"AI will allow educators to focus on what matters — teaching & mentoring students": GITAM's Prof S Arun Kumar
While the industries themselves are grappling with Artficial Intelligence (AI) developments, are our educational institutions truly ready to offer courses on AI and Machine Learning (ML)? Why or why not?
The fast pace of technological advancement in AI and ML means that educational institutions need to be nimble and agile in their attitude towards the same. Institutions must continuously update their courses, invest in faculty training, and provide access to cutting-edge resources to keep pace with scientific and technological developments.
While some institutions are leading the way, others are still finding their feet to meet the demand.
The rapid evolution of AI and ML has indeed presented challenges for educational institutions. While many universities have introduced AI and ML programmes, there’s still a significant gap between academic curricula and rapidly evolving industry needs.
However, we are seeing positive trends. Many institutions are partnering with tech companies to develop more relevant curricula. There is also a growing focus on interdisciplinary approaches, recognising that computer science and AI impact virtually every field.
At GITAM, the inclusion of AI and ML in the curriculum is two-fold. Our engineering curriculum is frequently revised to incorporate cutting edge engineering concepts in AI and ML.
In addition, both engineering and non-engineering students are taught the use of the latest AI tools. Further, the study of use cases and projects involving the application of AI and ML in diverse industry verticals is part of the curriculum.
What are the new advancements and developments you predict in AI and ML?
The AI and ML landscape is evolving at a breakneck pace. One major development is the increasing sophistication of natural language processing (NLP) — especially Large Language Models (LLMs) — which will lead to more intuitive and human-like interactions using AI systems.
Additionally, advancements in reinforcement learning and unsupervised learning techniques will enable AI to learn more efficiently from less data.
Another exciting frontier is AI for social good. We will witness AI and ML being applied to address global challenges such as accessible healthcare and education for all.
On the other hand, AI and ML present certain challenges like the requirement of enormous computing power and storage, resulting in an increased carbon footprint and e-waste.
It is important to strike a balance between these pros and cons of AI deployments for reaping their benefits in the long run.
As far as AI and ML trends go, what do you think educators should look for?
We are witnessing a transformative period where AI is not just a buzzword but a powerful tool reshaping how we teach and learn. One of the most exciting trends we are observing is the quick growth of the generative AI market. It is projected to reach a staggering $207 billion by 2030, up from an estimated $23.17 billion in 2022.
In the education sector, we are particularly interested in how AI is personalising the learning experience. Adaptive learning platforms and intelligent tutoring systems are using machine learning algorithms to analyse student data and provide customised instruction.
This is a huge step towards effective education, based on one’s learning capabilities and pace, rather than a one-size-fits-all approach.
We are also excited about the potential of ML in educational analytics. This is helping us gain deeper insights into student performance, identify learning gaps and provide timely interventions.
Lastly, AI has the potential to revolutionise administrative tasks in education. By optimising resource allocation and making informed decisions, AI can help educational institutions operate more efficiently. This will allow educators to focus more on what really matters — teaching and mentoring students.
As we move forward, it is crucial to remember that while AI and ML offer immense opportunities, they can only augment human intelligence and skills — not replace them.
In addition, safe and explainable AI and ML models need to be trained on unbiased data to provide responsible decisions. Therefore, educational institutions need to leverage these technologies carefully, ensuring that they serve to elevate the learning experience for all students.
Are the engineers we are nurturing today ready to face the ethical challenges of AI that will come up tomorrow? Because that is one major concern in the field, the ethics of it all.
The ethical challenges of AI are indeed a pressing concern in the field of engineering education. We are just beginning to understand the ethical implications of the adoption of AI. While some discussion on these ethical implications has started, the rapidly evolving nature of AI means that we must continuously adapt our approach.
Practical, hands-on ethics training that mirrors real-world scenarios is one way to do this. This includes working with diverse datasets, addressing algorithmic bias and designing transparent and explainable AI systems.
The key is fostering a mind-set where ethical considerations are seen as integral to the engineering process of AI deployments, not an afterthought. By cultivating this ethical awareness alongside technical skills, we can better prepare our students for the complex AI landscape they will face in the future.
It is crucial that we maintain open dialogues with industry partners, social scientists and cognitive specialists to ensure that our education remains relevant and that our future engineers are equipped to tackle the ethical challenges of AI head-on.
