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

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

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

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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.


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

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

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

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

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

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

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.

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

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

graphical user interface, text, application, chat or text message

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 research hospitality marketing, consumer behavior & AI in services. 

I collaborate with research tools (e.g., SciSpace, Paperpal, AnswerThis AI) to educate researchers on responsible AI use

Academic Editor at Human Behavior and Emerging Technologies

40+ SSCI-indexed publications.
Presentations at AMA

🔗 ORCID: https://orcid.org/0000-0003-1992-6180

--------------------------------------------------------
Community & Mentoring

Founder of TWO NEWSLETTERS:
• Tourism Tech-Buzz (tourism & tech insights)
• The Hybrid Researcher

Reaching 35,000+ subscribers worldwide.
Ranked #124 globally as an academic content creator (Favikon).

Muhammad Shoukat

If I were supervising a new PhD or Master’s student today,
this is the first thing I would teach them:

How to use AI to choose a serious research topic; not a random one.

Not “ask ChatGPT for ideas.”
Not “generate 20 topics and pick one.”

But how to think with AI, not let AI think for you.

Because most students struggle here:
→ too broad
→ too generic
→ already done
→ no real gap
→ no real contribution

So I’d show them this exact framework: R.T.C.R.O.S

And we’d use it like this 👇

Step 1 – Role (R)
I’d tell them:
“Never ask AI as a student. Ask it as an expert.”

Example:
Act as a senior researcher in hospitality and AI…

This changes the quality instantly.

Step 2 – Task (T)
Be precise.
Not: give me topics
But: identify practical problems + theoretical gaps

Specific task = useful output.

Step 3 – Context (C)
This is where most fail.

We’d define:
• target industry
• audience
• region
• constraints
• theory angle

AI needs context to think like a researcher.

Step 4 – Reasoning (R)
I’d tell them to instruct AI how to think:

“Bridge practice and theory.”
“Focus on operational challenges.”
“Highlight unexplored areas.”

This is how you get publishable direction, not noise.

Step 5 – Output (O)
We wouldn’t accept paragraphs.

We’d demand:
• topic
• research questions
• method
• theoretical contribution
• practical relevance

If AI can’t structure it, the idea isn’t ready.

Step 6 – Stopping rule (S)
Boundaries matter.

We’d limit:
→ no vague topics
→ no overdone areas
→ no theory-only fluff

Only novel + feasible + relevant ideas survive.

If you’re a student:
This is how you stop guessing and start positioning.

If you’re a supervisor:
This is how you save months of confusion.

If you’re already in research:
This is how you stop drowning in “ideas” and start building contributions.

AI doesn’t replace your thinking.
It amplifies it; when used correctly.
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Muhammad Haroon Shoukat 

I simplify research—one MUST READ post at a time | 12M+ people reached | Hospitality & Tourism | AI & Service Innovation | Research, Editorial & Reviewing Roles

Most researchers waste hours on manual work because they skip these powerful AI Tools.

Let’s be honest:
Academic writing is not just hard—it’s time-consuming when you don’t use the right tech.

Every week, researchers struggle to:
➟ Draft and polish their manuscripts manually
➟ Do endless citation hunting
➟ Search papers without structure or summaries

⚠️ The result?
Burnout. Delays. Lower-quality-output.

A curated list of the Top 10 AI Tools to supercharge your research workflow 


1. SciSpace
→ Best for: Research proposal outline, literature review, deep review 
🔗 https://www.scispace.com

 2. Scite.ai
→ Best for: Smart citation tracking with context
🔗 https://scite.ai

3. Jenni AI
→ Best for: Real-time writing with citation suggestions
🔗https://jenni.ai

4. Paperpal
→ Best for: Academic writing and grammar 
🔗 https://paperpal.com

5. ResearchRabbit
→ Best for: Visualizing citation networks over time
🔗 https://lnkd.in/djabGuZu

6. Trinka.ai
→ Best for: Technical writing corrections for researchers
🔗 https://www.trinka.ai

7. Aveksana
→ Best for: Finding research gaps, literature review
🔗 https://www.aveksana.com

8. @Elicit
→ Best for: AI-assisted literature review
🔗 https://elicit.org

9. Litmaps
→ Best for: Citation mapping and visual lit review
🔗https://www.litmaps.com

10. AnswerThis (YC F25)
→ Best for: Literature summaries with citations
🔗 https://www.answerthis.ai

🎓 Research smarter. Publish faster.
These tools save hours and elevate your paper quality—without breaking your focus.

