University of Waterloo

Waterloo recognizes exceptional achievement and pre-eminence in a particular field of knowledge through the designation 'University Research Chair'.

University Research Chair

My work is in UX and Games.

I solve problems within 

- communication technologies
- design
- psychology
- human-computer interaction
- human health
- wellbeing

I have a background working with biotechnology (namely, physiological sensors), where I evaluated physiological signals elicited by humans when playing games.

I train graduate students, mentor UX researchers, and create innovative technologies with startup companies and universities.

Professor

HCI Games Group

The HCI Games Group is a research group located within the Games Institute and the Stratford School of Interaction Design and Business.

Our graduate students conduct research in:

- gamification
- games user research
- biofeedback and phsyiological game evaluation
- exergames
- storytelling in games
- virtual reality
- realism
- audio in games

We are not open for unsolicited applications. Please reach out to me directly before applying to work with us.

Director

MotiviUX

At MotiviUX, we use gamification to create enjoyable personalized user experiences.

We consult with you to help you create positive behavioural changes with your technology and build habit-forming, self-improvement products, systems, or services.

We help you create engaging customer and employee relationships, or meaningful learning.

Gamification and User Experience Strategist, CEO

Ontario Tech University

My research here combined the areas of entertainment computing, interaction design, affective gaming, and user experience evaluation.

Assistant Professor

University of Saskatchewan

The Human-Computer Interaction Lab is a research facility in the Department of Computer Science at the University of Saskatchewan, where I did research on affective computing, evaluation of virtual worlds, games for fitness, emotions, and user experience in games.

Postdoctoral Research Associate in Affective Computing

Blekinge Institute of Technology

Experimental research into forms of future entertainment, involving new interaction technologies and biofeedback mechanisms. Statistical analysis of psychophysiological player data.

PhD Candidate

FUGA - The fun of gaming: Measuring the human experience of media enjoyment

My main responsibilities in FUGA included game stimulus development and conducting empirical studies of game experience using psychophysiological equipment.

Researcher

Otto-von-Guericke University Magdeburg

Research into new forms of software engineering, project management, serious games and innovative game design.

Doctoral Student

Self-employed

Designed, implemented and administered several database-driven websites, wikis and linux servers.

Freelance Programmer

impara GmbH

Freelance Game Designer

I share effective strategies for better research + writing (often with AI). 34k+ citations of my research. University Research Chair & Tenured Full Professor.

I am a tenured University Research Chair and Full Professor with 20+ years of research and teaching experience in user experience (UX) design and research focused on gamification, human-computer interaction, and generative artificial intelligence (AI). My Ph.D. research was on the scientific measurement of UX in interactive entertainment. I direct a research group and a graduate degree program at the University of Waterloo, Stratford School of Interaction Design and Business. I'm also a solopreneur and online creator, helping early-career researchers to unlock their full potential.

I have written 300+ research papers (and counting), edited the major games user research book and several high-impact conference proceedings, started a top-tier academic conference series that is now a journal (CHI PLAY), and started companies for UX research and online training. My research programmes have been funded with millions of dollars over the past decade through joint funding from major companies and the Canadian government.

Many of my papers have won best paper awards and have hundreds of citations. In addition, I run  newsletters, a podcast, write daily on social media, and am fascinated by scientific copywriting.

Besides my academic work, my mission is to make the world a better place by creating more enjoyable experiences with technology and translating academic insights into actionable strategies for everyone.

If you want to know more about my academic writing strategy and how I created this research output over my career, you can take my self-guided online courses at:

→ https://go.lennartnacke.com/chicourse

Email me at linkedin@lennartnacke.com or find me on the following social networks:

• YouTube: @lennartnacke
• BlueSky: @lennartnacke.com
• X: @acagamic
• Instagram: acagamic
• TikTok: @acagamic
• Twitch.tv: acagamic

I also have a newsletter that helps you write better academic papers: https://go.lennartnacke.com/newsletter

Most researchers waste months on a systematic review

(when a rapid review would have been good enough.)

Two review types. Same question.
Completely different amount of work.

According to this paper, 14 literature review types exist.

If you get started, focus on 2 main types:

Run a systematic review when you’re shaping guidelines.
Use a rapid review when leadership wants an answer this quarter.

Systematic reviews:

• Multi-database + grey literature search, no date limits
• Typically used for guidelines or high-stakes decisions
• Dual screening + full critical appraisal, validated tools
• In-depth narrative synthesis to explain heterogeneity
• Detailed evidence tables, if possible, meta-analysis
• Formal, pre-registered protocol (e.g. PROSPERO)

Rapid reviews:

• Typically used for time-sensitive service (1–6 months)
• Output a short decision brief, slide deck, or summary
• High-level narrative summary with minimal detail
• Focused search (fewer databases, tighter limits)
• Single-reviewer screening with spot checks
• Streamlined or internal-only protocol

The distinction?
Speed vs. thoroughness.

Pick systematic when:

→ You need thorough certainty
→ Time isn't the main factor
→ Quality is everything

Pick rapid when:

→ You need quick insights
→ Time drives decisions
→ Good enough works

Don't overcomplicate this.
Your timeline decides.

