What Research Skills Should One Have In The Age Of AI?

We are currently living in an era where generative AI has become a sort of tool that, if you don't have it, you are considered to be an outdated fossil. On one hand, it makes sense considering how important AI has become in critical thinking and research nowadays, but on the other hand, it has also led to a gradual erosion of research skills among the student populace.

Many students nowadays question whether or not they should learn new research skills in the first place, as they believe that these skills would soon become redundant anyway. If you are one of those students who think that research and analytical skills are becoming obsolete, there are some things that you need to know about. There are actually some skills that only humans can capitalize on, and learning these skills should be your top priority.

Is AI-Assisted Research A Direct Threat To Traditional Research?

Before we take a look at the essential research and analytical skills you need to have in today's day and age, it is important to answer one fundamental question: Is AI-assisted research a threat to traditional research? While most academics would say that the answer is a unanimous yes, we think the answer is more nuanced than just a simple yes or no. Here are some points that suggest whether or not large language models are a threat to academic research or not.

1) Automation of the Baseline:-

AI can instantly handle foundational tasks like literature reviews, data extraction, and summary generation. This makes low-level, entry-point research roles highly vulnerable to AI automation.

2) The Integrity Crisis:-

Over-reliance on generative AI greatly increases the risk of students accepting AI hallucinations. Hallucinations, in this case, mean the AI conjuring up random information that is unsubstantiated yet easily accepted by people who do not test the veracity of the information by themselves. Too much trust in hallucinations can destroy the integrity of academic research.

3) The Skill-Gap Threat:-

The main danger is not necessarily to the research process itself, but to those researchers who fail to evolve past mindless information consumption and cannot critically vet or effectively prompt the AI. Many people use large language models as assistants, instead of tools that also have to be mastered.

4) Erosion of Foundational Memory:-

Constant use of AI for information recall can weaken the researcher's knowledge base. While the erosion of knowledge won't happen in one go, the overtime effects caused by excessive AI reliance can be disastrous as the researcher's mind slowly loses the critical thinking skills it had developed over the years.

5) Focus Shift: From Collector To Curator:-

A rather overlooked aspect of AI's influence on research is that generative AI has effectively rendered data collectors obsolete. The researcher is now forced to evolve to become an expert curator who can synthesize, verify, and ethically apply AI-provided data.

Top Academic Writing & Research Skills That You Must Know In The Age Of AI

In this section, we are going to discuss some of the most underrated yet essential research skills that you need in order to keep up with the rise of generative AI. Large language models can probably do most things that humans are capable of, but there are some key skills that only humans can successfully capitalize on, and here are some of them.

a) Creating Academic PRP (Purpose, Rationale, Procedure):-

The core challenge in all works of academic literature has to be identifying the gaps in the literature that only your research can fill. While you can use the Google Scholar AI to find a ton of sources from your body of knowledge, it is best that you learn how to prompt AI effectively to generate multiple drafts of the Rationale section.

This will allow you to spend more time refining the central thesis and ensuring the theoretical contribution is razor-sharp.

b) Meta-Analysis and Data Synthesis:-

Meta-analysis refers to the scientific method of strategically combining results from multiple independent statistical studies to come to a final, powerful conclusion. You must apply judgment to define which studies are methodologically sound enough to be included.

One thing you should also take note of is the fact that large language models can flag statistical anomalies, but the researcher must interpret the implications of publication bias and selection bias across studies, and articulate these limitations in the final paper.

c) Grant Applications: Persuasive Storytelling:-

A grant application is a very high-stakes avenue for persuasion. Even though AI can write clearly, it can't convey the human emotion and persuasion that only a human will be able to show, thanks to its academic writing capabilities. Remember, a good grant application can only be written by a human.

d) Creating a PPT:-

Can AI make PPTs? Yes! But it is not something that you should rely on. AI presentations can only present the content in a substandard way, but the purpose of a presentation in research is to elevate the content, not merely present it. A systematic review of the presentation should prove that your content benefits greatly from the presentation in one way or another.

e) Presentation:-

We would like to repeat the previous point, but in a more all-encompassing way. The human act of presenting your work to the review board is something that AI can never replicate. Often, you will see that the mere skill of presenting a paper can elevate everyone's perception of a paper that would have otherwise been considered mediocre.

Qualitative research skills like authentic engagement, reading the room, and adapting your delivery based on audience feedback are skills that only a human deserves to possess.

Do Not Make These Skill Acquisition Mistakes In Research!

There are a lot of skills that you need to have before you start dabbling in research. But there are some mistakes people make in the initial stages of skill acquisition that can haunt them later on in their research process. Here are some mistakes you shouldn't make while acquiring skills to build your data literacy and academic research skills.

1. Mistaking Prompt Engineering For Research:-

AI can draft for you, it can summarize information for you, it can even synthesize and edit your paper for you, but it cannot provide an original line of thought. This is why you need to keep in mind that asking the right question of the AI doesn't equate to high-level research. Prompt engineering is a good skill to have, sure, but it isn't something that should replace critical thinking and creativity.

2. Outsourcing Critical Source Verification:-

The biggest threat to research and data literacy as a whole is the mindless acceptance of AI-generated sources. AI has a tendency to hallucinate citations, which is why you should always cross-check the sources after AI gives you a response.

3. Over-Indexing on Tool Proficiency over Conceptual Depth:-

Always remember that the goal of AI-assisted research is not to master every single AI tool, feature, or advanced querying language. The main objective is actually conceptual depth, which is exactly what a researcher should strive for instead of chasing tool proficiency.

4. Relying on AI for Thesis Generation:-

In qualitative research, many people suggest that you can use AI for brainstorming and synthesizing research. However, do not make this mistake at all. Never forget that AI only picks from existing literature, and your primary objective (which we don't think could be any less obvious) is to provide new information. The entire point of thesis generation is defeated if you cannot give new data in the end.

5. Neglecting Presentation and Interpersonal Skills:-

Lastly, always remember that a presentation goes a long way in research, more than you can imagine. Whenever your professor conducts a systematic review of your paper, the presentation will be the thing that catches their attention the most. Simultaneously, the presentation could make or break the professor's opinion of your paper.

Final Verdict: AI For Research Or Smart AI Use By Humans For Research?

Before we wrap things up, we must discuss whether or not you should delegate every single research task to AI or not. While the final choice is yours, we highly recommend that you only use AI for research in a limited capacity as of now. You see, generative AI tends to make a lot of mistakes as of now. Hallucinations are one thing, but most studies show that no AI-assisted research tool can perform all research functions perfectly.

Some tools are good at synthesizing information, some are good at qualitative research, some are good at audio-visual media generation, some are good at writing literature reviews, some are good at finding citations, and some are good at quantitative research. Unfortunately, there is not one single AI tool that can perform all of these tasks flawlessly. This is why you need to keep in mind that using AI for research can be incredibly unwise if you totally rely on it, but it can be a wise decision if you know how to use it in moderate doses.

Conclusion

Overall, the era of AI has not only begun, but it is in a highly transformative stage as of yet. There is absolutely no shame in using AI to enhance your research, but as we said earlier, completely relying on AI-assisted research tools is nothing short of stupidity. Keep in mind that there are some skills that only human beings possess, and your aim as a researcher should be to harness these skills while also mastering the usage of AI tools and techniques to become the most perfect research scientist that the 21st century needs. If you want to learn more about academic writing and research skills and want to start as a researcher yourself, feel free to contact India Assignment Help, and our experts will help you learn how to master AI research tools like a pro.