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AMR Bridge Reviewer Graduation Certificate 2025

Advanced Content Marketing

KMRP Reviewer Certificate

Coaching New Hires

How to Support Your Employees' Well-Being

The School of Hotel and Tourism Management of The Hong Kong Polytechnic University is driven by the need to serve its industry and academic communities through the advancement of education and dissemination of knowledge. Ranked among top hotel and tourism schools in the world, the SHTM is a symbol of excellence in the field, exemplifying its motto of “Leading Hospitality and Tourism”.

With 90+ academic staff drawing from 21 countries and regions, the School offers programmes at levels ranging from undergraduate degrees to doctoral degrees. Currently a member of the United Nations World Tourism Organisation (UNWTO) Knowledge Network, the School was bestowed the McCool Breakthrough Award in 2012 by the International Council on Hotel, Restaurant, and Institutional Education (I-CHRIE) recognising its breakthrough in the form of its teaching and research hotel – Hotel ICON – the heart of the School’s innovative approach to hospitality and tourism education.

Research Assistant

Academic Editor

Marketing Positions

Marketing Student

I simplify research—one MUST READ post at a time: Hospitality & Tourism | AI & Service Innovation

Are you struggling to write a research paper? 

The MEO’s Fish Bone Model is a visual guide that simplifies the process by breaking down key components:

📢 Want to dive deeper? 


Check out the original paper here: 

https://lnkd.in/dj4F7Xtf

Ever feeling stuck in writing research paper? 🤔

You are not alone. Writing a paper is a marathon. But breaking it down into smaller, digestible steps makes the whole process much more manageable.

That’s why I created this 12-week plan to help you write your research paper effectively.

Here’s how you can break it down, week by week:

Week 1: Clarify the Focus
• Finalize your research question
• Define objectives and contributions
• Check journal scope fit

Week 2: Rapid Literature Scan
• Identify key theories
• Map 15-20 core papers
• Spot patterns and gaps

Week 3: Deep Literature Review
• Read critically
• Compare and contrast studies
• Build argument flow

Week 4: Design the Method
• Select methodology
• Define sample and tools
• Justify design choices

Week 5: Data Collection
• Collect data
• Maintain accuracy
• Document process

Week 6: Data Cleaning & Prep
• Screen data
• Handle missing values
• Prepare for analysis

Week 7: Data Analysis
• Run analysis
• Generate tables/figures
• Identify key findings

Week 8: Write Results
• Report findings clearly
• No interpretation yet
• Visual first, text second

Week 9: Write Discussion
• Interpret results
• Link to literature
• Explain implications

Week 10: Write Intro & Conclusion
• Finalize problem framing
• Highlight contribution
• Emphasize value

Week 11: Review & Refine
• Improve clarity and flow
• Tighten arguments
• Check coherence

Week 12: Journal Ready
• Format to guidelines
• Check references
• Final proof and submit

This approach turns the daunting task of writing into manageable steps. You’ll feel much more in control.

Want to make your research writing process smoother? Follow this plan, stay consistent, and finish strong!

👇 Download the full guide and let’s get started 👇

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Writing a research proposal can feel overwhelming, 
especially when you're not sure where to start. 

I have seen many researchers struggle with crafting the perfect proposal, unsure of what goes where.

So, to make things easier for you, here’s a simple research proposal template that breaks everything down step-by-step. 

This template is designed to ensure you cover all the key elements while keeping things concise and clear.

Here’s how you can structure your proposal:

1. Introduction
• Introduce your research topic
• Explain the main idea of your study in 1–2 short paragraphs
• State the purpose or aim of your research

2. Background of the Study
• Provide basic information about your topic
• Explain what’s already known and what has led to this research problem

3. Research Problem
• Clearly explain the problem your research will focus on
• Describe why this problem exists and why it needs to be studied
• Provide facts or examples to support the problem

4. Research Questions
• Write 3–6 clear questions your research will answer

5. Research Objectives
• State the goals of your study
• Each objective should start with “To...” (e.g., To examine... To analyze...)