Save this for when you’re writing your next review.
Know someone stuck choosing a review type?
Send this to them.

Source: Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91–108. DOI: 10.1111/j.1471-1842.2009.00848.x

Which review fits your current project: systematic or rapid?
Don’t overcomplicate this. Your timeline decides.

Join 12,816+ researchers getting snack-sized methods tips in their inbox.

#phd #litreview #research

Lennart Nacke

games-user-research-book

hcigames

chiplay

motiviux

Talks about #ux, #writing, #research, #gamedesign, and #psychology

Making AI + UX research fun and accessible | 12,696+ researchers learning to 10x productivity | Research Chair & HCI Prof @ UWaterloo with 250+ published studies

I review dozens of papers every year. Painful, yes.

But I see the same 5 mistakes destroy solid research.
 
The science is good. The writing is bad.

Specifically, the Discussion section.
The part where you explain what it all means.
 
The 5-step protocol I use to turn Rejects into Revisions:
 
1. Stop summarizing your results
 
Reviewers already read them.
They don't need a recap.
 
Your first paragraph should answer your research question in 1-3 declarative sentences.
 
"The intervention significantly reduced anxiety by 60% at 12-week follow-up."
 
Not: "We conducted repeated-measures ANOVA and found significant differences."
 
Give them the answer. Then tell them what it means.
 
2. Position yourself in the literature debate
 
Your work doesn't exist in isolation.
 
→ Which established studies do your results support?
Say it explicitly.

→ Where do your findings diverge or contradict?
Explain why.
 
An unexpected null finding isn't a failure.
It's a caveat the original theory missed.
 
That's your novelty. Frame it. Name it. Claim it.
 
3. State the contribution out loud
 
This is where most Discussions die silently.
 
Researchers describe findings, compare to literature.
Then they stop.

As if significance were self-evident.
 
It's not.
 
You have to say: "These results suggest Theory X's boundary conditions extend beyond the populations originally tested."
 
Not: "Our results are interesting and warrant further investigation."
 
One is a contribution. The other is a meaningless shrug.
 
4. Attack yourself before reviewers do
 
Dedicate a section to limitations.
But frame them as constraints.
They're not your confessions.
 
Every study has weaknesses.
The question is whether you understand yours.
 
Pro tip: Use AI to pressure-test your methodology.
Prompt it to act as a hostile reviewer and find 3 flaws.
Address them in your draft before submission.
 
If you find the problems first, you control the narrative.
 
5. Never end on limitations
 
That's ending on apology nobody wanted from you.
 
Your final paragraph should reassert main significance.
Link back to the big problem you opened with
(in your Introduction).
 
End with one sentence that looks forward.
Stake that claim.

Position yourself as someone driving the field.
 
The twist in our tale is that
most rejections aren't caused by bad data.

They're caused by not explaining why our data matters.
 
Your Discussion makes that case impossible to ignore.
 
This week:
→ Pull up your last rejected manuscript
→ Check if Discussion intro answers research question
→ If it doesn't, rewrite it in three sentences
 
If this saves one paper from desk rejection
→ Send it to a colleague who's stuck in revision hell.

Tired of desk rejects?
I send one lab publishing system every week to PIs who want to bulletproof their papers. 12,879 researchers. One email. No filler. Get it here: https://lnkd.in/eMt5PNJ7

Your-Discussion-is-getting-you-REJECTED

Google just killed keyword search

But most researchers haven’t noticed yet.

That's a mistake.
The era of guessing keywords is over.

Google released Gemini 3 yesterday and it's amazing.

But Scholar Labs changes how gaps are discovered.
Conceptual search is taking its place.

Here are 5 ways Scholar Labs beats traditional literature searches:

1. You search concepts, not keywords

Traditional search = “caffeine and memory.”
Conceptual search =
 • caffeine consumption
 • short-term memory mechanisms
 • age variations

One search. Three dimensions. Better results.

2. Context comes built-in

Every paper comes with an explanation of how it answers your question.

No more reading 30 abstracts to find 3 relevant papers.
Quick summaries get you sorted.

3. Interactive outlines accelerate reading

Open any PDF → automatic outline.
Each section summarized.

Jump straight to methods, results, or discussion.
Just a quicker way to skim papers.

4. Citations turn into navigation

Click any reference → open the paper instantly.

The PDF reader converts citations to links.
You follow research trails without switching windows.

This matters when you're tracking how ideas evolved across papers.

5. Gaps become visible

Compare your manual search vs the dimensional search.

The AI might find connections you missed.
Or you might find niche terms the AI overlooked.

The difference between those two lists?
That's where research gaps hide.

The catch:
Scholar Labs is experimental.
Limited access. Might change soon.

But the approach matters more than the tool.
Conceptual search find more than keyword search.

Other tools have already adopted this method.
Learn how to use dimensional thinking now.

Caveat: Replicability.
How do we systematically reproduce search results now?

Gemini 3 scores 91.9% on GPQA Diamond academic questions.

Google Scholar's 20-year database backs every result.
That reliability matters.

The power of conceptual search
cannot be underestimated.

It will change how we approach research questions.

