What I Learnt on the INASP AI in Research Pathway — And Why It Changed How I Think About My Work
The Learning Experience
The first thing that surprised me was the format. INASP's AI in Research pathway has been structured to bring learning resources together in one place. The courses, videos, and other learning materials are all under a specific topic. Rather than searching for other resources and hunting for different things, the platform provides an easy way to reach other learning resources with just a click, and also helps bookmark such pages. Added is the transcription for all videos, which helps save time and lets you read through the course faster. The platform also provides the space to learn at your own pace, earning digital badges as you progress, and the opportunity to share on LinkedIn after completion, which motivates learners to walk through the course successfully.
...the platform provides an easy way to reach other learning resources with just a click, and also helps bookmark such pages. Added is the transcription for all videos, which helps save time and lets you read through the course faster.
What I Actually Learnt
AI is not one thing: AI in Research Pathway taught me that AI is not one thing; such as ChatGPT, Claude, Meta, or Gemini, but AI is a vast family of tools and techniques. Thus, some are designed to perform various tasks such as – some analysing data, sorting images, detecting patterns in huge datasets, creating animations, and high music performances, etc. Some of these tasks are time-consuming, and a human would provide such a result. This boils down to the question, “Which ability should I complement with the use of AI?” rather than “Should I use AI?”
How AI can help one finish the research process: Various AI tools assist with literature searches, help structure arguments, summarise dense papers in unfamiliar fields, and to scrutinize the mind behind an abstract before submission. This has not entirely replaced the ability of humans to think; it rather means the ability of AI, as an assistant that can handle certain technical or time-consuming tasks, so one can reserve energy for the parts that truly require a human brain.
The critical importance of verification: AI tools can produce non-logical results with extraordinary confidence. Citations that may not be true or numbers that do not exist. The AI Pathway taught that most AI output should be treated as a first draft and not a conclusion; thus, results should be verified. This is the one lesson I would like to keep with me in my learning journey.
Thinking about bias: the world contains data everywhere; however, not all the data are reflected entirely and correctly in data systems. For researchers working especially in the context of the global south, this is a huge problem that cannot be overlooked. Thus, most researchers working across diverse contexts — especially those of us whose communities are underrepresented in mainstream datasets – take these matters very seriously. The pathway prompted the need to think carefully about whose knowledge is being centralised, or the voice that might be flattened. These are not abstract concerns. They affect the result.
The AI Pathway taught that most AI output should be treated as a first draft and not a conclusion; thus, results should be verified.
Recommendation
Almost anyone in academic research who feels like AI is moving faster than they can keep up with has the urge to learn something about AI. The pathway is designed for researchers at all levels — you don't need a technical background, you don't need to know how to code, you don't need to have experimented with any tools yet. You can start where you are.
AI has come to stay, so all hope of getting AI out of the way is aborted. What is left is the ability of the human resource to harness these tools effectively and efficiently to produce proper results free from bias. I am delighted to walk away as a researcher who now understands what I'm being sceptical about. That's a much better place to be.
AI has come to stay, so all hope of getting AI out of the way is aborted. What is left is the ability of the human resource to harness these tools effectively and efficiently to produce proper results free from bias.
The AI in Research pathway is available on INASP's learn@inasp platform at learn.inasp.info. You can find it alongside the grant proposal writing and research methods pathways, which are also excellent.