I help PhDs, Postdocs & Lecturers secure tenure-track jobs and navigate academia | Insider knowledge tips on how to succeed | Academic Career Mentor | Professor & Research Director

I was a lost PhD student. Confused. Overwhelmed. Ready to quit.

Sitting in my tiny office at 2 AM, drowning in research papers, I made a promise...

"If I somehow make it through this, I'll help others navigate what I'm struggling with."

Today, I'm a Professor at a top UK university.

But here's what matters more...

I've kept that promise by helping researchers complete their PhDs and navigate academia.

------

The brutal truth about doing a PhD/DBA whilst working

I hear the same struggles from the 26% of PhD & DBA students who are working professionals:

→ I can't keep up -the mental capacity is exhausted
→ I'm juggling three full-time jobs with no bandwidth
→ I've been stuck for 3-6 months with no real progress
→ My family thinks my PhD/DBA is a hobby, not a second job
→ I feel isolated - no one understands what I'm going through

Sound familiar? You're not alone. And it's not your fault.

Universities train PhD/DBA researchers for academia, but working professionals need something completely different:

❌ You're investing $25-80K but risk joining the 33% who drop out
❌ Motivation crashes after major life events (births, job changes, pandemic)
❌ Distance learning leaves you isolated, struggling to navigate systems alone
❌ You don't have 40 hours/week; you have 10 hours scattered across nights & weekends  
❌ Your supervisor gives great academic feedback but doesn't understand work-life-PhD balance

That's why I created specialised services for serious researchers.

My transformation:

→ 30+ PhDs/DBAs mentored to success
→ 130+ publications across top journals
→ £9 million in research funding secured
→ From PhD uncertainty to Professor by 40
→ 230K researchers follow my guidance daily
→ Research reached 30+ million people globally

FREE RESOURCES:

✅ Weekly newsletter (10K+ subscribers) www.phdtoprof.com/newsletter
✅ Free academic resources www.phdtoprof.com/store
✅ AI tools for ethical research productivity

PREMIUM SERVICES:

🎯 Premium PhD/DBA Mentoring - For senior professionals completing their PhD/DBA on time
🎯 PhD/DBA Proposal Development - Support developing and passing proposals
🎯 Expert Thesis Review - Comprehensive dissertation feedback from an experienced PhD examiner
🎯 AI in Research Masterclasses - Responsible and ethical AI use for researchers
🎯 University & Institution Training - Workshops on responsible AI in research for academic teams

My mission:

Help you complete your PhD/DBA on time while using AI responsibly in your research.

Academic success doesn't have to be a mystery. You just need the right guide.

Emmanuel Tsekleves

A PhD student showed at my office in tears. Prof Clarke sent me to you. I can't do this anymore. She wasn't failing her PhD. Her PhD was failing her.

Heather was brilliant. Motivated. Passionate about her research.

But her supervisor was failing her.

Not academically. Emotionally.

That conversation changed how I think about mentorship forever.

Here's what I learned:

Most supervisors focus only on research progress.

They ignore the human being behind the work.

Heather's supervisor was technically excellent.

But emotionally absent.

She didn't need a smarter supervisor.

She needed a more present one.

I started meeting with Heather weekly.

I gave her what was missing:

1) Recognition
I celebrated small wins. Not just problems.
"Your literature review structure is improving" mattered as much as technical feedback.

2) Responsibility
I helped her own her research decisions.
She didn't need permission for every choice.

3) Learning
I created space for questions without judgment.
"I don't understand this" became a starting point. Not a failure.

4) Joy
I reminded her research should excite her.
We discussed ideas over coffee. Not just problems in formal meetings.

The transformation was incredible.

Heather went from wanting to quit to finishing 3 months early.

Her confidence returned. Her research quality improved.

She now supervises her own students using these same principles.

The problem was never Heather's ability.

It was inadequate support.

Your PhD supervisor has more impact on your mental health than your therapist.

Choose them wisely.

And if you're a supervisor reading this:

Your job isn't to push students harder.

It's to support them better.

---

Which of these 4 elements did YOU need most during your PhD?

Recognition, Responsibility, Learning, or Joy?

#PhDLife #AcademicMentorship

I help busy researchers complete their PhDs and DBAs on time | Expert Thesis Reviewer | AI in Research Training for Researchers & Universities | Professor | 200,000+ researchers guided

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"I can't afford a PhD." I hear this every week.
And every week, I show them scholarships they never knew existed.
There are 50+ fully-funded PhD programmes worldwide.

Most students only know about 3-4.

They apply to the same obvious ones.

They compete against thousands.

They get rejected.

Then they assume funding doesn't exist.

It does. They just don't know where to look.

I spent 2 months mapping every major PhD scholarship I could find.

50 programmes across 6 continents.

Here's what surprised me:

→ 7 fully-funded options in North and South America alone
→ 19 programmes across Europe (not just UK and Germany)
→ 16 scholarships in Asia most Western applicants ignore
→ Hidden gems in Africa, Middle East, and Oceania

Some cover tuition only.

Many cover tuition + living expenses + travel.

A few even include family allowances.

The difference between funded and unfunded PhDs isn't talent.

It's knowing where to look.

I've mapped all 50 scholarships by region in the visual below.

Save it. Share it with someone who thinks they can't afford a PhD.

Links to each programme in the comments.

---

Which region are you considering for your PhD?

#PhDScholarships #PhDFunding

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Writing Guides

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Newsletter

Ready for more? 

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PhD 1:1 Autoplug 1 (Form-directed)

Doing your PhD while working full-time is lonely.

Your supervisor is juggling 12+ students. They don't understand how it feels to squeeze research into exhausted evenings and weekends. You get vague feedback like "needs more depth" with no clear direction. Months pass. You start wondering if you'll ever finish.

I've mentored 30+ mid to senior professionals through this exact struggle to successful completion.

20 years at a top 1% university. 25+ PhD examinations. I know where you're losing time.

I take on a small number of dedicated professionals for private premium 1:1 mentorship.

Interested? Start here: https://forms.gle/L3wuDxfAgvDA17SD8

1:1 mentoring Autoplug 2 (Form-directed)

Ready for more? 

Join my exclusive weekly Academic Pathfinder newsletter where I share the exact strategies that have helped hundreds of PhDs finish their PhDs on time, use AI responsibly in Research and thrive in academia. 

Get Wednesday's insider tactics delivered straight to your inbox: https://phdtoprof.com/newsletter

1:1 Mentoring Autoplug 3 (DM-directed)

You're trying to do three jobs at once: career, family, PhD.

Your supervisor has never lived that reality. I have spent 20 years mentoring professionals who have.

Private premium 1:1 mentorship for mid to senior professionals serious about finishing. Limited availability.

DM "MENTORSHIP" with your PhD stage and biggest challenge right now.


1:1 Mentoring Autoplug 4 (DM-directed, shortest)

Feeling behind on your PhD? Supervisor too busy to help? Wondering if you'll ever finish?

I provide private premium mentorship for mid to senior working professionals. 1:1 support. Limited spots.

DM "MENTORSHIP" with your situation.


My student cut her literature review from 4 months to 6 weeks.
Same number of papers. Same quality. Half the time.
She didn't work harder. She switched tools.

My PhD student was drowning in 200+ papers.

She spent 6 hours a day just reading and organising.

No time left for actual writing.

I introduced her to 5 tools.

Within a month, she cut her literature review time in half.

Here's what I've learned after testing 42 research tools with my students:

The right tool at the wrong stage is useless.

Reading tools can't help you write.
Writing tools can't analyse your data.
Analysis tools can't make your presentations.

Most researchers pick one tool and force it to do everything.

That's why they stay stuck.

The productive researchers I know have a system:

→ Different tools for reading and understanding papers
→ Different tools for presenting research
→ Different tools for organising literature
→ Different tools for writing and editing
→ Different tools for data analysis

After years of testing, I've mapped the best 42 tools across 6 research phases.

Each one categorised by:
→ What it actually does best
→ Whether it's free or subscription
→ Which research stage it belongs to

Paper Reading. Literature Review. Paper Review. Data Analysis. Paper Writing. Research Presentation.

I've organised everything in the visual below.

Save it. You'll reference it more than you think.

---

Which research phase slows you down most?

#ResearchTools #PhDLife
Consensus Paperpal Bohrium Inkwell Gamma

I nearly failed a PhD student last year, as an examiner.
Not because her research was bad.
Because I couldn't follow her thesis.