If you haven’t started testing conceptual search,
you're already behind. Sorry.

#ai #research

Most research proposals are dead on arrival.

I’ve reviewed dozens of them.
(And I’ve secured over $2M in funding.)

I can tell you exactly why reviewers toss yours in the reject pile.

It’s usually not the science. It’s the storytelling.

Here is the brutal truth about winning grants.

1. You are burying the lead

Reviewers are tired.
They are reading your proposal at 11 PM on a Sunday.

If they've to hunt for the problem you solve, you lose.

State one clear issue.
Frame it immediately.

Show the impact before you get into the weeds.

2. Your literature review is a laundry list

Most PhDs treat this section like a book report.
"Smith said this, Jones said that."
Boring.

Command the literature.
Don't just summarize.

Challenge old assumptions.

Map the specific gap that only 𝘺𝘰𝘶𝘳 work can fill.

3. Your methods are vague

"We will analyze the data" is a lazy sentence.
It screams incompetence.

Match specific tools to specific questions.
Detail your data collection.

Show me you have a plan for when things go wrong.

4. You are hallucinating the timeline

Research never goes according to plan.

If your timeline looks perfect, I know you’re lying.

Break it into phases. Set clear milestones.
Add buffer weeks for the disasters that will happen.

5. You write to sound smart

Cut the academic jargon.
It doesn't make you look smart.
It makes you look insecure.

Use simple language.
Make it scannable.

Reviewers shouldn't need a dictionary to understand your hypothesis.

6. You ignore the boring rules

I have seen great people lose funding
because they messed up the margins.

Follow the guidelines exactly.
Check every citation. Submit early.

Don't let a formatting error kill a year of work.

Good proposals tell stories.
Great proposals solve problems.

Your job isn't just to do science.
It's to sell a future where a specific problem is solved.

I turned my specific framework into a free guide.

It breaks down the 7 moves to make your proposal stand out.

Grab it in the comments.

And if this gave you a reality check,
repost it ♻️ to save a colleague from a rejection letter.

#phd #research #proposal

Reviewers don't reject boring data.
 
They reject papers that feel like work to read (especially at 1 AM with three other manuscripts in the pile.)
 
After 300+ manuscripts over two decades, here’s the pattern:
• The papers that get cited tell a story.
• The ones that get skimmed read like a data dump.
 
Your research is a journey.
Beginning, middle, end.

Most researchers forget this.
Then wonder why their work sits at 3 citations after 5 years.
 
The narrative arc that pulls reviewers through:
 
1. Motivation
 
Why should a tired reviewer care about this problem?
Answer in your first paragraph or lose them.

Open with who gets hurt if this problem stays unsolved.
 
2. Tension
 
What gap in the literature creates the question?
Name the gap that only your study can realistically answer.

Write the tension paragraph 𝘣𝘦𝘧𝘰𝘳𝘦 you outline any methods.
 
3. Discovery
 
Your results should land like a payoff, not a data table.
Frame findings as answers to the tension you built.

Introduce each result as an answer to a question you raised earlier...
Not as ‘Table 2 shows…’.
 
4. Meaning
 
Discussion is where you shape what this means for the field.
Connect back to motivation.
Close the loop.

Spell out what changes for the field if your results hold up.
 
5. Direction
 
End with where this leads.
Give readers a reason to cite you.
Be the foundation for what comes next.

Offer 2-3 specific follow-up questions your paper unlocks.
 
This structure does something critical:
It makes reviewers want to finish reading.
 
And reviewers who finish reading are reviewers who recommend acceptance.
 
Your job is not to report findings.
Your job is to move readers from 'so what' to 'this matters.'
 
Do this test: Read your intro and then your discussion
If they don't feel like the same conversation, your paper is weak at the core.
 
Before you submit, run this check: does your Discussion explicitly answer the problem you opened with in the first paragraph?

If not, your narrative arc is broken.
Use this 5-step arc as your final checklist before you hit submit.

I send one framework like this every week. No fluff. No theory. Just systems that get reviewers to actually finish reading your paper.
 
12,879 researchers are already in: https://lnkd.in/e4HfhmrH

If this could save someone from another revise and resubmit,
send it to them.

Complex ideas don't need complex words.

After reviewing hundreds of papers across two decades, I can tell you the biggest lie in academia:

It has to be complex because the ideas are complex.

No.

Your writing is failing because you believe simplicity signals intellectual weakness.

It doesn't.
It signals mastery.

Here's what academic writing actually requires:

1. Write clearly

Make complex ideas accessible to your field.

Bad: Within the burgeoning interdisciplinary terrain of contemporary epistemic inquiry, it is incumbent upon scholars to rigorously interrogate and problematize taken-for-granted ontological presuppositions.
 
Better: As researchers, our job is to question the assumptions everyone else takes for granted.

If a reviewer can't follow your argument:
• They won't fund it. 
• They won't cite it.
• They'll reject it.

2. Reference extensively

Stand on the shoulders of giants. 
Show your work belongs in the conversation.

3. Argue strongly

Lead with evidence.
Add a little rhetoric.

Make it impossible to ignore your contribution.

