

• Found relevant papers I hadn't discovered
• Connected papers across different fields
• Added them to my library with one click
• Surfaced hidden relationships

But the real magic? The built-in question engine.

Most researchers make a fatal mistake in their proposal's introduction, but understanding the "Why-What" sequence can change everything...

Here's a 15-part structure I use that makes it simple.

Let's break it down into 7 broad steps: 1. Start with why it matters in context

• State your research purpose
• Hook readers with significance

2. Back up claims in literature

• Map the existing knowledge
• Find the gaps to fill

Most philosophers got problem-solving better than modern design thinking.

This missing piece changes everything about tackling complex challenges:

Most people think philosophy is useless in business and politics.

They're missing the most powerful operating system for decision-making. There is a kind of magic to how great thinkers solve problems.

Here's the deal:

Your mind uses philosophical frameworks on inner autopilot:

• Logic structures how you make decisions
• Metaphysics shapes how you see reality
• Epistemology determines how you learn

A simple trick: treat different thought processes like tools.

Most researchers make a critical mistake in their methods section that instantly signals 'amateur' to reviewers. It's so common that I see it in 7 out of 10 papers, yet so simple to fix...

Delay writing your Methods section.

Spend time owning your research process first: By answering 4 questions:

• What problem did you solve?
• For whom did you solve it?
• Why did this problem need solving?
• How did you solve it effectively?

Get comfortable thinking through each:

Think through your research design
Think through your ethical choices
Think through your data collection
Think through your analysis steps
Think through your limitations

Write down specific answers for each.

And if you have unclear answers:

→ Take time to better understand

Every groundbreaking discovery in science started with someone willing to challenge their own assumptions. Your next literature review could be the one that changes everything.

Your academic work needs to fight confirmation bias.

It's blocking you from great research.

And it's easier to fix than you think.

Here's how to destroy confirmation bias in your research: 1. Plan before you search

→ Write your research questions
→ Define inclusion criteria
→ List your search terms
→ Pick your databases

2. Document everything

→ All search strings used
→ Every database checked
→ Number of results found
→ Selection decisions made

3. Remove author identity

→ Code your studies
→ Hide author names
→ Mask institutions
→ Review methods first

4. Use two reviewers

→ Independent assessments
→ Compare decisions
→ Discuss differences
→ Record resolutions

Frustrated by academic bias against AI tools?

If you’re using AI for writing, reviewers might be working against you.

But most reviewers can't tell if your research paper was written by AI.

(This changes how we think about academic writing)

Here's what my research team discovered (new article published): With top reviewers as participants,
our research breaks down AI misconceptions:

• AI-assisted papers were seen as more honest
• They consistently failed to spot which was which
• Their judgment of research quality stayed the same
• Those familiar with AI gave higher ratings for clarity
• Reviewers rated AI-generated and human-written papers equally

But here's the most interesting part...

Reviewers valued one thing above all:

The "human touch" in research writing.

Most PhD students fail at research questions.

(I used to be one of them)

See, back when I started my research journey, I thought coming up with research questions was pure luck.

Just throw something at the wall and hope it sticks.

Wrong.

Here’s the step-by-step breakdown: 1. Define the broad topic area of research

Start broad.

In just one or two sentences, outline the general area you’re interested in.

(Example: “User interaction in virtual reality environments.”)

This sets the stage for deeper investigation.

Stop writing academic papers like a robot.

Tell stories instead.

Most academic writing puts people to sleep.

But it doesn't have to.

Academic storytelling changes everything.

Here's why it works: → Builds emotional connection
→ Creates memorable content
→ Makes complex ideas clear
→ Keeps readers engaged
→ Drives more citations

Yet most academics fail at storytelling.

So I broke it down into 5 simple techniques:

How I turned a 50-page literature review into a concise 10-page masterpiece using 9 simple questions:

Most researchers get lost in endless summaries.
They miss the big picture.

But there's a simple framework to fix this.

I call it the 9-Question Literature Review Framework: 1. What has been done?
2. What were the hypotheses?
3. What were the research questions?
4. How was the work done?
5. When was it done?
6. Who did it?
7. What were the main findings?
8. What were the conclusions?
9. What should be done next?

This framework helps you:

• Organize your thoughts
• Identify research gaps
• Develop your own questions

Feel like giving up on research proposals? (Read this, please)
Back in my early career, I lost two grant applications.

In my PhD, I nearly quit academic writing altogether.

I almost gave up on research. Twice.

But as Associate Professor, something shifted. 3 things, actually: 1. Proposal structure → without compromise

I committed to using a foolproof outline for every proposal:

Title Page → Your research's first impression
Table of Contents → A roadmap for reviewers
Abstract → Your research in a nutshell

What if choosing between quantitative and qualitative methods is asking the wrong question entirely?

A comparison of quantitative, qualitative, and mixed methods

Researchers often agonize over picking quantitative or qualitative methods.

But here's the truth: This "either-or" thinking is outdated.

Let's break down why:

1. Quantitative methods:

• Focus on numbers and statistics
• Provide generalizable results
• Test hypotheses

→ But can miss nuanced insights