The renaissance of PhD programmes:
Embracing AI and the return of the polymath?
In an age where AI is revolutionising every sector, how might it reshape the very essence of academic pursuits?
By Dr Shadi Hijazi, Principal Consultant, QS
Throughout history, polymaths have been at the forefront of innovation, leaving indelible marks across various fields of science and the arts. Leonardo Da Vinci, celebrated for his myriad of roles from artist to anatomist, stands as a testament to this. Avicenna (Ibn Sina), an 11th-century Persian physician, is another shining example, with his vast contributions ranging from astronomy to philosophy. To provide a more diverse historical context, one can also look at figures like Rabindranath Tagore, an Indian polymath who dabbled in everything from poetry and painting to philosophy.
The modern era, however, has ushered in a wave of specialisation. Today's PhD programmes are perceived as deep dives into unique areas, distinct from broader studies of the past. After foundational courses in their Bachelor's and Master's, students are set on a path to contribute directly to science through their PhD research. This journey, typically spanning three to six years, begins with defining a research topic and culminates in a thesis, with seminars, peer reviews and potential publications in between.
For many, a doctorate symbolises the pinnacle of academic achievement. This specialised journey usually requires both an undergraduate and master's degree in the relevant field. Yet, the exponential growth of knowledge and the specificity of PhD research led to the birth of the professional doctorate. This degree moulds experts in fields like business and law, equipping them to address real-world challenges and ensuring they remain leaders in their professions. It offers top-tier postgraduate education without veering into extreme specialisation.
2023 has been a transformative year, prompting us to reevaluate the structure and purpose of PhD programmes. This year saw a massive uptick in the adoption of AI tools. Soon, the innovative ChatGPT interface for generative AI, introduced by OpenAI in late 2022, will herald a new era of versatile and practical AI tools. These tools are poised to become integral to daily applications for billions worldwide. The upcoming version of Windows will feature an in-built AI co-pilot, and the next iteration of Microsoft Excel plans to integrate Python and its machine learning libraries. ChatGPT's influence is expanding, now integrating with services like graphic generation in the popular design software, Canvas. OpenAI's professional tier of GPT-4 caters to corporate needs, and Adobe's foray into generative tools with Firefly is revolutionising the design sector. Developers now have a plethora of premier coding-centric generative AI tools at their disposal, from Github Copilot to Meta's robust open-source platform, Code Llama. Furthermore, platforms like Google Colab and Hugging Face democratise access to advanced models, which once necessitated costly hardware. It's clear: we're entering an era where familiar tools are being enhanced and transformed by AI.
AI influx in the classroom
This AI wave is leaving its mark on academia. The primary concern for many has been the potential for plagiarism and cheating. Ensuring that AI tools aren't misused to bypass assignments and exams is crucial to uphold the educational goals of universities. Platforms like Future Tools list an astounding 280 AI tools dedicated to copywriting and research. In response to this AI boom, AI Detection tools have emerged to identify AI-generated content. This dynamic between AI content creators and detectors highlights the balance between groundbreaking advancements and ethical responsibility.
Among the plethora of AI tools, Caktus AI shines as a unique student resource, curiously missing from Future Tools' list. This emerging AI-powered content generator offers more than just writing services; it curates academic content, STEM resources and professional services, serving over 2 million students. While Caktus AI's essay and paragraph writing tools mirror ChatGPT's features, its unique ability to evade AI plagiarism detectors and incorporate citation sources distinguishes it. The rise of such tools has propelled the issue of AI-driven plagiarism and cheating to the academic forefront.
As we navigate this AI influx, it's vital to consider how postgraduate programmes, especially PhDs, might evolve. Is it time to resurrect the ideal of the Renaissance being, contributing across both science and arts? Can we foster 'generalists' — individuals who harness AI to traverse multiple disciplines, blending specialised depth with interdisciplinary breadth? This potential shift challenges the deep-rooted hyper-specialisation of PhD programmes and resonates with Blaise Pascal's sentiment: “It is much better to know something about everything than everything about something. Such universality is the finest. It would be still better if we could have both together, but, if a choice must be made, this is the one to choose.”
While this vision may seem utopian, integrating AI tools into academia could indeed promote a holistic approach to PhD programmes across various domains. AI's potential to amplify research capabilities is unparalleled. It can sift through vast data sets at incredible speeds, identifying patterns and correlations that might escape human notice. This empowers PhD candidates to explore broader research areas, merging insights from different disciplines to enhance their primary study focus.
The days of skimming journals in university libraries are a relic of the past. While digital journals and research have evolved, AI-powered searches can elevate this process to new heights. This progress also encourages interdisciplinary research and collaboration, allowing insights from one field to benefit another, fostering the rise of generalists. Platforms like nextnetinc.com are trailblazers, aiding scientists in discovering hidden connections and formulating hypotheses from diverse data sources.
Efficient time management is another key area. With AI handling tasks like data analysis and content generation, researchers can allocate more time to understand the broader implications of their work, attend interdisciplinary seminars and engage in cross-disciplinary discussions. Tools now cover a vast spectrum, from managing repetitive tasks like recording, transcribing and analysing audio, video, and text files (e.g., speakai.co) to storing and retrieving unstructured data (www.quivr.app) and conducting AI interviews for extensive qualitative research (www.getreveal.ai). Numerous tools also specialise in summarising content, exploratory data analysis, copywriting and editing.
"The present scenario offers a unique chance to redefine PhD programmes. Instead of producing hyper-specialised experts, we might see the rise of modern polymaths, individuals with deep expertise in their chosen field and a broad understanding of other domains."
Holistic learning
However, the essence lies in championing meta-learning. PhD students must grasp the philosophy of their discipline, which might involve a foundational understanding of the broader philosophy of science and perhaps philosophy in general. Comprehending the historical evolution of their discipline, mastering literature, honing critical thinking skills and understanding ethics are paramount to instilling a universal perspective in the PhD candidate.
During my Master's, I was fortunate to attend a qualitative research methodology course that spanned from the philosophy of science to practical research methods. Each session was led by an experienced professor specialising in that area. This holistic exposure ensured a profound yet extensive understanding of science and research. As AI tools facilitate interdisciplinary and generalist approaches, it's imperative for PhD candidates to focus on 'meta' subjects like the philosophy of their science, its history, critical thinking techniques, and effective teaching methods. Incorporating AI training and AI ethics into the PhD curriculum is essential to make this happen.
The present scenario offers a unique chance to redefine PhD programmes. Instead of producing hyper-specialised experts, we might see the rise of modern polymaths, individuals with deep expertise in their chosen field and a broad understanding of other domains. As academia fully embraces AI tools, the boundaries of knowledge will expand, potentially reconciling the age-old tug-of-war between specialisation and generalisation.
This article was published originally in QS Insights Magazine 8.