4. Question endlessly

Intellectual humility wins long-term.
The best researchers poke holes in their own arguments before reviewers do.

Obscurity isn’t the flex you think it is. Clarity is.

The counter-intuitive part?

The most cited professors write books.
Books require clarity.

Jargon-heavy papers get read by 12 people.
Cited by three.

Your ideas are too important to bury under language that few will read and fewer will understand.

This week, try one thing:

1. Take your latest abstract.
2. Read it aloud.
3. Every stumble = a sentence to simplify.

If a colleague outside your sub-field can't summarize it back to you, rewrite it.

Clarity is not weakness.

Clarity is citations.
Clarity is grants.
Clarity is reach.

If someone praises a paper for being sophisticated when it's actually incomprehensible, send them this post and walk away with a good giggle.

If you’re tired of reviewers confusing obscure with rigorous, that’s exactly what I help fix every week. I send tactical writing and publishing systems to 12,879 research leaders every week: https://lnkd.in/e4HfhmrH

Interviews are won in the parking lot after you're done.

Most postdocs miss this brutal truth:

(If you’re on the market this year, this is where most offers quietly die.)
 
You nailed the job talk.
Your research plan was tight.
Your funding pitch was solid.
 
Then you get in the car with a junior faculty member for the ride to dinner, drop your guard, and complain about your current advisor.
 
You just lost the job.
 
After sitting on 15+ search committees, I've seen dozens of qualified candidates destroy their chances in moments they didn't think counted.
 
The dinner conversation.
The hallway small talk.

The casual breakfast before your flight.
 
Every single interaction is the interview.
 
Search committees don't reject you because your h-index is too low. They reject you because they can't imagine having coffee with you for the next 20 years.
 
Here's what separates candidates who get offers from those who get rejection emails:
 
1. You answer the fear, not the question.
 
When they ask "How will you handle the teaching load?" they're really asking "Are you going to dump your service work on us?"
 
Address that fear directly.
Don't recite your teaching philosophy.
 
2. You read the room in the first 90 seconds.
 
Identify:
• The Alpha (who's leading)
• The Skeptic (folded arms)
• The Nodders (your allies)

Direct 70% of your eye contact to the Nodders.
They give you energy back.
 
Ignore the person scrolling their phone. You can't win everyone.
 
3. You prove collaboration fit with specifics.
 
Saying "I'd love to collaborate" means nothing. Saying "I read your paper on X and see a clear path to combining it with my work on Y" proves you did your homework and can think strategically.
 
4. You stop talking.
 
The worst candidates don't know when to shut up.
Answer in 60-90 seconds max, then stop.
Make eye contact.

Let them ask the follow-up.
 
If you want scripts for the dinner, the car ride, and the hallway ambush questions, that’s what the Playbook is for.
 
Link in my first comment. Drop PLAYBOOK in the comments and I'll bump the link for you.
 
📌 Save this for the night before your campus visit.
📧 Send this to the friend who’s flying out for an interview next month.

#phdjobs #facultyhiring #postdoc

How-to-win-faculty-interviews-beyond-the-job-talk

Smart PhDs don’t read papers. They interrogate them.
 
PhDs stuck in the lit review? This 15-minute rule changes everything.

You open 37 PDFs. You read 4 full papers. You write 0 useful paragraphs.

Most researchers still try to prove their rigour by reading every word of every paper. That’s a trap. That approach leads to burnout and unfinished drafts. You need a process that filters information quickly so you can focus on writing.
 
Step 1: Scan (3 minutes)
 
Interview the document instead of reading it. Spend three minutes on the abstract, figures, and discussion. You look for the core claim. If the figures prove the point, you have what you need. If the abstract is unclear, discard it.
 
Step 2: Select (deep read, once)
 
Focus your attention where it matters. Choose one section, usually the methods, for a deep read. If a paper supports your current view, add a citation note and move on. Reserve your deep reading energy for papers that challenge your assumptions.
 
Step 3: Synthesize (3 sentences per paper)
 
The surprising truth: restricting time increases quality.

Before you close the PDF, force yourself to write:

1️⃣ The claim
2️⃣ The evidence
3️⃣ Why it matters for your project

This becomes the raw material for your manuscript.
 
Lit reviews aren’t won by who reads the most,
but who synthesizes the fastest
 
Restricting time increases the 𝘲𝘶𝘢𝘭𝘪𝘵𝘺 of your thinking, not the quantity of your reading. Limiting yourself to 15 minutes per paper forces you to detect the signal.

You stop wasting mental energy on passive reading
and start building your argument.
 
What this buys you in one morning:
 
2 x 45-minute sprints:
→ 30 synthesis sentences
→ skeleton of your related work chapter.

A single morning session builds the foundation of your related work section or chapter.
 
I designed S³ while publishing in HCI.
It keeps you rigorous 𝘢𝘯𝘥 fast.
 
Want this as a plug-and-play system?

The S³ Literature Review Tracker turns your stack of PDFs into a morning ritual: scan → select → synthesize. Designed for part-time PhDs who need a rigorous lit review without reading 200 full papers. Includes: Notion dashboard, AI prompts, printable templates for offline reading.
 
