Ever since we started using computers, we have been taught to type everything on keyboards. And as a result, this is how we interact with the machines. So whether you write papers, you write responses to emails, you write LinkedIn posts, anything, essentially, you're just typing. Recently, I was introduced to VoiceInk, an application that lets you dictate what you want to say and transcribes it into text. I know I've been trying to use Google Docs for this. I've been trying to use MS Word for this and then including some of the transcription services available on Mac. But I always found that it was fairly inaccurate. I couldn't really rely on it because the grammar was off, and it missed words because of my non-native accent. But now we see that AI tools actually make it much more profound. So you can essentially leverage AI to understand how you speak and convert your speaking to text with very high accuracy. So the tool that I'm using, as I mentioned, Voice Inc, it uses some of its own AI tools, but you can also hook it up to OpenAI or any other tools that you have available. You can use open source tools like Ollama. To get that AI enhancement. And it has already saved me quite a lot of time. So I started using that two weeks ago. Right now, it has saved me about 40 minutes to 60 minutes of my time. It's a little bit weird for me to speak to the PC rather than type. So I'm still learning. But I thought I would share this with you. I know we are all kind of struggling with time, and essentially, you speak faster than you write. So that could actually help you with writing papers because you can just start talking about your paper, talking about your results, talking about your discussion, and then just fix the grammar, fix the writing and everything else. So that's one of the main reasons why we have a writer's blog, because I posted on LinkedIn today. We do want to make sure our writing is perfect. And as a result, we write and then we edit, we write and then we edit. And as a result, we waste time.

The main idea behind this community is to give you practical tools for publishing and for building your visibility as researchers. And because I'm not just talking about this, but I'm actually walking the walk of building that visibility myself, I thought I would give you a practical example of how you can share your research via webinars by inviting you to one of my webinars next week on Wednesday, the 22nd of April. I'm going to share some insights from my recent research on decarbonising the transport sector. So what we're going to talk about in that webinar is the different decarbonization pathways based on the Tees Valley case study, where we're going to explain electrification, hydrogen, whole-system cost analysis, and many, many more details. If you're up for it, here's the registration link. It's free to sign up. https://book.drhanak.com/industrial-decarb P.S. Feel free to share your published papers/presentations with the community - you never know what collabs it could generate.

In today’s live session, we explored how to use AI tools to enhance your research without outsourcing your brain. The focus was on doing this ethically, transparently, and in a way that actually strengthens your critical thinking rather than weakening it. Why use AI in research? - To speed up literature discovery, initial synthesis, and data extraction so you can spend more time on real thinking and analysis. - To navigate growing publication volumes and quickly map out trends, contradictions, and limitations in a field. Key principles for ethical use - Always double- or triple-check AI-generated statements, numbers, and citations; you are responsible for the accuracy of your work. - Use AI to support your reasoning, not to replace it: reading and comparing papers manually is still essential for developing research-gap and critical assessment skills. What “Answer This” can help you with - Literature reviews: generating structured drafts with line-by-line citations, plus exportable reference lists for your reference manager. - Research gaps and field mapping: visualizing topics, trends, and underexplored areas, and running bibliometric-style overviews. - Data work: extracting numerical and tabular data from papers, building paper/data tables, and saving outputs into notebooks you can refine and edit. How to integrate it into your workflow - Start with a focused prompt (e.g. “comprehensive review on integrated CCU, focusing on materials, process integration, and business models since 2020”) and refine via filters (sections, number of points, Q1/Q2 journals, preprints only, date ranges, etc.). - Use the output as a starting point: export references, inspect DOIs, open and read the underlying papers, and then rewrite and restructure in your own words and style. If you missed the session, the recording is now available here - you can pause, follow along, and test the workflows on your own topic.

We’re all here to learn and develop our skills. But remember it’s equally important to rest. What are you up to this weekend? P.S. we have taken our crazy cocker spaniel for training today - she loved it!

I'm going live soon and will review a few papers today - if I don't select yours, don't worry, we'll make it a regular thing in the community! Here's the link to the session https://www.skool.com/live/l63ZPK3vPMp