AI in Academia - Awesome or Asinine?

Summer Elsie explores the pros and cons relating to the rise in AI use in academia and discusses how to find the balance between the costs and benefits of AI.

A person typing on a laptop at a coffee table. Image Credit: Peter Olexa on Pixabay

At first sight, artificial intelligence, such as Open AI - seem like shining beacons when it comes to contemporary academia; efficiency gains, promising breakthroughs and a new version of thinking. However, like every shining beacon, it casts long shadows onto the environment and beyond. Students and educators are increasingly incorporating AI into their daily life at university, so it’s worth asking - what is gained and what might be lost?

The Promise: What AI Brings to the Table

Efficiency and Personalization

Blackboard, Canvas and Moodle; these platforms are ubiquitous, but they often feel like a one-size-fits-all. AI, on the other hand, can offer adaptive learning tools and techniques that will tailor to a student or professors pace. A student that is struggling to cope with the rhythms of university life may receive targeted exercise in their weaker areas, yet a confident student may skip ahead. Tools like intelligent tutoring systems would provide immediate feedback, cutting corners such as wait times which are seen within the traditional feedback loops.

New Research Horizons

These large language models, data-mining tools and pattern recognition software are not just new buzzwords, but working instruments within cutting-edge research. They allow scholars to sift thoroughly through vast corpora, detect trends across disciplines and even generate insights that may have gone unnoticed in past research. AI could be used as a magnifying glass on the ‘academic unknown’ - helping us see what was once invisible.

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The Shadows: What We Risk Losing

Academic Integrity and Authorship

A looming question: when does using an AI tool cross the line from assistance with academics to academic dishonesty and laziness? If a student submits an AI-generated essay on their own accord, or if a researcher relies on generative test without any proper attribution, this erodes trust. Authorship becomes increasingly fuzzy. What does it mean to own an idea when much of the text or analysis submitted and/or published could be machine made?

Dependency and Skill Erosion

We risk creating students who can prompt to AI, but think less deeply. Skills, such as critical thinking, reading, manual data analysis, argumentative writing and independent synthesis become atrophy if AI becomes a crutch rather than a tool. It’s akin to always using a calculator for basic arithmetic; sooner or later, one forgets their times-tables.

Bias, Errors, and Misinformation

Artificial intelligence is trained on data that it's provided. Human-produced, human-biased, human-FLAWED data. The models ingest these temporal prejudices, replicating, amplifying, and sometimes misinterpreting. A false source, or a mis-attributed quote generated with confidence - these aren’t just glitches. Within academic settings, both accuracy and reliability matter significantly. An AI hallucination could mislead research and data, as well as propagate false narratives.

Equity and Access

Not all academics or students have equal access to higher end AI tools, subscription costs, hardware needs and bandwidth limitations - these are significantly limiting, and often reinforce existing disparities. What about students within underfunded institutions, or regions with weaker internet infrastructure? The AI revolution could deepen inequalities if it is not managed consistently.

AI, Forests and Carbon Emissions

Artificial intelligence, despite its benefits towards sustainability of the modern world, plays a significant role in the destruction of rural life; deforestation, greater emissions, water waste are all heavily linked back towards artificial intelligences, such as generative AI. When it comes to the use of AI, students, universities and the economy must decide on their own whether they want to cut time and use up more energy, or vice versa.

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Finding the Balance: How We Can Use AI Wisely

Setting clear guidelines: Universities should define what constitutes acceptable use of AI in coursework and research must be disclosed, what must be original, what must be vetted.

Integrate AI literacy into curricula: Students and faculty alike benefit from learning how AI works; its limits, biases, and potential pitfalls, NOT just how to use the tools.

Use AI for augmentation, not replacement: Let AI assist in drafting, repetitive tasks, but keep human judgment, creativity, critical thinking, and authorship in the driver’s seat.

Ensure equitable access: Subsidised access, shared tools, appropriate infrastructure must be part of the solution so that no one is left behind.

To Conclude...

AI in academia is not a question of “if,” but of “how.” It carries the possibility of transforming how we teach, learn, research, and yes, who gets to participate. But it also demands ethical vigilance, a renewed commitment to critical thinking and a willingness to guard against the ease of letting machines take over.

If we embrace AI as a tool, properly understood and responsibly used, it can enrich the academic landscape within remarkable ways. But we must not surrender the human core of scholarship: curiosity, creativity, skepticism, and integrity.

About The Author:

Summer is a graduate from the University of Exeter with a bachelors of science in Zoology. She studies a wide range of STEM subjects ranging from Chiropterology to a diploma within Robotics, alongside being the Deputy editor for Wild Magazine UK. You can follow her on LinkedIn: Summer Elsie.