🧠 S³ Literature Review Tracker (Notion dashboard + AI prompts + printable templates)
 🔗 https://lnkd.in/eT6Gr8y4

🖨️ Send this to the lab mate who still prints every paper.

#litreview #phd #research

Since my PhD, I’ve published 200+ papers.

Here are the 5 rules that actually got them accepted:

In my first year, I had three papers rejected for the same reason: weak discussion sections. Nobody tells you this in grad school, so here’s the version I wish I’d had.

(I know, some Universities charge crazy tuition for this)

1. Ace your literature review → comprehensiveness and critical analysis. A review that's exhaustive creates context. Therefore, it's easily defensible.

Most researchers list papers, not synthesize knowledge.

2. Focus on your discussion → goes hand-in-hand with point 1. Less than 1% of your interpretations will be groundbreaking. So connecting your results to existing literature? Absolutely vital.

Remember the rule: 1 finding. 1 interpretation. 1 significant contribution.

3. Pay special attention to figures → SPSS is standard, sure. But that doesn't mean you should use just screenshots. That extra "polish" never goes unnoticed.

First impressions matter, especially in peer review.

4. Context. Context. Context. → Doesn't matter what type of research you primarily do. Quantitative. Qualitative. Or mixed methods. Context is how you prove relevance.

Pay special attention to your implications and limitations!

5. Never submit and forget → One of the worst things you can do as a researcher is send your manuscript and NOT be prepared to revise. 

Remember:
Reviewers scrutinize your results.
But readers remember your discussion.

And before anyone says it...

No, you don't need this many publications to be successful. But a strong publication record certainly plays a HUGE part in your future academic career!

Hope you gained some insights today!

(Thanks so much for spreading the word on this one)

📌 Save this for your next submission (and send it to the labmate who keeps submitting and forgetting).

If you’re stuck on a paper right now, which of these 5 is giving you the most trouble?

🎅 If you want 24 AI prompts behind publishing tactics like this, you can get them for the next 48 hours in 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗹𝗶𝗻𝗸 𝗶𝗻 𝗺𝘆 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀.

You've written 50,000 words of great research.
 
But we'll delete 42,000 of them.
 
Most Master's thesis conversions fail.
Because they cling to their original work.

You need the guts to cut.
 
After supervising dozens of PhDs and reviewing hundreds of rejections, I see the same disaster unfolding over and over. You aren't writing a paper. You're creating a patchwork of disconnected ideas.
 
Here is how to stop the bleeding and actually publish:
 
Stop treating it like a book. Publish your thesis fast.
 
A thesis is a documentation of your education.
A journal article is a single contribution to science.

They are not the same species.
 
Trying to condense a thesis is like trying to turn a house into a single brick. You can't just smash it smaller. F*ck that diamonds under pressure analogy. You have to take the one good brick and throw the rest away.
 
In your thesis, you had to prove you read everything.
You wrote a history of the field.
 
Journals do not care. Sorry.
 
If you submit a 4,000-word literature review: Rejected.
Delete the exhaustive history.

Keep the 8-12 papers that prove your problem exists.
If it doesn't directly set up your fight, it dies.
(You should still have more references!)
 
One paper equals one claim. Boom.
 
Your thesis had four research questions.
Maybe even three pilot studies.
You feel attached to them.
 
Cool. Get over it.
 
An easy-to-accept journal article handles one focused contribution. Not two. Definitely not four. Pick the single strongest finding that surprises people. Everything else is a big salami that no one can put on their Butterbrot.
 
You don't need more data.
 
Most students think they need to run another study. You don't. You have plenty of data. You just lack the guts to delete the 80% that doesn't matter.
 
🚨 Screenshot this: your 4-step protocol for this week

1. Write your one single claim on a Post-it note. Stick it to your monitor.
2. Open a blank document. 
2.1 (Do not copy-paste your thesis.)
3. Copy 𝘰𝘯𝘭𝘺 the paragraphs that prove that one claim.
4. Delete any sentence that starts with "It is important to note."
4.1 (It rarely is.)
 
Stop rewriting the same chapter for six months.
Ship one ugly draft before Friday.
You got this.

📌 Save this so you don’t rewrite the same chapter for six months again.
 
If you want the exact checklists and templates I use to strip a thesis down in 4-8 weeks, I built a system for it.
 
Grab the 'Master’s Thesis to Article Accelerator':
👉 https://lnkd.in/esh3WgkJ

Link also in my first comment—comment BUTTERBROT if you want me to DM it to you.

#mastersthesis #publish #research

How to turn your Master's thesis into a publishable journa

Most research dies before it ever reaches a journal

And it’s completely avoidable.

The real reason your thesis never becomes a paper isn’t quality but bad strategy.

I’ve helped more than 100 students turn theses into accepted papers. After 15 years supervising graduate researchers, this is the mistake I see everywhere.

Students spend months deleting text.
They should be extracting value.

Your thesis already contains publishable research.
Here's the decision framework that makes it
publication-ready from day one:

Decision 1: Extract One Central Claim

Articles advance one idea.
Not five. Not three. One.

Find the result that would
surprise experts in your field.

