January 2024 – José A. Apolinário Teixeira

Learning about the use of AI generative tools for literature reviews

I have already written and published several literature reviews. Some were pure review papers, but most were empirical research papers that reviewed extant theory before reporting methods and results.
I have been more lucky to publish systematic literature reviews than with critical literature reviews (have one that has been bouncing back with rejects already 3 times). Besides my experiences, I feel that my students know more about using some online available tools for literature reviews than me.

I have a very carefully curated collection of references organized in .bib files, but my students might write faster literature reviews even if they did not read, digested or organized hundreds of papers as I did before. I learned during my master thesis, that you (1) get an article, (2) read a sentence (3) imagine that you throw the sentence written on paper to the garbage, (4) write in your words sentence and cite the source. However, now as a teacher, I came across students that did not get the articles, barely read them and go to one of the recent AI generative tools that can write and format the literature review for them as in a quite advanced state.

Whether is ethical or not, whether it leads to plagiarism or not, it’s productive. Still, literature reviews generated with those systems will not lead to serious scientific advances per se. But I think in future, many great scientific discoveries will be communicated and integrated with a literature review that leveraged the power of AI generative tools or AI “co-pilots”.

It is a hot topic, last December when attending the International Conference on Information Systems, many panels were discussing the use of generative AI systems in teaching and research. A panel on the use of AI generative systems in papers submitted to top journals led to a lot of discussion and disagreements.

It’s time to learn about this things, and here is a good place to share my lessons learned. I started learning about this on YouTube. I ended up following the videos of academics such as @DrAndyStapleton, @profdavidstuckler and @DrMatJ.

They introduced me to a good few tools and processes that should help in the process. It’s time now to test them and see if they can help me with my current research on how students evaluate information in the context of sports and fitness. At the moment, I already know about a few “must cite” papers. Would AI tools find them? Will I get plagiarized content? Would it be worth learning of these tools? Or it is faster to just do it the good old way? So far is seems a very interesting and a creative process to combile all those different tools.

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How to introduce the open source concept to a more general audience

As early put by Marc Andreessen (2011), a well-respected serial entrepreneur and venture capitalist who co-founded Netscape and later sold to AOL for $4.2 billion, “software is eating the world”. Every industry ranging from oil, gas, financial services, healthcare, education, and even defense are increasingly powered by software.

Software matters for our modern societies. There should be no doubts about it. Still, few people realize the importance of open-source software. From my experiences of talking with highly educated professionals without formal computer-related education, many do not know what open-source means. Neither they can give examples. This is even if they use it (without realizing it) on their Apple computer, their BMW car, their Android-based smartphone, or their Samsung TV. Worst than that, many associate it directly with software that does not cost (that you don’t need to buy).

To me this is surprising, one of the latest industry reports points out that:

99% of new software projects rely on open-source components;
78% of companies use open source software over proprietary software;
96% of applications have at least one open-source component;
Open source makes up over 80% of the software code in use in modern applications;
The top contributor to open source projects on GitHub (a popular hosting service for open-source software projects) is Microsoft;
Over 70% of developers said that working on open-source software projects helped improve their skills;

Still, it is easy to spot active university students, lawyers, politicians, nurses, doctors and professors who don’t know what open-source software is, nor have “never heard about it”. How can something so impactful in our society be not even noticed by the general population? Maybe this lack of awareness explains why my research proposals related to open-source software don’t get more funding. After, most software runs in the hardware, being often invisible for the common users.

The point is software is very important, open-source software is also very important. This is because most software is either: (1) open-source per se, or (2) largely dependent on it. Still, people do not understand the meaning of open-source software. Many never hear about it. Maybe they heard about Linux, Android, or Firefox, but not open-source software. Often misunderstanding the concept with the concept of freeware.

Due to my research and past professional experience, I got invited a few times of times to give a talk about open-source software. Often to a young audience at universities (i.e., guest lectures) or to local businesses in Finland (e.g., training, seminars, tech Tuesdays, among other internal events in companies). Here I share my approach to talking about open-source software in a more general audience:

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Four methodological books I recommend to Master Students

For the ones doing their master thesis within a business school, or for the ones looking for an introductory book on (1) qualitative research, (2) quantitative research, (3) case study research, or (4) social network analysis. Here are my recommendations. Those would be the “first reader” to the different methodologies. I read myself all of them. But I might have missed the most recent editions.

Book cover of Doing Qualitative Research by David Silverman (6th edition) as on amazon.com

1) Silverman, D. (2022). Doing Qualitative Research (6h Edition). Sage.

Written in a easy and accessible style, the book provides a step-by-step guide to a first qualitative research project. This was my main book during the first year of my doctoral studies. It is a very easy to read and very well structured. Full of examples.

Book cover of Quantitative Social Science Data with R: An Introduction (2nd edition) by Brian J Fogarty as on Amazon.com

2) Fogarty, B. J. (2023). Quantitative social science data with R: an introduction (2nd edition). Sage.

I am a very big fan of R. Mastering is a very valuable competence to have in both industry and academia. Being able to load data into R and perform visualizations and statistical analyses using some of the hundreds of modules available is a good way to master quantitative research. R works with both small and big data. I value this book because introduces the methodology with “hands-on” examples on my favorite open-source statistical platform. There are many books out there on R and quantitative methods. I think this is a fun one for a master student that wants to go quantitative.

Book cover from Case Study Research and Applications: Design and Methods 6th edition as in Amazon.com

3) Yin, R. K. (2009). Case study research: Design and methods (6th Edition). Sage.

It is one of the most cited methodology books in the social sciences. It was my main reference for my own Master’s Thesis where I interviewed many people at Nokia to understand “Why do mobile devices vendor co-produce a mobile platform in an open-source means?”

Book cover of What is Social Network Analysis? by John Scott as in bloomsbury.com

4) Scott, J. (2012). What is social network analysis? Bloomsbury Academic.

While there are better reference books on Social Network Analysis (SNA), this one introduces the principal ideas, nature and purpose of the method to non-specialist readers. It touches theory, methods and tools. While reading it, we also learn about topics on across many disciplines. To get it, you do not even need to go to library. You can just download it by open-access manners via https://library.oapen.org/handle/20.500.12657/58730.