Seneca Polytechnic Dean Ranjan Bhattacharya explains how his engineering faculty is responding to disruptive technologies like ChatGPT, with lessons for every engineer looking to keep their skills sharp.
Technology changes fast, and it’s getting faster. How can engineers keep their skills sharp in the age of digital transformation?
We looked for answers in the gauntlet every engineer must pass: school. Whether your memories of engineering school are fond, foul or forgotten, there’s no better place to learn how to succeed as an engineer at any stage of your career.
Engineering.com sat down with Ranjan Bhattacharya, Dean of the Faculty of Applied Science and Engineering Technology at Seneca Polytechnic in Toronto, to understand how the next generation of engineers is being brought up to speed.
Bhattacharya explained how digital transformation is driving industry expectations, how ChatGPT is changing the nature of engineering education, and the utmost importance of an agile mindset—because by the time you finish reading this Q&A, who knows what new technology will be on the brink of disruption.
The following interview has been edited for brevity and clarity.
Engineering.com: How do you approach engineering education in an age of digital transformation?
Ranjan Bhattacharya: I’d like to contextualize it just a bit broader in terms of one of our main pillars here at Seneca Polytechnic: to work with our local industry and community partners on socioeconomic drivers of change. And digital transformation—or digitalization, depending who you talk to—is one of those driving forces of change.
The number one thing that they’re looking at us for is a really well prepared talent pipeline. We’re very fortunate that our graduates and our co-op students are all thought of very highly amongst our industry partners.
In terms of digitalization, it still requires that really solid grounding in math, in physics, in natural sciences and also in communications. Because those are the fundamentals upon which we build everything in future engineering graduates. We try to make sure that we get them set with their math skills, because math is the language of engineering.
How has your curriculum adapted to new digital tools and technologies?
We start introducing simulation and modeling tools fairly early on: Matlab, AutoCAD, SolidWorks and other computer aided design and manufacturing tools. We’ve also introduced Catia. The other one that we’ve used is Siemens NX software, specifically in product life cycle management where students are expected to be able to optimize a process line.
We’ve been trying to introduce data analytics and data visualizations as rapidly but as relevantly as we can. Those are really important to students. Everything now is IoT. So we try to introduce the concept of IoT, but the agnostic elements of different IoT platforms.
And then the other really new, exciting development for us is digital twinning tools. We’re creating a virtual digital replica of objects, systems or processes. So those tools and technologies, for example Unity and Unreal Engine for AR and VR experiences, which is part and parcel of digital twinning.
The other piece is around designing systems, processes and tools for humans—in terms of the human machine interface, making sure that is intuitive and easy to use. User experience is something I think engineers also need to be very aware of, so that’s something we try to embed into our students.
Automation and robotics is another integral part. At Seneca we teach students to operate the robots, but we’re now getting to the point of teaching students to design a robot for the specific application that their workplace requires.
How are you incorporating AI into your curriculum?
We worked a strategic framework around our approach to AI education. When you look at AI itself, you have AI theories, which lead to tools, which lead to business applications. We have taken the position that we’re going to teach the relevant theories, but that’s not going to be the focus. The focus for us is going to be on the tools and business applications, which I think is what an engineering degree is about—tool use, tool development, essentially looking at a business problem.
Large language models are going to be commoditized very soon, I’d say, so building those large language models as well is part of our AI education framework here at Seneca. That’s how it’s being incorporated into the curriculum. We’ve done our best to get in front of it and start to use it where it makes sense rapidly. We’re also developing new curriculum and new programming for Seneca. We are working on our AI governance and AI data policies right now. I imagine every other institution is as well.
Our software engineering program specializes in AI. We started that in 2021. We’re also launching a post graduate certificate in AI. AI is being embedded rapidly into our cybersecurity programming, into our regular programming courses and our data science programs as well.
Are generative AI tools like ChatGPT being used in your engineering courses?
I mean, it’s been a puzzle in the early days. ChatGPT is just over a year old. It was crazy town for a long time—it still is, in some ways—but I think we’re rapidly evolving. If you look at the hype cycle, generative AI is at the hype of over inflated expectations. Over the next year, it’ll just become a regular tool that we all use. So I’m not alarmist about it at all. I mean, I don’t know if we’re going to hit the singularity or if Skynet’s going to become self-aware in 2024 or not, but that’s where we’re at right now.
The existential concern my programming faculty have right now is with ChatGPT and generating code. How do we now start teaching code? We had that initial state of alarm about what is generative AI going to be able to do for students. I basically guided my faculty to say if you think of this as an arms race, you’re going to lose, because if you go on the internet right now you can see there’s so many different ways to defeat the AI detection systems out there.
So how do we incorporate generative AI into teaching and learning? I don’t think we have it figured out entirely. But I know, for example, one of our faculty members, instead of teaching the syntax and the mechanics of programming, he’s more interested in the structure of programming for efficiency and analyzing a sample code that ChatGPT spit out. So they want to be able to analyze the code, maybe not generate the code so much.
In terms of engineering, I can’t say everyone’s going to become a prompt engineer, but you’re going to have to have prompt engineering skills. I think that’s going to get even better. We still believe that the use of AI is going to move humans up the value chain, but I don’t believe it’s going to be a situation where a rising tide raises all ships. People are going to have to invest in it a little bit and improve their skills.
What other skills do you think are important for engineers to learn?
We’re always trying to instill in our students that when an employer is looking at them, they’re hiring somebody to solve a problem. So we need to keep reminding students that engineering is about finding solutions to problems. One thing that we do is we introduce problem based learning very early in the program. We try to emphasize those critical thinking skills.
As much as possible we try to assign real world projects for them to solve, and then we try to encourage creativity and natural curiosity wherever possible. I think once you get the natural curiosity going they tend to solve the problem, so we try to provide environments for them to be able to do that.
And we have a term here at Seneca: human skills. We’ve heard loud and clear from all parties that in a lot of cases the technical skills are incredibly well developed, but unfortunately those human or soft skills could use a little bit more.
We think that is going to be a competitive advantage for us, so we’ve actually introduced something called the human skills project that will be spreading across all the different programs. So every student will be able to say that they’ve been exposed to communication skills, teamwork skills and conflict resolution. Those are the three big ones there.
How do you ensure that your students are going to be prepared, not just to be able to work when they graduate, but to adapt to all the changes that are in store?
We’re very committed to this. There is an institutional commitment to making sure that we’re staying on top of tech technology trends and practices. One thing that I always do with my faculty is encourage intelligent risk taking. I think our faculty need to be able to take that intelligent risk and we need to support that risk. I’m not saying it’s worked out every time, but that’s how we fail fast and iterate and move on.
So that’s the agile environment that we’ve created here at Seneca. That’s what I think is going to be our biggest advantage. The fact that we have 100% placement rate for co-ops, nearly 100% placement for graduates, and a number of the graduates are now the digital leaders in their various corporations and enterprises leads me to believe that we’re doing the right thing.