If it takes more than one line,
you don't have the claim yet.

Decision 2: Select Your Target Journal First

Random submission guarantees desk rejection.

Pull five recent articles from your target journal.
Match their length, style, and evidence standards.

Email the editor a 150-word abstract before writing.

But after coaching researchers across 20+ fields,
this next point is the real challenge.

Decision 3: Cut Using Surgical Deletion

You must remove 80 percent.
Trim by rules, not vibes.

Intro: Keep only the motivation, gap, and contribution.
Related Work: Synthesize 8-12 papers by theme.
Methods: Cite standard procedures and protocols.

Results: Only data that supports the single claim.
Discussion: Lead with findings, then explain meaning.

Decision 4: Restructure Your Story

Theses demonstrate your knowledge.
Articles advance everyone's knowledge.

Lead with the contribution.
Use figures that carry weight.
Use headings that guide logic.

Decision 5: Add What Journals Need

Thesis committees and journals enforce unlike merits.

Write structured abstracts with Background, Methods, Results, Conclusions sections.

Conflicts. Contributions.
Checklist items matter.

Miss one and expect rejection.

The barrier isn't research quality.
It's strategy.

Pick your one claim this week.
Everything else becomes easier.

If you want the full breakdown, I wrote a deep dive here:
https://lnkd.in/dvwUv3F6

And if you want to convert your thesis fast,
our PhD Thesis Fast Track course is $10 until Nov 30:
https://lnkd.in/dmppmiGa

(For some new AI workflows, join me on Thursday on the livestream here: https://lnkd.in/dM5evDU8)

#masters #publicationready #research

My student almost quit her PhD.
 
Her prompts were the problem.

I see this happen daily with researchers:

(Last month I reviewed an AI-assisted thesis draft and the same mistakes keep showing up.)

They assume ChatGPT knows how to cite properly
They paste prompts without frameworks
They avoid reading source material
They skip methodology entirely
They miss critical context

It kills papers before they start.

My AI-writing process has 8 steps:

1. Identify the real need
→ What do you want the AI to accomplish?

Prompt: I want [specific output] that [achieves X goal] for [target audience]

2. Gather solid data
→ What context does AI need?

Prompt: Use this background information: [key facts, constraints, requirements]

3. Analyze the context
→ What critical details matter?

Prompt: Pay special attention to [specific elements] because [reasoning]

4. Create prompt strategies
→ How can AI assist here?

Prompt: Follow this process: First, [step 1]. Then [step 2]. Finally [step 3]

5. Pick the right format
→ How should the output look like?

Prompt: Structure the response as [format] with [specific elements]

6. Plan Validation
→ How to check quality?

Prompt: If you had to check your output, list the criteria you would check against.

7. Test & validate
→ Does output match goals?

Prompt: Review this output and improve [specific aspects]

8. Keep iterating on results
→ What can we improve?

Prompt: Based on previous response, adjust [elements] to better [goal]

Most people just write: "Write content about X"
Then wonder why results disappoint.

You can't write what you don't understand.
Stop rushing to AI tools.

Start owning your research:

• Read primary sources
• Gather real evidence
• Question AI outputs
• Map key concepts
• Test assumptions

Great academic writing comes from deep understanding.

Not quick AI generations.

Great prompts can go a long way to help you though.

Thanks for reading my tips.

Comment with the number (1–8) you’ll fix in your next paper.

If you’ll use this in your next draft, hit 𝘚𝘢𝘷𝘦 so you don’t lose it.

I break processes like this down in more detail in my newsletter for researchers. Link here: https://lnkd.in/eMt5PNJ7

#chatgpt #writingtips #research

PhDs finishing in 5+ years lack top research questions.

They should have asked simpler questions.

Some doctoral students spend six months on questions.
(They could have started collecting data in week three.)

The process is not that complicated.
We just make it complicated to justify our existence.
 
Here's the version nobody teaches:
 
1. Pick a topic you actually care about
 
Research takes years.
If you choose something because it sounds impressive,
you'll hate yourself by month six.
 
Follow your energy.
What do you read when nobody's watching?
What problems make you angry?

That's your starting point.
 
Not "What will get me tenure."
Not "What will impress the committee."
What keeps you reading past midnight when tired.
 
2. Read until you find the gap
 
You can't ask a new question
if you don't know what's already answered.
Spend two weeks reading recent papers in your topic.
 
The shortcut: jump to Limitations or Future Research.
Authors hand you a to-do list of unsolved problems.
 
That's your gap. Pick one that matches your resources.
 
3. Narrow it until it hurts
 
Climate Change is not a research question. It's a Wikipedia category.
 
Use the question words: Who? What? Where? When? Why? How?
 
Turn "School violence" into
"How does bullying affect grades of middle school students in rural areas?"
 
The narrower, the better.
If your question feels too small, you're getting close.
 
4. Write it as a sentence
 
Good questions don't start with "Is" or "Does."
They start with "How" or "What" or "Why."
 
If you're stuck, fill in this template:
 
"I'm studying [Topic] because I want to find out [Question] to help my reader understand [Significance]."
 
One sentence. No jargon.
If your grandmother can't really understand it, rewrite it.
 