6. Literature Review
• Review and discuss important studies related to your topic
• Identify gaps in existing research

7. Theoretical Framework
• Mention the theory or model you will use in your research
• Explain why you chose this theory
• Describe how it helps you understand your research topic

8. Significance of the Study
• Explain why your research is important
• Mention how it helps students, researchers, policymakers, or industry

9. Justification of the Study
• Explain why this research should be conducted
• State why the topic is worth studying

10. Research Methodology
• Explain how you will conduct the research (What will you study? How will you collect data? Why this method?)

11. Ethical Considerations
• Explain how you will follow research ethics (privacy, consent, confidentiality)

12. Limitations of the Study
• Mention possible limitations and how you plan to reduce them

13. References
• List all sources you used in the appropriate referencing style (APA, Harvard, etc.)

Use this as your go-to guide when structuring your research proposal, and you’ll be on your way to creating a compelling, clear document.

🔗 Save and share with fellow researchers!

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Harvard University is offering FREE Courses

Early in my research journey, 
I wished I had world-class resources without worrying about cost.

Now it’s possible.
Harvard has opened several free courses to boost your research + data skills 

👇
📌 Top 8 Picks
 1️⃣ Python for Research
 2️⃣ Statistics and R
 3️⃣ Remote Work Revolution
 4️⃣ Programming with Scratch
 5️⃣ Causal Diagrams
 6️⃣ Data Science: Capstone
 7️⃣ Machine Learning
 8️⃣ Probability

👉 Access all links in comments

My tip: Start with Statistics and R — it’s the bridge to almost every kind of research.

💡 Question for you:
 Which course will you start first — Python, R, or Machine Learning?
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I still remember my first research paper draft.

15 pages.
No structure.
No flow.
Just ideas… everywhere.

I thought writing was the problem.

It wasn’t.

Structure was.

Once I understood *how a paper is supposed to be built*, everything changed.
Writing became easier.
Revisions became clearer.
And feedback finally made sense.

That’s why I created this visual:
How to Structure a Research Paper (Step by Step)

Not theory.
Not jargon.
Just the real skeleton of a solid paper:

1. Abstract – your movie trailer
2. Introduction – the problem + the gap
3. Literature Integration – connect, don’t dump
4. Methods– earn trust through clarity
5. Results – tell the data’s story
6. Discussion – translate numbers into meaning
7. Conclusion – why this work matters

If your paper feels “heavy” to write…
It’s usually a structure problem, not a skill problem.

Get the structure right.
The writing follows.

♻️ Find this useful?
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PhD Students – Do you know what's allowed and what's not in academic publishing?

Last week, one of our students asked me a key question:

"Can I use AI tools like ChatGPT to help write my paper?"

I could tell they were worried about the ethics of using AI in academic writing. So, I decided to break it down for them.

Here’s what you can and can’t use when it comes to AI tools in academic publishing:

✅ Allowed:

Quillbot
• Paraphrasing tool
• Helps avoid plagiarism

DeepL
• Accurate translations
• Better than Google Translate

Grammarly
• Corrects grammar and typos
• Must disclose use

Notion AI
• Organizes ideas
• Great for planning, not writing

❌ Not Allowed:

Perplexity
• No reliable citations
• Not suitable for papers

ChatGPT
• Generates full text
• Easily flagged by editors

Claude
• Polished output
• Still AI-detectable

Any Undisclosed AI Tool
• Ethics violation
• Risk of rejection or retraction

The key takeaway?

AI is an incredible tool, but it’s essential to use it responsibly. When writing academic papers, ensure that you’re disclosing your use of AI tools and not relying on them for the final output.

By using AI tools like Quillbot and Notion AI, you can enhance your writing process without compromising academic integrity.

Stay ethical. Stay smart. Keep your research original. ✅
====================================
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How I Identify research paper type

I agree; identifying the correct research paper type can save you hours of frustration and improve your publication success

Let’s break it down into different types 

1️⃣ Empirical Papers 

➡️ Purpose: 
Original data 

Structure: Intro, Lit Review, Method, Results Discussion 

--------------------------------------------
2️⃣ Review Papers

➡️ Purpose: 
Summarize, Analyze, Identify Gaps 
Structure: Background, Gaps, Conclusion 

--------------------------------------------
3️⃣ Theoretical papers 

➡️ Purpose: 
New Theories 

Structure: Intro, Theory Development, Discussion 
--------------------------------------------
4️⃣ Methodological papers

➡️ Purpose: 
New Methods, 
Validation 
Structure: Intro, 
Development, Case Study 
--------------------------------------------
5️⃣ Case Study paper 

➡️ Purpose: 
Specific Case, 

Broad Conclusions 
Structure: Context, 
Analysis, Conclusion 

-------------------------------------------
6️⃣ Conceptual paper 

➡️ Purpose: Deep Ideas, 

Practical Insights 
Structure: Concept, 
Discussion, Summary 
---

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PhD Students – Are you clear on the difference between a research gap and a problem statement?