67,000 words. Good range of findings. Three years of work.

But the structure was a mess.

Her literature review jumped between 40 different themes.

Her methodology appeared in three different chapters.

Her discussion repeated her findings instead of interpreting them.

I spent 6 hours trying to understand what she'd actually discovered.

That's when I realised something.

Most PhD failures aren't about bad research.

They're about bad structure.

After examining 50+ theses, I've seen the same pattern:
→ Brilliant students who bury their contribution in chapter 6
→ Strong findings hidden behind confusing methodology sections
→ Conclusions that introduce new ideas instead of landing the plane

Your examiner has 4 hours to read 80,000 words.

If they can't follow your story, they can't pass your thesis.

So I created a visual guide to the 7-chapter structure that works every time.

It's in the image below.

Save it. Print it. Refer back to it with every chapter you write.

Because your thesis isn't just proving expertise.

It's telling a story clearly enough that others can build on it.

----

Which chapter is giving you the most trouble right now?

#PhDThesis #AcademicWriting

4 fake citations. 3 hours wasted. 1 very frustrated supervisor.
My PhD student learned the hard way: ChatGPT isn't built for finding papers.

I watched a PhD student spend 3 hours on a literature review.

He used ChatGPT for everything.

Finding papers. Checking citations. Summarising findings.

The result? 4 hallucinated references and a frustrated supervisor.

The problem wasn't AI.

It was using the wrong tool for the job.

After testing 20+ AI tools with my research students, here's what actually works:

ResearchRabbit
Best for:
→ Discovering papers from seed articles
→ Visualising citation networks
→ Finding connected research you'd miss
→ Mapping a new field quickly

Semantic Scholar
Best for:
→ Finding highly cited papers
→ Tracking influential authors
→ Getting AI-generated summaries (TLDR)
→ Building reliable reference lists

Consensus
Best for:
→ Answering research questions with real studies
→ Evidence synthesis across papers
→ Checking what the literature actually says
→ Finding citations that won't disappear

Paperpal
Best for:
→ Academic writing and grammar
→ Journal-specific language suggestions
→ Improving clarity and flow
→ Polishing drafts before submission

The real advantage?

Knowing which tool to open before you start.

ResearchRabbit can't polish your writing.
Paperpal can't find connected citations.
Semantic Scholar can't synthesise evidence.
Consensus can't map an entire field.

Stop switching between 10 tabs hoping for the best.

Start matching the tool to the task.

I've mapped out exactly when to use each one in the visual below.

Save it. Share it with your lab group.

Which AI tool do you rely on most in your research right now?

#AIforResearch #AcademicTools

My first paper got rejected 3 times. Because I kept submitting to the wrong journals.
This how to find the perfect journal for your paper without wasting months on rejections

I'd pick based on prestige. Or what my supervisor suggested years ago. Or whatever came up first in a Google search.

Each rejection cost me 3-4 months.

By the time I figured out the pattern, I'd lost over a year.

That's when a colleague showed me journal matching tools.

I wish someone had shown me these on day one of my PhD.

There are 12 free tools that analyze your abstract and match it to journals where your research actually fits.

Major publishers like Elsevier, Springer Nature, Wiley, and Taylor & Francis all have them.

So do open access platforms like PLOS and MDPI.

And independent tools like JournalGuide and Scimago that let you compare across publishers.

The difference?

Instead of guessing and waiting months for rejection, you get data-driven matches in minutes.

I've listed all 12 tools in the visual below with links found in the comment too.

Use 2-3 of them to cross-reference results before submitting.

Your research deserves the right audience.

These tools help you find it.

---

What's your biggest frustration with journal selection?

#AcademicPublishing #PhDLife

Proposal development

Writing your upgrade proposal while working full-time is a different kind of hard.

Your supervisor is juggling 12+ students. They don't understand how it feels to squeeze proposal writing into exhausted evenings and weekends.

You get vague feedback like "needs more depth" with no clear direction.

Your upgrade deadline approaches. You start wondering if you'll even pass.

I've mentored 30+ mid to senior professionals through this exact struggle to successful completion.

20 years at a top 1% university. 40+ PhD examinations. I know where you're losing time.

I take on a small number of dedicated professionals for private premium 1:1 mentorship.

Interested? Start here: www.phdtoprof.com

Academic Writing

Writing your PhD while working full-time makes this ten times harder.

Your supervisor gives feedback like "needs better flow" with no clear direction.

You rewrite the same paragraph five times after a 10-hour workday.

You wonder if your English is good enough. If your arguments are strong enough. If you'll ever get published.

Frameworks and free guides help. But they can't give you personalised feedback on your specific writing.

I've mentored 30+ mid to senior professionals through this exact struggle to successful completion.

20 years at a top 1% university. 40+ PhD examinations. 130+ publications.

I know where your writing is losing marks. And I know how to fix it fast.

I take on a small number of dedicated professionals for private premium 1:1 mentorship.

Interested? Start here: https://forms.gle/L3wuDxfAgvDA17SD8

Abdel-Hameed Badawy

I almost quit my PhD in year 2. The isolation was crushing. 
Then I discovered academic podcasts. Suddenly, I had mentors in my earbuds.

People who understood the imposter syndrome.

The rejection letters.

The 3am writing sessions.

I wasn't alone anymore.

Although there were not back then as many podcasts as they are now.

That's why, I now curate them for my students.

So they never feel as alone as I did.

Here are 9 podcasts that have helped hundreds of my mentees survive (and thrive) in academia.

I've organised them by career stage:

For PhD Students:
→ The PhD Life Raft – practical survival strategies 
by Emma Brodzinski
→ PhD Talk – real solutions for everyday PhD problems
by Eva Lantsoght
→ Better Biologist – research skills and career development
by Kenan Kraković
→ The PhD Life Coach – support with PhD journey
Vikki Wright

For Early Career Researchers:
→ The Struggling Scientists Podcast – proof you're not alone
by Jayron Habibe, PhD and Suzanne Van Wouw
→ Beyond the Thesis With Papa PhD – career planning that works
David Mendes da Silva, PhD
→ Changing Academic Life – navigating academia's challenges
by Geraldine Fitzpatrick

For Academia to Industry Transition:
→ PostPhDirections – insider tips from hiring managers
Elena Hoffer, PhD  
→ PhD Career Stories – real stories of success and failure
by Tina Persson - Adaptability Intelligence, AQ


Turn your commute into a masterclass.

Your coffee break into a mentoring session.

Your workout into career development.

I've put all 9 with descriptions in the visual below.

Save it for when you need it.

---

Which podcast got YOU through your PhD journey?

#PhDLife #AcademicPodcasts

Helping Overwhelmed Doctoral Researchers| Specializing in Supporting Creatives with ADHD| Crafting Personalized Solutions for PhD Success and Work-life Balance| 1-to-1 coaching| Group Programmes| Institutional Packages

Vice Dean for Research at Universidad San Francisco de Quito & Associate Professor at Delft University of Technology

I help STEM students find and land their dream lab positions 🧬 Book an intro session below to see how we can work together.

Professional life coach at ThePhDLifeCoach.com

I share advice to help make your academic journey less of a struggle | Co-founder The Struggling Scientists | Communications Officer at Westerdijk Institute

Co-Founder The Struggling Scientists | Academic Development Coordinator |

Scientific & Medical Communications | Knowledge Mobilization & Research Impact | PhD | Health & Public Engagement | EN / FR / PT

Developing people and culture as a consultant, facilitator, trainer, speaker, podcaster, coach. Univ Professor i.R., TU Wien.

Help PhDs to kick-start careers outside of academia | CEO @Alma.me | Complementing universities services for PhDs & Postdocs to secure jobs outside academia | Speaker I Podcast Host @PostPhDirections

Executive, Leadership & Career Coach | Adaptability Intelligence | AQ | Innovation | Supporting people & teams and organisations in a fast-changing world driven by AI & technology

How to email potential PhD supervisors. The 6-step email system

Priya had a strong research proposal and excellent grades.

But her emails were long, generic, and focused entirely on herself.

Professors deleted them without reading past the first line.

That's when I taught her my 6-step email system.

Her next 10 emails got 8 responses.

Here's what changed everything:

Professors get 25+ emails weekly from students wanting PhD supervision.

Your email has 15 seconds to stand out.

Most applicants write emails like personal statements.

Formal. Lengthy. Self-focused.

That's why they get ignored.