5. Test it before you commit
 
Run three checks:
1. So what?
Does the answer matter to anyone besides you?

2. Reality check:
Can you finish this with your actual budget, timeline, and skills?

3. Clarity:
If you showed this to a colleague, would they know exactly what you're measuring?
 
If any answer is no, go back to step three.
 
The students who finish their PhDs in five years
don't have better questions.

They have simpler questions that they can execute.
 
Stop trying to solve world hunger.

Start trying to answer:
• one specific thing
• about one specific group
• under one specific condition.
 
Share this with the PhD student in your lab who's been iterating their question for eight months or more.

You're not a PI. You're a proofreader.

I've coached 15+ new PIs (assistant professors) for this.

The pattern is always the same.

Smart researchers. Drowning in their team's drafts. Rewriting every paragraph at 11pm. Missing grant deadlines because they're stuck fixing someone else's comma splices.

They call it supporting my students.

I call it a slow-motion career wreck.

Here's the brutal math: Your intellectual capital costs $200+/hour. You're spending it on work a grammar checker handles in 30 seconds.

The fix takes 21 days.

WEEK 1: Stop reading garbage

Institute a Zero-Draft Rejection Policy. No draft lands on your desk without three things:

1. A structured outline
2. A self-critique checklist
3. A confirmation it passed a grammar checker

Missing any of these? You don't read it. Period.

WEEK 2: Standardize the output

Mandate paragraph templates.
Ban sentences over three lines.
Require group peer review 𝘣𝘦𝘧𝘰𝘳𝘦 the draft reaches you.
 
Your team learns to catch their own errors. You stop being the safety net.
 
WEEK 3: Change your job description
 
Stop reading for typos. Start reading for one thing only: Is this idea fundable?
 
Your new feedback vocabulary:

• What's new here?
• Is it feasible?
• Who cares?

If you can't answer those from the draft, the communication failed. Send it back.

The surprising part?

Your team gets 𝘣𝘦𝘵𝘵𝘦𝘳 when you stop rescuing them. They learn to think like reviewers. They submit tighter drafts. You edit less and strategize more.

After 21 days, you're not a proofreader anymore.

You're the Editor-in-Chief of a publishing operation.

📌 Save this if you're ready to stop being your lab's bottleneck.

You're not a PI. You're a proofreader.

Status updates are where manuscripts go to die.
 
You already know this. You just haven't fixed it yet.

Your weekly meeting ends. Nobody knows what's next.
That's not a meeting. That's a group hallucination.

After supervising 8+ postdocs, I stopped running status meetings. Now we run a standup once a week. Meetings got shorter, and manuscripts actually started moving.

Every manuscript and grant moves one step forward.
Or we know exactly why it didn't.

Here's the structure:
The infographic below is the 20-min checklist we use.

1. No spectators allowed.

If someone doesn't own a manuscript or grant deliverable, they don't attend.

2. Everyone updates the dashboard before the meeting.

Stage. Next action. Owner. Deadline.

If you can explain it before the meeting,
don't explain it during the meeting.

3. Talk in verbs and deadlines.

If it needs more than 60 seconds,
that's a separate working session.

"Draft discussion section by Friday."
"Respond to reviewer #2, comment 4, by Monday."

One sentence per project.

4. Surface blocks, not stories.

One round:
"Who is stuck, and what do you need?"

"I need a decision on target journal."
"I need stats guidance on Table 2."

Name it. Assign it. Move on.

5. End on time. Always.

20 minutes. Hard stop. Confirm dashboard changes.
Repeat critical dates aloud. Done.

One slightly ironic detail:
The PI should talk less than 50% of the time.

Your job is editor-in-chief, not hero writer.
If you're doing all the talking, you're still the bottleneck.

Run this for a month.

Check:
• Did at least one paper or grant move stage each week?
• Do people leave knowing exactly what to do next?

📌 Save this and run the check once a month.
If you hit two nos, you’re back to status meetings.

If you want more shipped papers and fewer zombie status meetings:

I write about grants, papers, and running a sane lab.
One email per week. No fluff.

→ https://lnkd.in/e4HfhmrH

And if you want more lab publishing posters, comment “LAB LEADER” and I’ll send them your way.

#newpi #researchleaders #phd

Stop AI tone from killing your research productivity.

The prompt rules most researchers ignore:

GPT-5.1 finally fixes the robotic tone problem.
But only if you structure your prompts correctly.

Most researchers will keep complaining about AI tone instead of fixing their prompts. People blame AI for the outputs their instructions trigger.

Here's the exact system to stop AI's call centre bot tone:

1. The Golden Rule Formula
Task + Context + Target Audience + Example + Format + Tone.

This isn't optional anymore.
Vague prompts create garbage outputs.

Each element controls a part of the AI's behaviour.
Missing elements trigger more generic output.

The result: people don't read what your create with AI.

2. Vocabulary Control

Create ban lists: "leverage," "facilitate," "utilize," "encompasses."

Create prefer lists: "use," "enable," "includes," "shows."

GPT-5.1 actually follows these lists now.
Previous versions ignored them within three sentences.

The difference is night and day.

