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TL;DR:
- The ScholarShift Accelerator is FuKazee's four-week live programme for Master's and PhD students who want to use AI properly in their research, without risking their degree.
- It targets the problems that slow theses down: too many papers, scattered references, drafts that keep coming back from the supervisor, and fear of breaking integrity rules.
- You practise on your own topic, in evening classes on Google Meet with weekend workspace sessions, and every class is recorded.
- Learning alone from videos or a general AI course can work, but it rarely gives you a full research workflow, feedback on your own work, or clear integrity rules. Paid writing services are a different thing altogether: they are contract cheating, and they put your degree at risk.
- The next cohort is planned for late January or February 2027. The fee is on the ScholarShift Accelerator page, and the dates will be confirmed once they are set.
Who this is for
This post is for Master's and PhD students in Kenya and across Africa who are deciding whether to learn AI for their research, and how.
It makes our case for the ScholarShift Accelerator. We believe it is the best choice for many postgraduates, and we show the evidence so you can judge for yourself. We also say plainly who it is not for.
If you want the story of why the programme exists, Rodney tells it in Why I Started Teaching AI to Postgraduates. This post is about the decision in front of you.
What problems does the ScholarShift Accelerator solve?
It solves the mechanics of research that slow students down: finding and reading literature, keeping citations in order, structuring and writing the thesis, and using AI without crossing integrity lines.
The ScholarShift Accelerator page puts it this way: most students who stall "are stuck on the mechanics of research", and these skills "are rarely taught step by step." That gap is what the programme works on.
This is what postgraduates usually tell us first, as listed on that page:
- "I know I should be moving faster but I keep getting stuck."
- "I use ChatGPT, but I know I am leaving a lot on the table."
- "I need ethical ways to use AI without risking my degree."
- "I want a system that works for my specific research workflow."
Here is how each common problem maps to what the programme teaches.
| The problem | What it looks like | What the ScholarShift Accelerator covers |
|---|---|---|
| Drowning in papers | Hundreds of PDFs, no idea which matter or how they connect | Literature reviews with SciSpace, LitMaps, NotebookLM and Zotero |
| References everywhere | Notes and citations scattered; you no longer trust your own system | A Zotero setup and a simple system for sources, notes and references |
| Draft back again | The supervisor returns the chapter, again, and your timeline slips | AI feedback on structure and clarity, with you doing the rewriting |
| Integrity worries | Fear of being flagged, or not knowing what your university allows | AI ethics in academia: how to cite AI, disclose it and stay inside the rules |
| Weak prompts | You get vague, generic answers from AI tools | The four Cs (Clarity, Context, Constraints, Customisation) and CRAFT |
| Starting from zero each time | You re-type the same instructions every session | Building your own research assistant in AI Studio or Gems |
| Defence nerves | Slides and presentation left to the last week | Building your presentation in Canva before you submit and defend |
What do the four weeks actually cover?
One theme a week, six practical modules in all. You work on your own topic, paper, proposal or thesis, so class time moves your research forward too.
Week 1, AI Foundations. Responsible AI use in academic work, better prompts, and when to reach for ChatGPT, Claude, Gemini or similar tools.
Week 2, Research Tools. Mapping AI onto each stage of your research, reading across many documents at once with NotebookLM, and finding, comparing and shortlisting papers.
Week 3, Finishing Strong. Pulling your review workflow together, managing references until citations feel routine, and turning your ideas into clear slides for proposals and your defence.
Week 4, Wrap-Up and Review. Another build session for your presentation workflow, then a review of the whole system so you leave knowing exactly what to do next.
How it fits around a job and family
- Live classes on Google Meet, Monday to Thursday, 8:00 pm to 9:30 pm.
- A weekend workspace session each week, in person at 124 Manyani East Road, Nairobi, with a virtual session running in parallel if you are outside Nairobi or outside Kenya.
- Every session is recorded, and recordings stay available for one year.
- Live Q&A in every session, and direct access to Rodney throughout the four weeks.
- Occasional guest speakers, when an outside view helps the cohort.
Who teaches it?
Rodney Maina Wanjohi, FuKazee's founder, teaches the programme himself. His trainer profile says he "still teaches in every programme" and has trained more than 3,000 people since 2022, including teams, founders, postgraduates and students.