Here's the system that transformed Priya's response rate:

1. Lead with relevance
Start with your strongest connection to their work. Not your life story.

2. Get to the point fast
Say what you want in the first two sentences. They're reading between meetings.

3. Show you know their work
Mention a specific paper or project. Prove you're not mass-emailing.

4. Explain your value
Don't just ask for supervision. Show what you bring to their research group.

5. Suggest a clear next step
Not "I hope to hear from you." Try "Would you have 15 minutes to discuss this?"

6. Make it scannable
Short paragraphs. White space. They might read it on their phone.

The results for Priya:

→ Response rate jumped from 4% to 80%
→ 4 supervisors agreed to discuss her proposal
→ 2 offered to support her scholarship application
→ She secured full funding at her first-choice university

The difference wasn't her qualifications.

It was making her emails impossible to ignore.

Most applicants write emails about what they want.

Smart applicants write emails about what they can contribute.

I've included a template with all 6 elements in the visual below.

Save it before your next outreach.

---

What's your biggest challenge when contacting potential supervisors?

#PhDLife #PhDScholarship

I've examined 47 PhD theses.
These phrases make me worry within 5 pages.
Here's how to fix them:

Every examiner has a mental checklist.

We don't talk about it. But we all have one.

And certain phrases trigger alarm bells before we've finished your introduction.

Here are 9 red flags that make examiners pause:

❌ "It is widely known that..."
What examiners think: Where's the citation? Are you avoiding the literature?
✅ Better: "Research has consistently shown (Author, Year)..."

❌ "This proves that..."
What examiners think: Statistical overreach. Do you understand your methods?
✅ Better: "These findings suggest..."

❌ "Obviously..." or "Clearly..."
What examiners think: If it's obvious, why are you doing a PhD on it?
✅ Better: "The evidence indicates..."

❌ "A lot of research shows..."
What examiners think: Vague. Which research? How much is "a lot"?
✅ Better: "Multiple studies (Author, Year; Author, Year) demonstrate..."

❌ "In conclusion, more research is needed."
What examiners think: Everyone writes this. What did YOU discover?
✅ Better: "These findings open specific questions about [concrete direction]..."

❌ "The researcher believes..."
What examiners think: This isn't a blog post. Where's your evidence?
✅ Better: "Based on these results, it appears..."

❌ "Perfect" / "Prove" / "Never" / "Always"
What examiners think: Absolutist language signals inexperience.
✅ Better: Use hedging words: may, could, suggests, indicates

❌ "Very unique" / "Extremely important"
What examiners think: These intensifiers weaken your argument, not strengthen it.
✅ Better: Let your evidence speak. Remove the adverb entirely.

The pattern?

Weak language signals weak thinking.

Even when your research is solid.

These phrases create doubt before you've had a chance to prove yourself.

Tools like Paperpal catch these patterns before submission.

→ Language Editing flags weak phrases automatically
→ Academic Paraphraser transforms vague writing into precise alternatives
→ Consistency Checks spot patterns that signal rushed work

I wish I'd had this when writing my own thesis.

Save this for your next chapter draft.

—

Repost and comment 'GUIDE' to receive my FREE "PhD Examiner Red Flag Phrase Guide: 50 Phrases to Avoid, And What to Write Instead" within 72 hours.

What phrase have you been told to remove from your thesis?

#PhDChat #AcademicWriting

Paperpal

How to read research papers 10x faster without losing your mind. I spent years doing it wrong. Then I found a method that cut my reading time by 70%.


Reading every word. Start to finish. Taking 3 hours per paper. Forgetting page 1 by page 15.

Sound familiar?

Then I found a method that cut my reading time by 70%.

It's called the Three-Pass Approach. Here's exactly how it works:

Pass 1: The Bird's Eye View (5-10 minutes)

Read only these parts:
→ Title, abstract, introduction
→ Section headings
→ Conclusions
→ Scan the references

That's it. You now know if this paper is worth your time.

Pass 2: The Content Grab (up to 45 minutes)

Now read with more care.
→ Study the figures and graphs carefully
→ Mark references you need to read later
→ Jot down key points in the margins

You can now explain this paper to someone else.

Pass 3: The Deep Dive (1-3 hours)

This is for papers you MUST understand fully.
→ Walk through the methodology step by step
→ Challenge every assumption
→ Identify hidden weaknesses

After this? You own that paper.

Here's the truth:

Most reviewers and readers only do ONE pass.

If they can't understand your paper in 5 minutes, they'll move on.

That's why your abstracts matter. Your headings matter. Your structure matters.

The three-pass method saves time. It builds understanding. And it makes you a better writer too.

I've used this approach for years. For conference reviews. For literature surveys. For background research.

It works.

Source: Keshav, S. (2007). "How to Read a Paper." ACM SIGCOMM Computer Communication Review, Volume 37, Number 3.

---

How many papers do you read per week? And do you have a system?

#PhDLife #AcademicTwitter

2 years for original research.
8 weeks for a systematic review.
One nearly broke me. The other launched my career.

Same journal. Same citation impact. Here's how to do it:

After my first 3 papers got rejected, a mentor asked me:

"Why are you making publishing so hard?"

I didn't understand the question.

Then she showed me her CV.

6 systematic reviews. All highly cited. Published in top journals.

She explained:
→ Systematic reviews synthesise what exists
→ They don't require new data collection
→ Journals love them because readers actually use them
→ And they get cited MORE than original research

My first systematic review took 4 months.

Accepted in 8 weeks. Now has 100+ citations.

That paper got me invited to editorial boards.

But here's what most people get wrong:

The methodology section makes or breaks your review.

Weak methodology = desk rejection.

After publishing multiple systematic reviews, I developed a 7-step framework:

1. Choose your Methodological Framework
PRISMA, Cochrane, or another established guideline.

2. Document your Search Strategy
List every database and your exact search terms.

3. Define your Inclusion Criteria
Specify populations, interventions, outcomes, and study designs.

4. Explain your Data Collection
Describe who extracted data and how you resolved disagreements.

5. Describe your Study Characteristics
Provide context on study sizes, locations, and methods.

6. Report your Quality Assessment
Name the bias tools used and how quality shaped your conclusions.

7. Detail your Data Extraction
Show your process so others can replicate it.

Miss any of these and reviewers will send it back.

I've broken down each step with real examples from published papers in the visual below.

Save it. Check your methodology against it before you submit.

---

Have you tried a systematic review yet? What's holding you back?

#SystematicReview #AcademicWriting

How do you turn 100+ papers into one coherent argument? I had no idea. 
Then I developed a 7-step system that changed everything.

"This is an incoherent mess."

My supervisor's words. About my literature review.

I had read 100+ papers over 6 months.

Notes scattered across 15 different documents.

But I couldn't write a single coherent paragraph.

I was drowning in information without direction.

That's when I developed my 7-step system.

It turned my chaos into my most cited publication.

Here's the framework:

1. Define Your Research Focus
Start with one clear question. Know exactly what's missing.

2. Search Systematically
Right databases. Clear search terms. Document everything.

3. Evaluate What You Find
Rate quality. Note strengths and weaknesses. Spot patterns.

4. Connect the Ideas
Group by theme. Map how concepts link. See the bigger picture.

5. Create Your Framework
Link theory to evidence. Build a logical argument flow.

6. Write and Refine
Draft from your outline. Get feedback. Polish ruthlessly.

7. Share Your Work
Present at conferences. Submit to journals. Build connections.

The results:

→ My literature review became a standalone publication
→ 47 citations in the first year
→ Two international collaborations started from it
→ It became the foundation for my entire PhD

The difference wasn't reading more papers.

It was having a system to organise what I read.

Most PhD students collect information without a plan.

Smart students build knowledge systematically.

I've broken down each step in detail in the visual below.

Save it before your next literature review.

---

Which step feels most challenging for you right now?

#LiteratureReview #PhDLife

3 patterns of misuse of AI in research I see every week that terrify me:

1. Researchers uploading confidential interview transcripts to ChatGPT

2. PhD students submitting AI-generated text without checking citations

3. Supervisors who have no idea what tools their students are using

Each of these can end a career.

I've seen researchers make the same mistakes over and over:

→ Uploading participant transcripts to ChatGPT (career-ending privacy violation)
→ Trusting AI-generated citations without checking (instant credibility destruction)
→ Feeding reviewer comments into AI (confidentiality breach)
→ Submitting to journals that ban AI entirely (automatic rejection)

The problem isn't AI itself. 

