Statement
AI Ethical Usage in Research

The power and functionality of AI, particularly large-language models (ChatGPT, Gemini, Claude, etc.) are quickly and radically altering all paces of human life. From basic customer service to art, there are countless professions, specialities and skills that are being either radically accelerated or entirely replaced by AI.

When applied to research, AI flips everything on its head. At the core, research is an information-gathering and information-sharing practice. Academic literature, whether a research paper, book, original source, etc., stores the information we use in research. We contribute to this growing body of human knowledge with each new publication.

AI tools today allow for rapid information analysis and writing. On the face of it, they completely annihilate “traditional” forms of research we’ve built over decades (even centuries). This shift has led to many rapidly-enacted institutional policies at universities, publication venues, etc., as well as a general sense of anxiety and fear surrounding the accuracy and reliability of `academic research which relies on or uses AI in any way. Nonetheless, AI has still taken the research world by storm. Many academics are using AI in their work, especially to write papers (e.g. 17.5 – 20% of papers published in computer science, and perhaps of 1% of all papers published in 2023 alone).

AI is far from the first novel technology to challenge our pre-conceived notions of how we gather, analyse and share information with one another. But, unlike previous technologies, it’s novelty and power seem unprecedented.

Through Effortless Academic, I’ve explored, used and taught a variety of research tools and methods, including some that use AI. The Tools list on this website outlines some of the top AI research tools I’ve reviewed and shared in my blog, newsletter and courses. I have dedicated significant time to reviewing and understanding the use of these AI tools in mainstream academic research. This means talking to the founders of these applications, using and analysing the tools in-depth, and creating actionable workflows that elevate academic productivity while keeping the researcher at the core of the entire process.

Here, I outline the academic standards I follow for myself, and therefore for the entire Effortless Academic platform. All my workflows, courses, and offerings adhere to these standards.

I care about good research, and I believe responsible use of AI is becoming (and already is) an integral part of being a great researcher. If you are capable of making life-changing discoveries that can save lives, address global warming, or provide some other mission-critical information faster and more effectively with the use of AI, how is it ethical to actively try and banish or avoid these technologies? Alternatively, how can you call yourself a researcher if you type a question into an AI agent, get back an answer and copy & paste this result as your own? What have you actually “researched”? These are the questions we must look at, critically and honestly to ensure we are maximising this tech, while upholding ourselves to the highest standards.

The Effortless Academic AI Ethical Use Standard

The Effortless Academic brand, courses, webinars, blogs, workflows and all related materials adhere to the following standards to ensure fair and ethical usage of AI and tools in academic research. As a communicator of innovative AI technologies, I take responsibility in sharing not just the what but also the how when it comes to AI in research.

I recommend all researchers to adhere to the following guidelines when using my suggested workflows and materials. These guidelines ensure the highest academic integrity, as well as a feeling of confidence and ownership in research that cannot be separated from high-quality output.

1. Ownership: I take full responsibility for every word, citation and piece of content I create when I publicly share my research. I ensure I can stand by my research contributions (oral presentations, papers, etc.). Wherever I identify as the author and owner of my work, I acknowledge that I can be held liable for its accuracy and reliability.
2. Understanding of Tools: I use a variety of resources, tools and innovations to support me in productive, accurate and efficient research. I understand how these tools function, their limitations, biases and perceived benefits.
3. Compliance: I am aware of and adhere to the policies and requirements of the institutions I interact with (institution of employment, journal venues, etc.). This ensures the larger research community can trust my work and that I do not engage in academic misconduct.
4. Researcher Identity: I am a researcher seeking to understand and advance my field. As such, I am the end-product of my notes, research and contributions. I take pride and ownership in my knowledge.
5. Duty to Advance Knowledge: As a researcher, I am obliged to contribute meaningfully to our collective knowledge in order to solve important problems. As such, I have the privilege, and even the obligation, to seek out and adopt efficient practices that support my work and enable me to be maximally effective.

The Role of AI in Effortless Academic Workflows

The generation of academic knowledge follows a simple 2-part recipe: understand your field, write your paper. But how you chose to understand your field is not always evident in the paper. AI seeks to automate many of the steps in the process of understanding, as well as in writing.

However, this automation can feel threatening to many researchers. This can be from a lack of understanding, concerns about dwindling cognitive sovereignty (discussed in video below), or just a fear of how the research landscape is changing.

A researcher may feel that a process appears like “cheating” when it seems just too easy or too quick compared to traditional methods. But, why is it cheating to use AI summaries or synthesis, and not cheating to use statistical models that would save us hours of manual calculations? The answer lays in our cognitive sovereignty, what we own in the process versus what we choose to automate.

Today, contemporary AI research tools enable radical acceleration of previously tedious and time-consuming research tasks. We’ve gone from spending months in a library collecting sources to spending days or weeks searching via online databases (Google Scholar, PubMed, Scopus, etc.) to now needing only a few minutes to search for a research question and get back a curated set of relevant resources with their findings synthesised into neat sub-topics.

This isn’t just wildly unprecedented… it threatens the very nature of what it means to be a researcher. I believe the way we do research has already fundamentally changed. I summarise this in the video below.