I was speaking to one of the team members recently who was struggling to define the core issue of her research. 

She asked, “How do I make sure my research question is clear and focused?”

Here’s the key: you must understand the difference between a research gap and a problem statement.

Let me break it down:

🔵 Research Gap:

Meaning: Refers to areas that haven’t been sufficiently studied or discussed in existing research.

Role: Highlights what’s missing, limited, or overlooked in current knowledge.

Main Focus: Concentrates on weak areas in previous studies.

How It’s Presented: Typically identified through a review of literature. For example:
“There is limited research available on…”

🟠 Problem Statement:

Meaning: Clearly defines the exact issue or challenge that the research aims to solve.

Role: Explains why the identified gap is important and turns it into a meaningful research problem.

Main Focus: Targets the specific issue the study will examine to address the gap.

How It’s Presented: Written as a concise statement. For example:
“This research aims to investigate the lack of…”

Both are essential, but they serve different purposes in your research journey.

🔗 Stay focused on identifying your gap, and then turn it into a clear, actionable problem statement to guide your study in the right direction.
==================================
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How to Write Paper Contributions

Stuck on how to show your research contribution?
Here’s a quick guide to help you out!
---
1. Theoretical Contribution

-Expanding or refining theory.
- Identify gaps in existing theories.
- Build or challenge frameworks.

---

2. Methodological Contribution

-Improving data collection, analysis, or design.
- Innovate methods.
- Apply methods in other fields.

---

3. Contextual Contribution

- Extend theories to new settings.
- Address research gaps.
- Provide practical insights.

---

4. Practical Contribution

- Real-world applications of research.
- Offer actionable solutions.
- Improve practices in industries.

---

5. Policy Contribution

- Influence policy development.
- Suggest improvements.
- Provide evidence for new policies.

---

6. Conceptual Contribution

- Introduce new concepts or frameworks.
- Clarify ambiguous ideas.
- Integrate existing concepts cohesively.

---

7. Technological Contribution

- Develop new tools or technologies.
- Improve existing systems.
- Apply innovations to solve problems.

---

8. Interdisciplinary Contribution

- Combine insights from different fields.
- Apply cross-field methodologies.
- Foster collaboration for innovative solutions.

---

9. Data Contribution

- Provide new datasets.
- Develop new data collection methods.
- Apply innovative techniques for analysis.

Save this guide for future reference!
-----
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A few months ago, a friend messaged me at 1:12 AM.

“Can you just check my literature review? Something feels off.”

It wasn’t weak.
It wasn’t poorly written.
It wasn’t even unstructured.

It was… flat.

No voice.
No argument.
No flow.
Just summaries stacked on summaries.

And that’s when it hit me (again):

Most people don’t struggle with reading literature.
They struggle with mastering it.

That’s exactly why I created this visual:
Mastering the Literature Review

Because a strong LR is not:
• a Wikipedia page
• a dumping ground of citations
• a parade of authors

It is:
• positioning
• interpretation
• connection
• judgement

It’s where you prove you can think, not just read.

If your LR feels heavy to write…
It’s usually because you’re reporting, not reasoning.

And once you shift from “what they said” to “what this means”,
Everything changes.
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When designing your study, 
the right choice of methodology can make or break your research outcomes.

But how do you know which design to choose? 🤔

Here’s how to decide, step by step:

I often get asked about the best study design, especially by new researchers. Here’s the simplified version I use to help them pick:

1. Case Reports & Case Series
➡️ Detailed presentation of one or more patients.
➡️ Ideal for new diseases or rare conditions.

2. Cross-Sectional Studies
➡️ Observes a population at a single point in time.
➡️ Useful for estimating disease prevalence.

3. Case-Control Studies
➡️ Compares individuals with and without a specific condition.
➡️ Ideal for rare diseases or long latency periods.

