As we watch Generative Artificial Intelligence (henceforth referred to as GenAI) develop so rapidly in front of our eyes, STEM students across the world begin to pose a significant concern: is AI going to replace my future career?

Admittedly, this is quite the exaggeration. Jobs within STEM are highly complex, and it is unlikely GenAI now, or in the immediate future, will be able to manage that kind of work efficiently. But, the world we were taught to expect - a place filled with long hours of work, hands on tasks within labs, hours of creating and processing data - is slowly fading away, instead being put into the hands of new GenAI softwares, leaving current university students in a position of uncertainty - if Generative AI is taking on all the tasks we expected to do, where does that leave us?

Well, it happens to leave us in a very exciting new position, one whereby we are teetering on the edge of a whole new world of potential discoveries. But how are we supposed to know about this? How are we supposed to feel prepared to take this on? Those currently in the workforce are dedicating themselves to learning how to take advantage of all this new technology, but in many ways undergraduate university students are getting left behind, especially since many of us still see GenAI as being an environmentally wasteful technology, a form of ‘cheating’, and a dangerous path leading to overreliance and lazy habits. Truth is however, AI is going to continue developing whether anyone likes it or not.

Having spoken to a range of UniMelb STEM students, the utilisation of GenAI is a common practice, but everyone’s habits differ enormously, resulting in varying levels of computer literacy. Yet there is a clear shared perspective that within the next few years, skills within GenAI literacy is going to become an important prerequisite to employers, in all STEM areas ranging from engineering to biomedical research. 

However, none of the interviewees could express exactly what GenAI literacy encompasses, and what specific skills tertiary students should be learning. That is primarily because most GenAI use is self-guided and private, with little discussion among peers concerning what specific software should be employed, or how it should be used. 

Thus, there is a general feeling of uncertainty - if AI literacy will become a compulsory skill, how can we prepare ourselves? 

Interestingly, Monash University has this at the forefront of their mind, with dedicated AI based skill classes available for students. Associate Professor Hoppe from the Monash Department of Anatomy and Developmental Biology - who teaches some of these classes - states the following, when asked whether GenAI is helping or hindering students in STEM:

"Generative AI is improving students’ preparedness for the workforce… I think GenAI also has the potential to level the playing field, but only if students have equitable access to and understanding of these tools. As an educator, I see this as critical - I want to ensure all students can use these technologies effectively, rather than only those who are already confident or experienced.”

Clearly, classes dedicated to the utilisation of GenAI are incredibly important, but such classes aren’t yet widespread enough to elevate all students onto the same computer literacy playing field. That is not to say however that it won’t happen soon. Possibly what is most important right now, is that we must all accept there is no avoiding GenAI in today’s world, and so we must take the initiative in turning GenAI into a source university students can employ confidently, and graduate into the workforce with appropriate and equal skill levels. 

Having also interviewed a range of postgraduate individuals alongside the undergraduate students within STEM, a key idea of what a future university STEM life - that includes education around GenAI - might look like became apparent. It seems that most students primarily have access to softwares like ChatGPT, Microsoft Copilot, and Gemini. Whilst they can easily be abused and be turned into cognitive crutches, Matthew Macawon , a bioinformatician in the Department of Immunology at Monash Universe says, “generative AI has the potential to greatly improve the efficiency of learning for students by explaining difficult concepts in ways that are more comprehensible”.

Additionally, Matt’s coworker Neo Nguyen, who also works in the Department of Immunology makes the important point that “learning in STEM has never been solely about arriving at the right answer but the simultaneous development of soft skills such as sound judgement, resilience, and the ability to reason through uncertainty.”

Associate Prof. Hoppe also adds that instead of only teaching content - which most students access through GenAI anyway.“We place greater emphasis on developing core skills such as communication, critical thinking, technical capability, and teamwork. These technical and psychosocial skills alongside hopefully greater confidence in seeking out opportunities, enable students to be prepared for the evolving workforce.”

Yet another unanswered, but key, question remains. Yes, the importance of GenAI has been established, but what might utilisation within the post graduate STEM workforce actually look like? Luckily for many postgraduate STEM workers, GenAI isn’t a new topic, and there are many truly astounding projects and careers developing right in front of us that perhaps can help provide an idea of what might be waiting for us out in the big wide world of adult jobs.

To start off, Matt (Bioinformatician) and Neo (Computational Research Assistant) from Monash, have stated to utilise GenAI software daily. In Matt’s case, it’s for his day-to-day tasks 

"Tasks that would previously have taken hours to complete, they are now completed within minutes”, but then states in relevance to complex softwares “the interpretation and analysis of complex data will and should remain in the hands of skilled individuals who can ensure that AI hallucination and drift does not negatively impact critical findings”. 

For Neo, GenAI has completely shifted his approach to research, whereby it enables him “ to understand and address health challenges from new angles and leverage emerging technologies to make research faster, more powerful and more impactful.”. He also expressed, similarly to Matt, that “rather than eliminating the role of a researcher, I believe AI will redefine what a good researcher looks like. The most effective researchers will be those who can integrate computational tools with biological insight while maintaining critical judgement.”

From the University of New South Wales, there is a stunning example of STEM and GenAI working cohesively for drug discovery, more specifically to predict drug synergy, cellular response to chemical perturbation, and pharmacological properties.Tom Marsland is one of the individuals who focuses on developing AI models specific to this domain. On this specific project, he states: 

"Leveraging AI to build more AI in the space of drug discovery, fundamental biology, and medicine will lead to some very important breakthroughs and technologies … saving years and hundreds of millions to billions of dollars of investment into a process that has a 90% failure rate.”

Additionally, he raises an important point that these GenAI models they develop could lead medicine in the direction of disease prevention rather than reaction post-diagnosis, specifically expressing:

"Rather than reacting to disease after it emerges, it could highlight probabilities of disease emergence and progression over time for an individual, allowing prevention or early management.”

Clearly, GenAI has some amazing capabilities, but instead of thinking of it as a cognitive crutch, it should be a priority for all current tertiary students in STEM to push for digital literacy skills to be included in our education, to prepare and encourage a new era of more efficient and exciting science, technology, engineering, and mathematics.