It isn't that researchers are using AI.

It's that nobody gave them the rulebook.
It's that nobody taught researchers the rules.

Universities don't cover this. Supervisors don't know it. Ethics boards are still catching up.

So after advising 500+ researchers, I created 13 golden rules for using AI ethically in research.

I've put them all in the visual below.

Screenshot it. Print it. Share it with your lab group.

Because what's acceptable today might be a retraction tomorrow.

---

Which of these 3 patterns have you witnessed?

#AIinResearch #AcademicIntegrity

Wrong PhD supervisor = dropped out within 2 years.
Right supervisor = completed while working full-time.
Here's how to choose well...

I've seen this pattern hundreds of times.

The supervisor you choose matters more than the university name on your certificate.

Here's what most working professionals get wrong:

They chase the famous professor.

Big name. Long publication list. Impressive CV.

But famous professors are busy. They travel. They have 20 other students.

They don't have time for someone who can only meet on Tuesday evenings.

Here's how to find the right supervisor instead:

1. Ask about flexibility first
Not publications. Not h-index. Flexibility.
"How do you work with students who have full-time jobs?"
If they hesitate, that's your answer.

2. Check their response time
Send them an email. See how long they take to reply.
If it takes 3 weeks now, imagine waiting 3 weeks for feedback on your draft.

3. Ask their current students
Not the ones who finished. The ones still struggling through.
"Does your supervisor understand when work gets busy?"
Their answer tells you everything.

4. Look for kindness over celebrity
A supportive generalist will get you to completion.
An unsupportive expert will get you to withdrawal.

5. Trust your gut in the first meeting
Do they ask about your life? Your job? Your constraints?
Or do they only talk about their research agenda?
You need a partner, not a boss.

Red flags to watch for:
"I expect you in the lab every day"
"Part-time PhDs never finish"
"I don't do video calls"

These aren't bad supervisors. They're just wrong for you.

The right supervisor exists.

You just need to ask the right questions before you commit.

What's the ONE question you'd ask a potential supervisor?

#PhDLife #AcademicTwitter

I used to spend 60+ hours searching for research gaps. Now I do it in under 60 minutes. Here's the free AI tool that changed everything.

Last year, every new project started the same way:

47 open tabs.
12 half-read papers.
Zero clarity.

Then a colleague showed me AllSci Corp.

It maps 20+ million research papers into hypotheses and evidence.

You don't just see what exists.

You see what's missing.

Last month I typed in my research topic.

Within 15 minutes I spotted a connection between two fields nobody had explored.

That gap became my latest paper.

The tool does 4 things brilliantly:
→ Organises results by hypothesis, not just keywords
→ Shows supporting evidence for each claim
→ Identifies the leading authors you should be reading
→ Visualises the entire research landscape so you spot gaps others miss

Here's how to use it in 4 simple steps:

Step 1: Enter your research topic
Type your question into the search bar and select "Detailed Search" for AI-powered discovery.

Step 2: Review hypothesis-backed results
Get results organised by hypothesis and evidence. Skip the noise. Go straight to verified claims.

Step 3: Explore hypotheses and key authors
Dive deeper into specific hypotheses. Identify the leading researchers you should be reading.

Step 4: Visualise the research landscape
Explore trends, journals, and connections. Use knowledge graphs to spot gaps nobody else sees.

I've mapped out each step with screenshots in the visual below.

The best part? It's completely free for researchers.

---

What research gap are you currently struggling to define?

#ResearchTools #PhDLife

AllSci Corp.

Your keyword search is lying to you.
It's hiding 40% of your literature.
Here's how to catch it

I've examined 47 PhD theses.
The same problem appears in almost every one.
Gaps in the literature review the student didn't know existed.

Not because they were lazy. Because keyword search failed them.

These questions consistently reveal gaps:

"Why haven't you cited the [adjacent field] literature?"
"This has been studied extensively in [discipline]. Why no mention?"
"How do you know you haven't missed something important?"

Most students can't answer the last one.

Why? The concept stays the same. The words change.

"Primary care" in the UK. "Family medicine" in the US. "General practice" in Europe.

Same system. Different terminology. Invisible walls.

My estimate: 40% of relevant papers get missed because of terminology barriers.

That's not sloppy research. That's a structural problem.

Here's the 5-step fix I share with every researcher I mentor:

1. Question your keywords: What would another field call this?
2. Search in sentences: Let semantic search find the right terms.
3. Follow citations: One good paper reveals terminology you didn't know.
4. Get outside eyes: A colleague in a related field spots blind spots.
5. Test before submitting: Different phrasings, different results = gaps.

Tools like Consensus use semantic search to understand concepts, not just keywords.

Same research question. 4x the relevant literature.

But tools alone aren't enough. You need to use them responsibly.

I've created a free guide: "How to Find the Papers You Don't Know You're Missing Using AI Responsibly"

Repost and comment 'SEARCH' below and I'll send it within 72 hours.

What terminology barriers have you found in your field?

#PhDChat #AcademicWriting

Consensus

You should not have to upload the same research paper six times to six different AI tools. But that is exactly what most PhD students do every week.
Here is the fix.

Every AI tool wants you to start from scratch.

This is ridiculous. But it is fixable.

A colleague came to me last week completely frustrated.

She had uploaded the same 38 papers to three different AI platforms.

108 uploads for documents she already had saved.

When she switched from Claude to ChatGPT for a different task, she lost everything. All the context. All the connections. Gone.

Each platform holds your documents hostage inside its own walls.

Your knowledge becomes platform dependent.
Your workflow becomes fragmented.
Your time disappears into endless re-uploads.

I showed her awRAG.io

Here is what it does:

You import your Zotero (or Endnote or others) library once.
One click.
All your papers indexed.

Then you access them from ANY AI tool.

ChatGPT. Claude. Gemini. Perplexity.

A browser extension connects your entire library to whatever AI you are using.

No more re-uploads.
No more starting over.
No more lost context.

The search understands what you mean.

Type "that qualitative methods paper about healthcare from 2021" and it finds exactly what you need. With relevance scores. With the specific passages.

For your PhD this means:

Literature reviews that pull from everything you ever saved.

Methodology sections backed by papers you forgot you had.

Theoretical frameworks built on your entire collection.

And here is what matters for sensitive research:

Your data stays in the EU.

GDPR compliant.
No AI training on your documents.
SOC 2 Type II certified infrastructure.

Your thesis data is not feeding someone else's model.

I created a step-by-step visual showing how to set this up.

Takes 10 minutes.

The guide is below.

How many times did you re-upload the same paper to different AI tools this week?

#PhDLife #AcademicResearch

awRAG.io

I've examined 43 PhD students.
Only 12 had the training they actually needed.
Here's what nobody tells you about PhD programs:


The rest? Left to figure it out alone.

Here's what nobody tells you about PhD programs:

Universities charge $80K+ in fees.
They teach you theory.
They teach you research methods.
They teach you how to run experiments.

But they don't teach the skills that actually determine your career.

→ How to network strategically
→ How to navigate the hidden job market
→ How to write papers that get published
→ How to manage your supervisor relationship
→ How to position yourself for faculty or industry roles

I learned this the hard way.

My PhD program never mentioned career strategy.

I spent 2 years applying to jobs with no responses.

No one told me 70% of PhDs struggle with the same thing.

No one told me only 8% land faculty positions.

The gap between PhD training and career reality is massive.

So what do successful PhDs learn outside the curriculum?

5 skills top students develop on their own:

1. Strategic publishing (quality over quantity)
2. Building a research identity before graduating
3. Conference networking that leads to opportunities
4. Writing that connects with readers, not just reviewers
5. Managing up with supervisors who are too busy

These aren't taught in any module.

But they separate those who thrive from those who struggle.

I made a promise when I became a professor:

I would teach what I wished someone had taught me.

The system isn't broken by accident.

It's designed for research output, not researcher success.

But you don't have to wait for programs to change.

Start building these skills now.

What skill do you wish your PhD program taught you?