4. Prospective Cohort Studies
➡️ Follows exposed and unexposed groups over time.
➡️ Best for studying incidence and causal relationships.

5. Retrospective Cohort Studies
➡️ Uses existing data to observe exposure outcomes.
➡️ Ideal when exposure data is already available.

6. Ecological Studies
➡️ Analyzes data at the group or population level.
➡️ Suitable for generating hypotheses on population-level effects.

7. Randomized Controlled Trials
➡️ Randomly assigns treatment or control groups.
➡️ The gold standard for testing interventions.

8. Field Trials
➡️ Conducted in real-world settings.
➡️ Suitable for evaluating preventive interventions.

9. Community Trials
➡️ Focuses on interventions at the community level.
➡️ Used for assessing public health interventions.

10. Systematic Reviews & Meta-Analyses
➡️ Synthesis of multiple studies.
➡️ Ideal for summarizing evidence on a specific topic.

Choosing the right design isn't just about collecting data; it's about answering your research question effectively. 💡

======================================
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The other day, I saw a friend juggling 5 tabs:
• Word
• Google Docs
• Citation manager
• Grammarly
• Plagiarism checker

His comment:
“I am spending more time copy-pasting than writing.”

That’s when I tested notebook-style AI tools like AnswerThis (YC F25) that keep everything in one place.

Login here : https://lnkd.in/eeWEpz3b

Here's my new, streamlined workflow:

1. Write inside the notebook
- Draft sections directly in the notebook, like Google Docs but with research features built in. 
- No more tool-hopping.

2. Use AI to refine, not replace
Ask it to:
– Clarify a paragraph
– Adjust tone to academic
– Shorten sentences
Keep the original nearby and double-check meaning.

3. Auto-manage citations
Select your style (APA, MLA, etc.), insert citations, and let the tool format them as you write.

4. Run plagiarism + AI detection
- Highlight text, run checks, and fix overlaps or add missing citations.

5. Collaborate in real-time
- Share the notebook link with collaborators and supervisors. 
- They can comment, suggest edits, and add references.

6. Export clean versions
- Once ready, export to PDF, DOCX, Markdown, or LaTeX for submission. 
- Keep the notebook as your living draft.

Important Reminder:
AI speeds up drafting, but you’re still responsible for:
• Factual accuracy
• Correct citations
• Final wording

Sign up here: : https://lnkd.in/eeWEpz3b
=========================================
Save this post if you want to optimize your writing workflow this semester.

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I still remember my methodology class.

I thought research was research.
Data is data.
Methods are just tools… right?

Wrong.

I mixed up quantitative and qualitative in my proposal.
My supervisor circled half the page in red and wrote:

“Decide what you want to understand before deciding how to measure.”

That one line changed how I design every study.

That’s why I created this visual:
Key Differences: Quantitative vs. Qualitative Research

Because many researchers don’t struggle with data.
They struggle with choosing the right lens.

Quantitative is for:
• measuring
• testing
• validating
• finding patterns at scale

Qualitative is for:
• exploring
• understanding
• interpreting
• capturing human experience

Neither is better.
They answer different questions.

And once you stop forcing one to do the job of the other,
your research becomes clearer, stronger, and easier to defend.

If your methodology section feels messy…
It’s usually a design problem, not an intelligence problem.

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A reviewer once wrote on a student’s proposal:

“Interesting topic. But… where is the gap?”

That single line crushed her.

She had read 80+ papers.
She had notes everywhere.
She had highlighted half of Google Scholar.

But she still couldn’t see what was missing.

That’s when I realized:

Most researchers don’t struggle with finding literature.
They struggle with seeing absence.

And that’s the real skill.

Because a research gap is not:
• “No one has studied this”
• “There is limited research”
• “Few studies exist”

A real gap is specific, defensible, and strategic.

That’s why I created this visual:
Identifying the Types of Research Gaps

So you can stop guessing
and start naming what’s actually missing:

• Methodological gap
• Theoretical gap
• Knowledge gap
• Empirical gap
• Evidence gap

Once you can label the gap,
you can design the study.

And once you can defend the gap,
reviewers stop questioning your relevance.

If your proposal keeps coming back with
“unclear contribution” or “weak justification”
this is usually why.

♻️ Find this useful?

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Stuck generating your research hypothesis?
Formulating a strong hypothesis can take months of prep.

But what if you could speed this up?