3. Define Clear Criteria

Set explicit rules before AI writes anything.
Goal, method, stopping rules, uncertainty handling.

Force the AI to plan 3-7 steps first.
Then self-check against 3-5 quality criteria.

Only after validation does it produce final output.

4. XML-Style Tags
Separate instructions using <task>, <context>, <format> tags.

The AI processes each section independently.
No more instructions bleeding into each other.
No more misinterpreting what you actually want.

Clean boundaries create clean outputs.

5. So, What's New?

GPT-5.1's adaptive reasoning changes everything.

It uses 71% more tokens on complex thinking.
It uses 57% fewer tokens on simple formatting tasks.

This mimics how human experts actually think and write.

Simple tasks get quick treatment
Complex analysis gets deep thought.

The system works because it addresses root causes.
It's a great method that makes ChatGPT more effective.

If your AI still sounds robotic, it’s not the model. It’s your prompt discipline.

If you want the detailed versions of these systems, I break them down every week in my newsletter. It’s where the practical academic writing and AI stuff lives: https://lnkd.in/e4HfhmrH

#ai #genai #gpt51

GPT-51-Facts-Hacks-and-Tactics from Lennart

Most academics have 24 unsolved problems in 1 draft.
 
You know something is wrong. But you can't name it.
 
Is it the gap statement? The confounds you missed? The fact that your hook sounds like a tax form?
 
All of the above. Plus 21 more problems you haven't spotted yet.
 
After 15 years of reviewing manuscripts, I can tell you why papers get rejected: authors try to solve 24 complex problems simultaneously.

You're trying to be a writer, a statistician, a storyteller, and a critic at the same time.

That's not writer's block. That's cognitive overload.

Here's what makes published researchers stand out:
They solve one problem at a time.

Not in a random order. In the sequence that prevents catastrophic failure first, then fixes the logic, then polishes the prose.

The problem isn't your intelligence. It's your process.

I broke down the publication pipeline into 24 specific, solvable problems.

Then I built an AI prompt for each one.

Days 1-8: Process and Polish
 
The stuff that seems minor until it destroys your credibility. Falsifiability checks. Accidental plagiarism detection. Predatory journal verification. Wordiness reduction that cuts your page count by 20%.
 
Days 9-16: Cognitive Load Management
 
The synthesis work that triggers severe procrastination. Gap analysis. Devil's advocate simulation. Theme naming. Conflict spotting between contradictory studies.
 
Let AI generate the harshest Reviewer 2 critique before the actual Reviewer 2 does.
 
Days 17-24: Existential Risk Removal
 
If you mess these up, you don't have a paper. You have a diary entry.
 
Problem definition. Gap statements. The 'So What?' check. Confound identification. The difference between a desk reject and a revise-and-resubmit lives here.
 
The 24-Day AI Research Rescue Protocol delivers one prompt per day to your inbox starting December 1st.
 
You paste it. You run it. You fix one specific problem.
By Christmas, your manuscript is submission-ready.
 
No course dashboard. No videos. Just daily execution.
 
$24 gets you 24 days of focused repair work on the exact problems that get papers rejected.
 
Link in comments.
 
If this could save someone's research career, send it to them.
#phd #research #aiprompts

Most researchers still build presentations the slow way.

This session shows the faster route.

AI for Research Presentations with NotebookLM, Gamma, and Gemini:

I’ll demonstrate a complete workflow using NotebookLM, Gemini Canvas, and Gamma App to turn raw research material into a polished presentation from scratch.

What you’ll see:
• How to structure a presentation directly from source documents
• How to use Gemini Canvas to refine arguments and visuals
• How to generate slide-ready drafts instantly in Gamma
• How to iterate the entire stack without losing control of your narrative

If you want to upgrade how you communicate research, join the livestream.

I spent 15 years fighting literature reviews.
 
Hundreds of papers. Thousands of highlights. 0 system.
 
Then one desperate client deadline changed everything.
 
I finally cracked the code.
Here's what happened:
 
My client needed 200 papers reviewed.
 
They had three months. And a full-time job.
The old approach meant certain failure.
So I started timing them.
 
One paper took 90 minutes. Another took two hours.
Most of that time was wandering through sections.
 
They had no plan. No structure. No exit strategy.
So I built them a checklist.
 
Nine questions I'd ask every single paper.
Same questions. Same order. Same format.
 
Week one felt painfully slow.
Week two, something clicked.
Week three, they finished papers in 12 minutes flat.
 
Their notes finally talked to one another.
Gaps appeared between studies like neon signs.
The committee called their lit review unusually detailed.
 
I almost laughed.
 
Detail was system.
Detail was repetition.
Detail was discipline.
 
My 15 years of struggle ended with one client checklist.
 
You don't need to read faster.
You need better questions.

Grab the checklist during my Black Friday sale here: https://lnkd.in/eq_7A-S3
 
I've launched a new store with best stuff here and will be launching products all the way to Black Friday: https://lnkd.in/eUmZJZg3

#blackfriday #checklists #phd

Thanks everyone for joining me on the livestream today.

It was lots of fun and I think we learned a lot about AI.

#ai #research #phd

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