#PhDLife #AcademicCareer

----
Sources:

Hazell, C. M., Niven, J. E., Chapman, L., Roberts, P. E., Cartwright-Hatton, S., Valeix, S., & Berry, C. (2021). Nationwide assessment of the mental health of UK Doctoral Researchers. Humanities and Social Sciences Communications, 8, Article 305. https://lnkd.in/dr6PzSmK

The mental health of PhD researchers demands urgent attention. (2019). Nature, 575(7782), 257-258. https://lnkd.in/du9HcyAF

Larson, R. C., Ghaffarzadegan, N., & Xue, Y. (2014). Too many PhD graduates or too few academic job openings: The basic reproductive number R₀ in academia. Systems Research and Behavioral Science, 31(6), 745-750. https://lnkd.in/dyda-SKa

Evans, T. M., Bira, L., Gastelum, J. B., Weiss, L. T., & Vanderford, N. L. (2018). Evidence for a mental health crisis in graduate education. Nature Biotechnology, 36(3), 282-284. https://lnkd.in/dtrJCzsF

A PhD student in our programme almost submitted a paper with 3 fake citations. They looked perfect.
5 AI Hallucination Red Flags & How to Spot Them:

ChatGPT had generated them. 

Author names. Journal titles. Even DOIs.

None of them existed.

Her supervisor caught them the night before submission.

This is why universities are panicking about AI in research.

And why I now run workshops for institutions on responsible AI use.

Here's what every researcher needs to know:

The 5 AI Hallucination Red Flags:

1. Citations you've never seen before
AI loves inventing plausible-sounding references. If you didn't find it yourself, verify it exists.

2. Statistics that seem too perfect
"Studies show 73.4% of researchers..." If it's suspiciously precise, it's probably fabricated.

3. Author names that don't appear on Google Scholar
Real academics have publication trails. Fake ones vanish when you search.

4. Journal names with slight misspellings
"Journal of Applied Neuroscience" vs "Journal of Applied Neurosciences" — one exists, one doesn't.

5. Claims that contradict your domain knowledge
You're the expert. If something feels wrong, it probably is.

The Verification Protocol I Teach in University Workshops:

Step 1: Never use AI citations directly
Step 2: Cross-reference every claim with primary sources
Step 3: Check every DOI link actually resolves to a real paper
Step 4: Document exactly how AI was used in your methods
Step 5: When uncertain, disclose more rather than less

One fake citation can end a career.

Five minutes of verification can save it.

---

What's the most surprising AI error you've caught in your research?

#AIinResearch #AcademicIntegrity

70% of researchers will use AI incorrectly this year.
Not because they're lazy.
Because nobody taught them the right way.

I've spent 15 years in academia. Made Professor by 40. Helped over 10,000 researchers navigate their careers.

And I've never seen a shift this fast.

AI is changing everything about how research gets done. 

The problem? Most researchers are using it to replace their thinking instead of support it.

That's a mistake that will catch up with them.

I joined Jeremy Bennett to break down exactly how to use AI ethically in research.

We covered:
→ Why AI should support process, not replace thinking 
→ How to manage research while working full-time 
→ The role mentors and structure play in finishing faster 
→ Why "sounding human" is becoming a competitive advantage 
→ How to choose a PhD topic you'll actually stick with

The researchers who get this right will pull ahead. The ones who don't will spend years rebuilding trust.

Have a listen. Link in Jeremy's post below.

What's your biggest question about using AI in your research?

Wondering how to use AI ethically in research?
 
Emmanuel Tsekleves shows you how.
 
In this episode, discover:
 
How to use AI in research the right way. 
The importance of building support systems.
How to manage your time while working full-time.
Why AI should support process, not replace thinking. 
How mentors and structure help you finish faster. 
Why passion matters when choosing a PhD topic. 
Why sounding human is starting to matter.
 
What’s your biggest 2026 goal?  
↳ Let us know in the comments.

🩺I take researchers from goals to published results 🇮🇳 🇬🇧 Founder II Academic Writing Mentor || AI researcher || Homeopath ✅️Need Research Solutions❓️ Go to my ABOUT section.🔻

I specialize in testing AI tools for literature reviews, academic writing, editing and statistical analysis, sharing insights that enhance research productivity and workflow.

As a PhD in Life Sciences, I am passionate about guiding academics and PhDs from research concept to completion, ensuring they have the right tools, methodologies and mentorship to succeed.

🎯 How I Help Researchers & PhDs??

Through 

🟪 RESEARCH MADE CLEAR 🟪

I offer consultancy services in:

✅ Scientific Writing & Editing: Ensuring clarity, coherence and impact in research papers.
✅ PhD Mentorship & Research Methodology: Supporting scholars in thesis writing, research design, and proposal development.
✅ Data & Statistical Analysis: Providing expertise in biostatistics, data analysis and result interpretation.
✅ Faculty & Student Development: Training in AI-powered research tools & ethical considerations.
✅️ Webinars & Workshops: How to use AI tools for Literature Review, research writing and Research Management, ethically.

🔍 My Experience & Research Contributions:

With 10+ years of experience across multidisciplinary research projects, I have:
📌 Led & coordinated multiple research trials and longitudinal surveys as a Research Manager.
📌 Published 8+ peer-reviewed papers in evidence-based medical writing.
📌 Developed the MAPNI questionnaire—a validated tool assessing perceived stigma towards mental illness in India.
📌 Taught Research Methodology & Biostatistics as an 'Assistant Professor', mentoring 500+ academic professionals in thesis & dissertation writing.
📌 Supervised successful scholarship applications for the Short-Term Studentship in Homoeopathy (STSH, CCRH, India).

📢 Let’s Connect
✅️Need expert guidance in research writing, AI tools or statistical analysis?
✅️Looking for collaborations, training sessions or mentorship?

💌 Email: dps@researchmadeclear.com
📩 DM me here on LinkedIn!

Dr Priya Singh PhD💜MD (Hom.)

More research is needed’ is not a research gap

Saying “there isn’t much research” is not enough. 
Reviewers want to know exactly what is missing and there are different kinds of gaps.

Here are some of the most important ones researchers should recognize:
 🔹 Evidence gap – We simply don’t have enough solid data yet.
 🔹 Knowledge gap – We don’t fully understand why or how something works.
 🔹 Practical gap – Research exists, but it’s not being used in real settings.
 🔹 Population gap – Certain groups are left out of studies.
 🔹 Methodological gap – The tools or methods used so far are weak or outdated.
 🔹 Theoretical gap – No strong theory explains the issue clearly.
 🔹 Geographical gap – Research is focused on some regions and ignores others.
 🔹 Empirical gap – Important variables haven’t been studied together.
 
When you name the type of gap, your study stops sounding general and starts sounding necessary.

If you’re writing a proposal or thesis, start by asking:
 What kind of gap am I actually filling?

PS: What would you add here?
Share in the comments

REPOST to help others.

Follow Dr Priya Singh Founder, Research Made Clear for more practical insights

Subscribe to my YT channel: https://lnkd.in/eSBGiCz8 for Tutorials and tips

Dr Priya Singh PhD💜MD(Hom.)

Helping PhDs & researchers complete and publish high-quality research PhD mentor || Thesis reviewer || Academic writing expert Training research professionals in working with AI

Before your next submission, read this 👇 

If you’re trying to publish in a good journal and feeling stuck, this paper is genuinely worth your time

It walks you step by step through what actually makes a paper publishable — in plain, practical terms.

Instead of vague advice, it explains:
✅ What editors and reviewers look for in a strong paper
✅ Why many solid studies still get rejected
✅ How to write each section — title, abstract, introduction, methods, findings, discussion, and conclusions — in a way that strengthens your chances of acceptance
✅ Common mistakes that quietly kill good manuscripts

What makes it especially helpful is that it focuses on presentation, structure, and clarity.

If you’re:
🔹 revising a manuscript
🔹 preparing your first journal article
🔹 mentoring students on academic writing
…this paper works like a behind-the-scenes guide to the review process.

Definitely one to read with your own draft open beside you. You’ll start spotting exactly where your paper can be sharper and more convincing.

PS: If you could get expert help on just one section of your paper, which would it be?

Share in the comments

REPOST to help others.

Follow for more research writing insights.

HOW TO WRITE REALLY GOOD ARTICLES 

Publishing in Q1 or high-impact journals is not about luck 😯 

This editorial makes one thing very clear 

most papers are rejected not because the research is weak, but because it sends the wrong signals.

✅ Lack of originality.
✅ Unclear contribution.
✅ Narrow topics.
✅ Flawed design.
✅ Poor writing flow.
✅ Out-of-scope submissions.

These are not small issues. They are instant filters.