Introducing the Hypothesis Generator Agent by Liner

I tried it with my question:
“What is the impact of guest delight on guest revisit intention?”

Here’s how it works:
1️⃣ Enter your research question.
2️⃣ The tool scans academic papers.
3️⃣ It generates 3 hypotheses with evaluations on novelty, feasibility, and clarity.
4️⃣ You can explore the hypotheses further.

In minutes, I had 3 solid hypotheses to work with.

Why it’s a game-changer:
● Saves months of prep.
● Boosts confidence in hypothesis quality.
● No more endless paper hunting.

What users are saying:
● “Hypothesis aligns with my team’s work.”
● “The literature review is super helpful.”
● “Mindmap UI helps me expand ideas.”

👉 SignUp here: https://lnkd.in/dCXWpErY
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Yesterday, I was reading a paper that made me rethink how I use the VIF score.

A researcher asked: “How reliable is VIF? It says my model is fine, but something feels off.”

I decided to dig into Kalnins and Hill’s (2023) study, “The VIF Score: What is it Good For? Absolutely Nothing”.

Here’s what I learned, step by step:

Step 1: The common misconception
Most researchers use VIF to check multicollinearity, assuming low VIF means no issue.
But Kalnins and Hill argue that VIF alone doesn’t account for omitted variables.

Step 2: The literature gap
The real problem? VIF misses bias from endogeneity. Low VIF doesn't guarantee a reliable model.

Step 3: New approach
Instead of relying on VIF, use bivariate correlations and check coefficient magnitudes.
This gives a better view of model accuracy.

Step 4: Key takeaway
VIF isn't enough. Always cross-check it with other methods.

Want to dive deeper? Read the paper here:
DOI: 10.1177/10944281231216381

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Kalnins and Hill (2023)

“Research Onion”?
When I first read Saunders’ Research Onion, I thought:

 👉 “This looks abstract, layered, and… honestly overwhelming.”

Many of my students face the same challenge:

 ❌ Which philosophy do I pick?
 ❌ Inductive or deductive?
 ❌ Cross-sectional or longitudinal?

To solve this, I created a visual cheat sheet 🧅:
 “Peeling the Research Onion: A Student’s Guide.”

It breaks each layer into:

 ✅ The challenge students face
 ✅ A quick cheat line / hack
 ✅ Practical examples for clarity

Here’s the key takeaway 👇

 🔑 Don’t get lost in jargon. Peel one layer at a time until you reach the core: techniques & procedures that drive your actual research.

💡 Save this post — next time you’re stuck with your onion, peel smarter, not harder.

📌 Your Turn:

 Which layer of the research onion do you find most confusing? (Philosophy, Approach, Methods, or Strategies?)
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Last week, a researcher asked me a very honest question:

“Is it even possible to write an academic report with AI… without faking references or losing credibility?”

So instead of answering, I showed them.

I opened SciSpace.

Not ChatGPT.
Not copy-paste prompts.
A proper AI agent built for research tasks.

Here’s exactly what I did — step by step:

Step 1: Log in to https://lnkd.in/e-Xbm4K4
After logging in, you’ll see multiple AI agents designed for different academic tasks.

Step 2: Select the “Write Report” AI agent
This agent is specifically built for structured academic reports.

Step 3: Enter the report title
I typed:
“A Report on A Comprehensive Review of Recent Advances in Service Robots in Hospitality”
Then pressed enter.

Step 4: Let the AI agent work
The agent immediately started:
• searching reputable academic databases
• identifying relevant studies
• structuring sections logically
• inserting real academic references

No hallucinated journals.
No fake authors.

Step 5: Review the output
In under a minute, I had:
• a ~2000-word structured report
• proper academic tone
• real, traceable references

I cross-checked one reference manually on Google Scholar.
It was real.

This is the key point most people miss:

AI didn’t think for me.
It handled retrieval, structure, and drafting.

Judgment, refinement, and responsibility still stayed with me.

Used this way, AI is not cheating.
It’s research acceleration.

If you’re writing:
• reports
• background chapters
• consultancy or policy documents

This workflow can save days, not minutes.

You can sign up here:
👉 https://lnkd.in/e-Xbm4K4

Use AI as an assistant.
Stay the author.

That’s the line that matters.
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The Battle

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Emmanuel Tsekleves's LinkedIn Performance

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