I love and admire Generation Z. They are smart, fearless, always learning, and have a very important ability: to detach themselves from expectations of others. They are also easy to connect with and always happy to teach you their language if you are really interested.
On a typical day, I interact with 4-5 youngsters in the 16-21 years age group. They work in coffee shops, restaurants, retail outlets; they come from villages, towns, and cities; they study, work, hangout with friends, live in the moment, and also think about what they should do next. They analyse (life) as part of their nature. As technology natives they understand privacy much better than any other generation and are very good at disconnecting. In fact, per my observation, they use technology mainly to communicate and to get certain things done. For the most part, they are very hands-on, connected to nature and physical activities.
Contrary to popular impression, this generation is not embracing AI nor are they technology dependent. They do use AI just as they use Insta or any other tool and for specific purposes. Nothing more, nothing less. Or maybe I am just meeting a subset of the generation that is like that. I must have spoken to at least 70 students this past month and guess how many are planning to study AI or some stream of engineering? Three. A majority of them are studying or planning to study psychology, the remaining are evenly distributed between mathematics, physics, communication, performing arts, sports, and interdisciplinary bachelors.
Within my family (including nieces, nephews) there are 12 in the 17-25 age group. Out of these 12, one is studying AI, two are planning to pursue engineering of some denomination, one is studying medicine, one is pursuing music studies, one is preparing for visual design studies, one is studying business administration, one is preparing for paramedical and nursing studies. Thus, no trend as such. Everyone seems to be following their interests. And those who are not clear about their interests as yet, are opting for general studies.
But what is a field of study? A ‘field of study’ refers to a structured domain of knowledge, inquiry, or practice, typically defined by its methods, principles, and subject matter. The concept of educational fields has evolved significantly over time:
Ancient Times: Education was organised around foundational disciplines such as logic, language (grammar, rhetoric), and philosophy. These core areas were seen as essential to the cultivation of knowledge and the development of rational thought.
Medieval to Early Modern Period: The classical trivium and quadrivium dominated early curricula, evolving later into distinct pure and ancillary fields. Sciences (e.g., physics, biology), social sciences (e.g., economics, sociology), arts, and humanities (e.g., literature, history, ethics) began to take form.
Post-Modern Era: The 20th century saw the rise of applied disciplines, which emphasised practical application of theoretical knowledge. Fields such as architecture, industrial design, journalism, applied sciences, and filmmaking emerged with professional and vocational focus.
Digital Age: The current era is marked by a significant shift toward interdisciplinary and multidisciplinary studies. These involve the integration of knowledge and methods from multiple traditional fields to address complex, real-world problems—e.g., data science, cognitive science, environmental studies, digital humanities, and human-computer interaction.
All major categories of academic fields—such as ethics, logic, pure sciences, social sciences, arts, and humanities—continue to be offered in today’s educational institutions at undergraduate and postgraduate levels. In parallel, newer subject areas like data science, environmental studies, human-computer interaction, architecture, design thinking, media studies, and bioinformatics have emerged to reflect contemporary needs and interdisciplinary integration.
To make a choice about a field of study, it is important to understand the Academic Progression across levels. Each level of education serves a distinct purpose in the intellectual and professional development of students:
- Undergraduate (Bachelor’s Level):
- Focus: Foundational knowledge and general exposure.
- Students learn basic theories, methods, and paradigms of their chosen field.
- Examples: Bachelor of Arts in Philosophy, Bachelor of Science in Physics, Bachelor of Architecture, Bachelor of Data Science.
- Postgraduate (Master’s Level):
- Focus: Specialisation and depth.
- Students delve deeper into specific domains or questions, engaging with advanced concepts and methodologies.
- Examples: Master of Science in Physics, Master of Arts in Political Science, Master of Journalism, Master of Design.
- Doctoral (PhD Level):
- Focus: Original contribution and scholarly independence.
- The candidate formulates research questions, makes methodological decisions, and aims to generate new knowledge or critique existing paradigms.
- This stage emphasises intellectual autonomy and critical decision-making.
While the content and techniques differ by discipline—e.g., empirical methods in physics vs. critical analysis in literature—there are shared academic structures:
- Emphasis on critical thinking
- Engagement with core texts or data
- Training in argumentation and communication
- Encouragement of original inquiry
Defining Artificial Intelligence as a Discipline, Why Study AI? Interdisciplinary Foundations and Applications
So coming back to AI as a subject of study: What does it mean to study AI? Why would one study AI? I look at Artificial Intelligence (AI) as an emerging interdisciplinary scientific field. It is not pure computer science because it involves at its core mathematics, cognitive science, computer science, engineering and philosophy. It is important to always be aware that it is an evolving field and how it shapes will continue to change. For instance, in recent years much of the development of AI is by way of software innovation in machine learning learning and data modelling, which has led to emergence of sub-fields such as Data Science (which is also an interdisciplinary field of study). As a philosophical and academic subject area, AI began much before software engineering emerged as a field of study because AI is an exploration of logic and cognition. So you would study AI if you are looking for answers to questions such as: Can intelligence be developed outside of life forms? Can human intelligence be replicated? And so on. It is answers to such questions that have led to the development of learning algorithms, decision-making models and the development of real-world systems such as autonomous vehicles, recommendation engines, and natural language processors. In the context of computer science, AI influences and is influenced by software, hardware, and networking. Thus at the Master’s level one could pursue studies in specialised hardware that accelerates AI workloads or explore distributed systems and networked infrastructure that facilitate mass-scale deployment of AI. Of course, most of AI’s contemporary development is linked to software processes by way of design, implementation, and optimisation of models and learning systems. Given the nature and extent of its adoption, AI is rapidly and continuously reshaping knowledge, knowledge systems, work processes, and therefore it is now at the centre of existential, scientific and philosophical enquiry, which adds a completely new dimension of study of AI.
One efficient way of comparing how different paths may stack up in the job market is to see if the different paths may lead to jobs in the same industry. (Which is the reality in today’s world). For example, one can do a Master’s in English or Physics or a program inData Science and all three could end up working in a technology company in the same division. But there would be differences in strategy points, salary levels, and subsequent progression.
Comparison Summary (in Creative-Tech Fields like Architecture Software)
| Path | Time | Strength | Limitation | Industry Role |
| Physics (BSc + MSc) | 5 years | Analytical, technical modeling | Needs design/context knowledge | Backend AI, simulation, optimization systems |
| English (BSc + MSc) | 5 years | Storytelling, user empathy | Needs technical/tool skills | UX writing, narrative design, user research |
| AI/Data Science (BSc | 3 years | Interdisciplinary, practical | May lack domain depth | Smart design tools, data-driven creative apps |
So what’s the take then? All roads can lead to jobs or professions or work. Study what you are interested in.