What I appreciated most is how practical the message is:
 
1. Editors look for generalisable insight, not local descriptions  
2. Theory and contribution matter, even in applied work
3. Methodological rigour is expected, not optional
4. Writing quality is part of scholarly quality

If you are submitting repeatedly and getting desk-rejected, this editorial is worth reading slowly and strategically.

PS: What was the hardest publishing lesson you had to learn the slow way?
Share in the comments

REPOST to help others publish better.

Follow for more tips on publishing 

-----------------------------------------------------------------------------------------------

I’m Dr Priya Singh Founder, Research Made Clear 
I offer review, academic editing, and proofreading to help improve quality of your resaerch papers and thesis before submission. DM if you’d like support.

Publishing in Premier & Q1 Journals

If your qualitative paper ends at themes, you may be stopping too early.

Rigor in qualitative research is not just about coding neatly or reporting themes clearly. 

It is about thinking conceptually and being intentional about how findings are elevated into a model that others can use, test, and build on.

Process transparency matters most.

When readers can see how you moved from raw data to ideas, your work becomes easier to trust, teach, and replicate across disciplines and methods.

If you struggle with the question
 “I have good themes… now what?”
 this paper is worth your time 👇 

PS: Is your qualitative paper descriptive or conceptual?
Share in the comments

REPOST to help others.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Hi I am Dr Priya, Founder Research Made Clear

I help researchers turn qualitative analysis into publishable papers. Need help? 

Feel free to DM.

Step by Step Process of Thematic Analysis in Qual Research

No one teaches you the real rules of publishing ❌ 

PhDs are trained to collect data, run analyses and read theory

But the rules that actually decide accept or reject?

Those are rarely taught.

Academic publishing operates on a set of tacit rules about contribution, writing, positioning, and reviewer expectations that many supervisors assume you already know.

PhDs struggle because they were never shown how the game is really played.

PS: Which of these hidden rules did no one tell you early in your research journey?

Share in the comments

REPOST to help others.

100 RESEARCH RULES OF THE GAME

A strong conclusion doesn’t close the paper. It opens the conversation.

Your conclusion is not a summary.
It’s your final chance to shape how the reader remembers your work.

I suggest my mentees to follow these for writing a strong conclusion 👇

1. Start by answering the research question clearly.

Say it plainly.
What did you find overall?
If someone only reads your conclusion, they should still understand the main takeaway.

2. Zoom out before you close

Move from specifics to the bigger picture.
Explain what your findings mean for the field, practice, policy, or future research.
This shows maturity, not exaggeration.

3. Link back to the problem you started with

A good conclusion feels like a full circle.
Remind the reader why the problem mattered in the first place and how your study helped address it.

4. Acknowledge limitations calmly

One or two sentences are enough.
State them factually, without apologising or weakening your work.
Confidence comes from transparency.

5. End with forward direction, not repetition

Don’t restate results again.
Instead, point forward:
 – What should happen next?
 – What questions remain open?
 – What could others build on?
That’s how research stays alive beyond the paper.

Save this if you’re writing a paper, thesis, or dissertation.

PS: What do you personally struggle with most when writing conclusions?
Share in the comments.

REPOST to help others.

Follow Dr Priya SIngh for more research writing guides Research Made Clear

Getting a paper published isn’t about “luck”
It’s about fit, clarity, and discipline.

This checklist from SAGE Publishing quietly reminds us of what many early researchers miss:

✅A good paper answers a real question, not just a gap for the sake of a gap
✅Journals care deeply about scope, structure, and audience
✅Small things (word limits, references, blind review rules) matter more than we think
✅Editors want clarity, not cleverness
✅And publication is as much about preparation as it is about results

Strong research fails every day because it wasn’t positioned well.

Before submitting, ask yourself: Does this journal want the paper I’ve written or am I hoping they will?

PS: What’s one lesson about publishing you learned the hard way?
Share in the comments

REPOST to help others

Goldmines for learning how real PhDs are written 👇 

High-quality, free PhD thesis repositories worth bookmarking:
1. 1. Global & Multi-Institution Aggregators
✅ Open Access Theses and Dissertations (OATD)
🔗 https://oatd.org
✅ The Networked Digital Library of Theses and Dissertations (NDLTD)
🔗 http://www.ndltd.org
✅ EBSCO Open Dissertations
🔗 https://lnkd.in/eCiUEiGb
✅ EThOS (British Library)
UK doctoral theses with many full-text downloads.
🔗 https://ethos.bl.uk
✅ Thesis Commons
A community-driven open repository for author-deposited theses and dissertations.
🔗 https://thesiscommons.org/

2. National / Regional or Subject-Specific Repositories
✅ Shodhganga (India)
Large open repository of Indian PhD theses.
🔗 https://lnkd.in/eRqD6Qsk
✅ Swedish University Dissertations (dissertations.se)
Index of dissertations in English from Swedish universities, many with PDFs.
🔗 https://lnkd.in/eHTSr9Cn
✅ theses.fr (France)
Access point to French doctoral theses across French institutions.
🔗 https://www.theses.fr/

3. Institutional Open Repositories 
✅ MIT DSpace
Theses and dissertations from MIT.
🔗 https://dspace.mit.edu
✅ Harvard DASH
Harvard University open access repository.
🔗 https://dash.harvard.edu
✅ Cambridge Apollo
University of Cambridge open repository.
🔗 https://lnkd.in/e86rnQn2
✅ SUNScholar (Stellenbosch University)
South African university theses.
🔗 https://scholar.sun.ac.za
✅ OhioLINK ETD Center
Consortium repository for theses from participating Ohio universities.
🔗 https://etd.ohiolink.edu/
✅ eScholarship (University of California)
Open access repository including UC theses/dissertations.
🔗 https://escholarship.org/

4. Tools & Directories that Aid Thesis Discovery
✅ BASE (Bielefeld Academic Search Engine)
Indexes open access content from repositories worldwide (theses included).
🔗 https://lnkd.in/eWbpj-hv
✅ WorldCat (free advanced search)
Global library catalogue where you can filter for theses/dissertations.
🔗 https://www.worldcat.org
✅ OpenDOAR / ROAR
Directories of open access institutional repositories (many contain theses).
🔗 https://lnkd.in/eDvBvcAm

Here is how PhD scholars can benefit from open thesis repositories:
• See how strong research questions are framed
 • Understand chapter structures that actually passed examination
 • Learn how methods and limitations are written in practice
 • See discipline-specific writing styles, not generic templates
 • Identify gaps and angles that published papers often hide
 • Reduce anxiety by realising: your draft doesn’t have to be perfect to be valid

PhDs are not written by magic. They are written by patterns you can learn.

PS: Which thesis repositories have helped you most during your PhD journey?

Share in the comments.

REPOST to help others

What do you check first in a paper?

Papers with clear charts and diagrams get read more, understood faster, and remembered longer.

Why?
Because reviewers and readers don’t read line by line.

They scan.

Figures act as cognitive anchors:
 ✅ They summarise complex arguments at a glance
 ✅ They guide the reader through results
 ✅ They signal clarity, rigour, and confidence
 ✅ They reduce mental effort for busy reviewers

A strong figure can do what three paragraphs struggle to explain.

That’s why well-designed diagrams often get cited, reused in talks, and remembered while text-heavy papers get skimmed and forgotten.

This doesn’t mean “add more charts.”

 It means add purposeful visuals that carry the argument forward.

If your key finding cannot be visualised clearly, it is often not clear enough yet.

📌 Tools for data visualisation (charts, graphs, dashboards)
Tableau – Interactive dashboards; widely used in research and policy settings
Microsoft Power BI – Strong for structured data and reporting
GraphPad Prism – A staple in biomedical and experimental research
R (ggplot2) – Publication-quality figures with full analytical control
Python (Matplotlib / Seaborn) – Flexible, reproducible scientific visualisation

📌 Tools for scientific illustrations & diagrams (conceptual figures, workflows)
BioRender – Gold standard for life-science figures
Mind the Graph – Accessible visuals for papers and presentations
Adobe Illustrator – High-end, journal-ready figure design
Inkscape – Free alternative for scalable diagrams
PowerPoint – Still one of the most used tools for assembling publication figures

PS: What more tools would you add?
Share in the comments

Repost to help others.

Follow Dr Priya Singh, Founder Research Made Clear for morte academic writing tips.

If your research gap is weak, your whole study stands on shaky ground.

Finding a real research gap is about seeing what is missing, unclear, or unresolved in what we already know.