He holds First Class Honours in Journalism and Mass Communication from the University of Nairobi. He teaches research workflows and AI tools; your supervisor still guides your research.
He teaches the way FuKazee teaches everywhere: he assumes nobody in the room knows AI yet, avoids jargon, shows the free version of each tool first and never pushes one tool. He also says plainly that nobody becomes an expert in a few days. Four weeks gives you a working system and the habits to keep improving it.
Who has taken part, and what do they say?
Researchers in past cohorts have come from universities in Kenya, Ghana and Scotland, according to the ScholarShift Accelerator page. The same page names the University of Nairobi as "our strongest partner relationship", and Rodney's profile lists AI training at the University of Nairobi "plus postgraduates from six more."
These are postgraduate reviews from FuKazee's reviews page, quoted exactly:
"My ability to handle research and tasks has completely transformed. Thanks to this training, I've mastered tools like SciSpace, Zotero and generative AI, making paper writing and data review a breeze." Vincent G., Postgraduate researcher, Google review
"The practical workspace sessions were great for application into actionable insights. I would highly recommend FuKazee for students and professionals keen on AI literacy." Rebecca S., AI training for postgraduate students, Google review
"They really helped me with my thesis and research, the best. I'd recommend them." Mark L., Postgraduate, thesis and research, Google review
The ScholarShift Accelerator page also has three video testimonials, in the learners' own words:
- Lee Ngugi (PhD, Kenyatta University), on how the programme sharpened his PhD project planning.
- Dorothy (PhD student), on how learning AI in the programme now helps her far beyond academia.
- Dorothy (Masters student, University of Nairobi), on her experience of the ScholarShift Accelerator.
As of 12 November 2026, FuKazee's reviews page shows a 4.9 rating from 38 Google reviews across all programmes. Most reviews shown on that page are about corporate and individual AI training, so the postgraduate ones above are the closest guide to this kind of training.
How does it compare with the alternatives?
Our case: you can learn some of this alone, but the ScholarShift Accelerator gives you a complete workflow, built on your own thesis, with integrity taught from day one and a teacher who answers your questions. Here is a comparison in general terms.
| Learning alone from free videos | A general online AI course | Paid writing service | ScholarShift Accelerator | |
|---|---|---|---|---|
| Built for research | Depends on what you find | Often built for office work or business | No: someone else does the work | Yes, every module is about postgraduate research |
| Practise on your own thesis | Only if you organise it yourself | Rarely | No | Yes |
| Integrity taught | Patchy, and sometimes wrong | Often a short section | Breaks integrity rules | A core module from week 1 |
| Feedback and questions | Usually none | Forums or recorded content | Not on your learning | Live Q&A every session and access to Rodney |
| A full system at the end | Pieces you assemble yourself | General skills | Nothing you can reuse | A research workflow you keep using |
| Risk to your degree | Low, if you check what you learn | Low | High | Low, if you follow your university's rules, which the programme teaches |
Learning alone from videos
Free videos are a fine place to start, and some are excellent. The problems are order and fit. You learn tools in random order, from people who do not know your university's rules, and many students end with what the ScholarShift Accelerator page calls "a few scattered ChatGPT experiments." If you only need one tool, videos may be enough. If you need the whole chain from search to citations to defence, structure saves time.
A general AI course
In our view, a good general AI course teaches prompting and common tools well. But many are built for office tasks such as emails, reports and meetings. Literature mapping, reference management, disclosure in a thesis and preparing for a defence are rarely covered. The ScholarShift Accelerator teaches the same prompting foundations, the four Cs and CRAFT, and then applies them to research.
Paid writing services
This one needs a clear answer. Paying someone to write your proposal, chapter or thesis is contract cheating.
The UK's Quality Assurance Agency for Higher Education (QAA) defines it simply: contract cheating "happens when a third party completes work for a student who then submits it to an education provider as their own, where such input is not permitted." It also counts friends or family writing work for you, even when no money is paid. Your university's rules may use other words, but the principle is the same.
The risks are serious:
- Your degree. QAA's guidance treats contract cheating as more serious than ordinary plagiarism, because it is a deliberate decision. It recommends suspension or expulsion for serious cases involving multiple offences.