Here are some Do’s and Don’ts to help you get it right 👇
✅ DO’s When Identifying a Research Gap

1. Do read for patterns, not just papers

Don’t treat articles like isolated pieces. 
Look for:
✅Repeated limitations
✅Conflicting findings
✅“Future research should…” statements
 Patterns point to gaps.

2. Do map what is known vs. unknown
Make two columns:
✅What researchers agree on
❓What is still unclear, under-tested, or debated
 Gaps live in the second column.

3. Do narrow the context
A gap becomes strong when it is specific:
✅A certain population
✅A setting or country
✅A method not yet used
✅A variable not yet examined together with others
Broad gaps are weak. Focused gaps are publishable.

4. Do check recency
A gap from 2012 may already be filled. Always check the latest 3–5 years of literature before finalizing your gap.

5. Do connect the gap to a real problem
Editors and reviewers love gaps that matter.
 
Ask: Who is affected if this gap remains unaddressed?

❌ DON’Ts That Weaken Your Research Gap

1. Instead of “No one has studied this”
 This is rarely true and reviewers will prove it in seconds.
 Instead say:
 👉 “Limited research has examined…”
 👉 “Findings remain inconsistent regarding…”

2. Don’t confuse a topic with a gap
“Social media and mental health” is a topic.
 
A gap sounds like:
“Few longitudinal studies have examined how social media use predicts anxiety among adolescents in low-income settings.”

3. Don’t copy the ‘future research’ line blindly
Use it as a clue, not as your gap. You still need to justify why that missing piece is important.

4. Don’t make the gap too big
You are not solving the entire field’s problem in one PhD.
A good gap is manageable, specific, and researchable.

5. Don’t write the gap before the review is mature
If you decide your gap too early, you’ll force the literature to fit your idea instead of letting the evidence guide you.

PS: Are you studying a topic… or solving a clearly defined gap?
Share in the comments

REPOST to help others.

PowerPoint feels clumsy and time-consuming

That is where graphical abstracts come in.

A graphical abstract is a single visual snapshot of your study:
the question, the method, and the key takeaway, all at a glance.

Why do Graphical abstracts matter today?
·      Editors use them to quickly grasp relevance
·      Reviewers use them to understand logic and flow
·      Readers use them to decide whether to click, read, or cite
·      Journals use them to increase visibility and sharing

In short, A good graphical abstract earns your paper attention.
Yet this is where many researchers struggle.

Recently, one of my mentees asked me for a practical tool to create a graphical abstract quickly, without design stress.

That’s why, I am sharing a demo showing how I used AnswerThis (YC F25) to break a study into visual components, convert methods and findings into clean diagram.

Step 1: Go to https://lnkd.in/ea5SUYVg and open the Diagrams feature (or select Diagram from the creation options).

Step 2: Select Diagram, then tell the tool what you want to create.
In my case, I wanted a graphical abstract.

Step 3: Paste your study abstract + add a clear instruction
I copied the study’s abstract and used a prompt like this:
“Create a graphical abstract for this study. Use the abstract below to summarise the background, aim, methods, key findings, and conclusion in a clean, journal-style layout. Abstract: [paste abstract]”

Step 4: Run the prompt
Click the red up arrow / Run button to generate the diagram.

Step 5: Review what the tool generates
Within a few minutes, the tool produces a graphical abstract and typically provides:
a short overview/explanation of the visual structure
how it translated the abstract into sections (aim, methods, findings, takeaway)

Step 6: Export the graphic
The output is available as a high-quality PNG. You can:
use it directly, or
use it as a base draft and refine labels/layout in your preferred design tool.

Step 7: Accuracy check (non-negotiable)
Before using it in a submission or posting it online:
verify every label, number, direction of effect, and conclusion
ensure the visual matches the paper (not assumptions or “typical” logic)

The key point here is not the tool itself, but what AI now enables.

AnswerThis (YC F25) can help researchers:
·      translate dense text into visual logic
·      reduce cognitive load during design
·      focus on clarity, not cosmetics
·      save hours without compromising accuracy

Graphical abstracts are no longer nice to have.
 
They are part of modern scholarly communication in many fields.

PS: What tools are you using to create graphical abstracts or research visuals?
Share in the comments

REPOST to help others.

Data speaks in results. You speak in the discussion.

The discussion is where you prove you understand your results.

Here’s a simple way to write a strong discussion section 👇

1. Start with the big answer, not the details
 Open by answering your research question in plain language.
 What did you actually find?
 
2. Explain the “why,” not just the “what”
 Results tell what happened.
 Discussion explains why it matters.
 
Ask yourself:
 “Why did this result turn out this way?”
 “What does this tell us about the problem?”

3. Connect your findings to existing studies
 Don’t list papers.
 Compare ideas.

 Say whether your findings:
 – support previous work
 – challenge it
 – extend it to a new context

4. Be honest about limitations
 Every study has limits.
 
Name them calmly and briefly.
Then explain why your findings are still useful despite those limits.

5. End with implications, not repetition
 So what?
 
What should researchers, practitioners, or policymakers do differently now?
This is where your study earns its value.

Strong discussions don’t restate data.
They interpret, connect, and position it.

PS: Which part of the discussion do you find hardest to write?
Share in the comments.

REPOST to help others.

Follow Dr Priya Singh for more research writing guides 

Founder, Research Made Clear

There was a phase in my own PhD where I didn’t feel “stuck”.

I was busy.

Reading more. Tweaking things. Making notes. Starting and restarting documents.
 
Telling myself, I’m working… just not ready yet.

Weeks passed. Then months.
Nothing was technically wrong.
 
But I had quietly lost the thread.

What I see now, after mentoring so many PhDs is that this happens to almost everyone.

Because a PhD is long enough for you to forget what you’re actually trying to finish.

You stay productive, but directionless.
You mistake movement for progress.

Here’s what actually helps:

✅ Decide what is in scope and what is not. Write it down. Stop renegotiating it daily.
✅ Decide on a weekly finish line (a section, a figure, 1,500 words, not “work on Chapter 3”).
✅ Decide when something is good enough to send, not perfect enough to keep.
✅ Decide that unclear feedback means act and clarify later, not pause indefinitely.

Finish one small thing this week.
Momentum follows decisions.

That’s how PhDs get finished.

PS: What are you finishing next—section, figure, or analysis?
Share in the comments.

REPOST for every PhD who needs to know this,

Follow Dr Priya Singh, Founder Research Made Clear for PhD survival tips.

Ten simple Rules for finishing your PhD

❓Three terms. One chapter. Endless confusion 🤔 

Literature review, theoretical framework, and conceptual framework do not mean the same thing. 

And confusing them can weaken your entire study.

Here is the difference in simple terms:
1️⃣ Literature Review = What we already know
This is where you show the research landscape.

You summarize, compare, and critique previous studies to explain:
✅ What has been studied
✅ What findings exist
✅ Where the gaps are

Example:
If your topic is AI use in medical education, your literature review might discuss:
Studies on AI tools for diagnosis training
Research on student attitudes toward AI
Evidence on learning outcomes

2️⃣ Theoretical Framework = The theory you stand on
This is about existing theory that explains why things happen.

✅ You choose a well-established theory that helps interpret your study.

Example:
If you use Technology Acceptance Model (TAM), you might say:
Students’ perceived usefulness of AI influences their intention to use it
Perceived ease of use affects adoption

Here, the theory provides a lens. You are saying:
 “I am viewing my study through this theory.”

3️⃣ Conceptual Framework = Your study’s blueprint
This is your model, built for your specific research question.
 
It may be inspired by theory, but it is customized.

You show:
✅ Your key variables
✅ How you believe they are related

Example:
You may propose:
AI training → AI confidence → AI usage in clinical learning
And you may add:
Faculty support strengthens this relationship

That diagram? That’s your conceptual framework.

When you separate them clearly:
✅ Your literature review justifies the gap
✅ Your theoretical framework explains the logic
✅ Your conceptual framework shows your research design

PS:  Are you describing what others found… or explaining how your variables connect?

Share in the comment.

REPOST to help others.

A systematic review without a plan is just academic burnout in slow motion.

A PhD student once told me, “I thought a systematic review would take 3 months. It’s been more… and I’m still screening papers.”

That frustration is more common than people admit.

Systematic reviews and meta-analyses look straightforward from the outside. 
Search some databases, read papers, summarize results. 

But in reality, they can quietly drain months (sometimes years), and in my opinion, most delays happen because the process is not structured from the start.

A strong review involves designing the journey, before you begin walking.

Here’s what saves time, stress, and rework:
✅ Be brutally clear about your research question before you search
 A vague question = endless screening and confusion later.
✅ Decide your rules early
Inclusion, exclusion, outcomes, study types. If you define these late, you’ll keep second-guessing every paper.
✅ Work in pairs, not alone
Independent screening and extraction don’t just reduce bias. They reduce mistakes that cost weeks to fix.
✅ Treat data like a database.
If extraction isn’t structured, analysis becomes a nightmare.
✅ Don’t assume you must do a meta-analysis
Sometimes the most honest and useful review is narrative. Forcing numbers where they don’t belong weakens your study.
✅ Document as you go
Trying to rebuild your flow diagram and methods section at the end is one of the biggest time traps in evidence synthesis.

A well-planned systematic review doesn’t just produce stronger evidence.
 It protects your time, your energy, and your sanity.

PS: Did your review take much longer than you expected?
Share in the comments

REPOST to help others.

The Discussion section is where good papers either land… or lose reviewers.

A strong Discussion is not a summary.
It’s your argument.

✅ Start with the answer, not the data
Open by clearly stating what your findings mean, not what you found. The reader already knows the results.

✅ Anchor every claim to evidence
If you say something is “important” or “novel,” explain why and link it back to theory or prior studies.

✅ Compare, don’t compete
Show how your findings align with, extend, or challenge existing work. You’re joining a conversation, not trying to win it.

✅ Explain surprises honestly
 Unexpected results are not weaknesses if you interpret them thoughtfully.

✅ Separate interpretation from implication
First explain what the results mean. Then discuss why they matter for practice, policy, or future research.

✅ End with contribution, not repetition
Your final paragraph should answer one question:
What does the field know now that it didn’t know before this study?

If Results show what happened,
Discussion explains why it matters.
That distinction changes everything.

PS: What would you add?
Share in the comments.

REPOST to help others

How we write a manuscript discussion

Last year, a mentee of mine almost gave up on her PhD dream!!

She had strong grades. 
A meaningful topic. 
Real motivation.

But after multiple university rejections, she was exhausted.

What hurt most wasn’t just the “no.”
It was the time.

Months spent drafting.
Weeks waiting for decisions.
Emotional energy poured into applications that quietly went nowhere.

When we finally sat down to review her research proposal, the issue became clear very quickly:

Her idea made sense in her head.
But on paper, it didn’t yet look like a clear, doable research plan.

And that’s where many applicants struggle.

Universities aren’t only asking,
“Is this topic interesting?”

They’re asking:
Do we clearly understand the problem?
Are the questions focused enough to guide a study?
Is the method realistic for a PhD timeline?
Does this project show a logical path from idea to outcome?

Once we reworked her proposal with those questions in mind, everything changed.

Not her topic.
Not her passion.
 
Just the clarity, structure, and flow of her proposal.
In the next application round, she received interview invitations and an offer.

That experience is exactly why I created this guide on crafting a strong research proposal. 👇 

Because a proposal is a document that helps reviewers quickly see:
✅ what you want to study
✅ why it matters
✅ how you will actually do it
✅ and whether the project is feasible

A good proposal doesn’t try to sound impressive.
It makes the research easy to understand and easy to trust.

If you’re planning to apply for a PhD or research program, don’t wait for another rejection to realise the proposal might be the missing piece.

PS: What part of writing a research proposal feels hardest for you right now?
Share in the comments.

Repost to help others. 

Follow Dr Priya Singh, Founder Research Made Clear for more research tips.

A PhD is rarely finished by inspiration. It’s finished by routine.

Writing a PhD thesis is a process.

After years of supervising and examining theses, here’s the simple truth most students aren’t told:

1. Start with structure before sentences
Do not begin by writing chapters.

Begin by locking:
Your research question
Your chapter map
What each chapter must prove

If you can explain your thesis structure on one page, writing becomes execution, not confusion.

2. Write to answer questions, not to sound academic
Every section exists to answer one question:

Introduction → What problem are you solving and why now?
Literature review → What do we already know, and where is the gap?
Methods → Why was this the right way to study it?
Results → What did you find?
Discussion → What does it mean?


3. Separate “thinking” from “writing”
Most PhDs get stuck because they try to think and write at the same time.

Instead:
Think first (notes, bullet points, rough logic)
Write later (clean sentences, flow, references)
This removes perfectionism from the process.

4. Write small, consistently
You do not need long, perfect writing days.

You need:
30–45 minutes
One clear task
One subsection at a time

Consistency beats intensity every single time.

5. Expect ugly drafts
A strong thesis always starts messy.

Your first draft is not evidence of your intelligence.
It is raw material.

Clarity comes from revision, not from waiting to feel “ready.”

6. Treat feedback as data, not judgment
Supervisors are not reading to admire your work.
They are reading to stress-test it.

When feedback comes:
Categorise it (structure, clarity, theory, method)
Address patterns, not emotions
Revise strategically, not defensively

This is how strong theses are built.

PS: Are you writing regularly, or only when deadlines panic you into it?
Share in the comments.

REPOST to help others

Follow Dr Priya Singh, Founder Research Made Clear for more PhD writing tips.

The Method section is not a formality.
 It is where reviewers decide whether to trust your study.

Here’s how to use it properly when writing 👇

1. Research Design
Don’t just name the design.
 
Briefly explain why this design was the right choice for your research question.

2. Participants / Sample
Be explicit about:
Who was included (and excluded)
How participants were selected
Ethical approval and informed consent
If there are sampling limits or constraints, name them calmly. 

3. Variables / Constructs
Clarity matters more than complexity.

Quantitative: define independent, dependent, and control variables clearly
Qualitative: explain key concepts, themes, or guiding questions

If readers can’t tell what you actually studied, the analysis won’t matter.

4. Data Analysis
Explain how you made sense of the data, step by step.
What analysis approach did you use?
Why was it appropriate?
What software supported the process?

5️⃣ Limitations (Optional, but powerful)
Strong papers don’t hide limitations.
They frame them responsibly.

Briefly note:
Sample constraints
Potential biases
Limits to generalisation

Other details that reviewers quietly appreciate
Software and tools used
Study timeline
Ethics approval reference number

Small details = big trust signals.

📌 Save this infographic for your next paper.

PS: What would you add? 

Follow Dr. Priya Singh for practical research writing guidance.

Most thesis stress comes from unclear expectations‼️ 

One of the fastest ways to feel confident and finish is to understand where you are in the thesis journey and what your stage is actually trying to train you to do.

Here are few things I advice my mentees: 
✅ Ask “What does success look like for this thesis?” In your first supervision meeting, request 2–3 sample theses/dissertations your department considers “good.” 
✅ Treat your thesis like a project, not a mood. Put milestones on a calendar: proposal, ethics, data collection, analysis, chapter drafts, revisions. 
✅ Create a “decision log.” Every time you choose a method, sampling approach, exclusion rule, or framework—write a 2–3 line justification. This saves you during viva/defense and revisions.
✅ Write earlier than you feel ready. Don’t wait for “complete reading.” Start with rough chapter skeletons and expand weekly. Writing is how thinking gets clearer.
✅ Use chapter checklists. Each chapter needs a purpose, a through-line, and a final “so what.” If you can’t summarise a chapter in one sentence, it’s still fuzzy.
✅ Separate reading into two lists: “Must-cite” (core, directly relevant) and “Nice-to-know” (interesting but optional). This prevents literature-review overload.
✅ Build a weekly evidence habit. Every week produce something tangible: a table, a figure, coded notes, an analysis memo, 500–800 words. 
✅ Get alignment on feedback style. Ask your supervisor: “Do you prefer full-chapter drafts or smaller sections?” and “Should I submit polished writing or rough drafts?” 
✅ Stop comparing timelines. Different disciplines and data types move at different speeds. Compare your progress to your plan, not someone else’s chapter count.
✅ Protect your “deep work” slots. Two focused sessions a week (even 60–90 minutes) consistently beats weekend panic-writing.

PS: Where are you right now in your thesis journey and what’s the one thing you wish someone had explained earlier?

Share in the comments

REPOST to help others.

------------------------------------------------------------------------------------------------
I’m Dr Priya Singh, Founder of Research Made Clear

I support researchers with thesis review, academic editing, and proofreading to strengthen clarity, structure, and overall quality before submission—DM me if you’d like support.

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