- Your degree, years later. Degrees can be taken back after they are awarded. In 2019, Kenyatta University revoked a PhD it had awarded in 2018, after finding plagiarism.
- Blackmail. QAA's guidance cites reports of essay mills contacting students after a purchase and threatening to report them to their university unless they pay more.
- The law in some countries. In England, the Skills and Post-16 Education Act 2022 made it an offence to provide, or arrange, these services for students in England in the course of a business.
- Your defence. You must explain and defend every argument in your thesis. Work you did not do is very hard to defend.
The ScholarShift Accelerator works the other way round. You stay the author. AI helps you search, read, organise and get feedback, and you disclose its use in the way your university requires. For a deeper look at where the line sits, read Using AI in Postgraduate Research Without Crossing the Integrity Line and our page on AI for thesis writing in Kenya.
When might the ScholarShift Accelerator not be the right fit?
It may not suit you if:
- You want someone to write for you. We will not, and the programme will not help with that.
- You cannot give it evening time for four weeks. Recordings help, but you get the most from doing the exercises on your own work during the cohort.
- Your university or supervisor bans AI tools for your work. Check first. Some of the skills, such as Zotero and a clean reading system, still help without AI, but you should not pay for a course you are not allowed to use.
- You need one narrow skill only. If all you need is help with references, a free guide such as How Do You Use AI for Citations Without Inventing Sources? may be enough.
- You need help with research design or statistics. Your method and analysis choices stay with you and your supervisor.
- You need to start before late January 2027. The next cohort is planned for late January or February 2027.
If you are unsure, the ScholarShift Accelerator page has a 60-second check that matches the four weeks to your stage.
Questions people ask
I already use ChatGPT. Will I learn anything new?
Most likely. Casual ChatGPT use is the starting point. The programme covers research workflows, specialised research tools, advanced prompting, ethics and building your own AI assistant, which most postgraduates have never set up.
Will using AI get my thesis flagged?
Using AI the way your university allows, and disclosing it, is the safest path. The programme teaches how to cite AI, disclose it and stay inside your university's rules. Always check your university's current rules and talk to your supervisor early.
Is it for Master's or PhD students?
Both. The fit check on the programme page covers Master's coursework and thesis stages, PhD proposal and data or writing stages, and people about to start.
When is the next cohort?
The next cohort is planned for late January or February 2027, once university exam dates are known. The ScholarShift Accelerator page shows the fee now, and the dates will be confirmed once they are set.
Will FuKazee write my thesis for me?
No. FuKazee teaches you to use AI and research tools well. You remain the author of everything you submit.
Sources
All sources last checked 12 November 2026.
- FuKazee, ScholarShift Accelerator page
- FuKazee, Rodney Maina trainer profile
- FuKazee, Reviews and letters
- FuKazee, AI for thesis writing in Kenya
- QAA, Contracting to Cheat in Higher Education, 3rd edition (2022)
- UK legislation, Skills and Post-16 Education Act 2022, section 27 (offence of providing or arranging a relevant service)
- Kenyans.co.ke, "Kenyatta University Revokes Lecturers PhD For Cheating" (25 September 2019)
- Zotero: described on its own site as "a free, easy-to-use tool" for collecting, organising and citing research.
Where FuKazee fits in
FuKazee trains people to use AI for real work, and the ScholarShift Accelerator is our programme for postgraduates. We believe it is the best choice for a Master's or PhD student who wants a full, honest AI research workflow, because it is built on your own thesis, taught live by Rodney, and puts integrity first. The reviews and testimonials above are the evidence we point to.
Read the full programme, the fit check and the fee on the ScholarShift Accelerator page. The next cohort is planned for late January or February 2027.
Questions before you join? Email us at lead@fukazee.com
Want guided support instead?
Turn AI into a repeatable research workflow. ScholarShift Accelerator is the hands-on cohort for Masters and PhD students who want a repeatable AI-supported research workflow: summarising papers, reviewing literature and organising sources.
Rodney has run FuKazee's training since 2022 and still teaches inside every programme line: corporate sessions, the postgraduate cohort and the KCSE examiner webinars. First Class Honours in Journalism and Mass Communication, University of Nairobi.
