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Live Q&A: What's Happening with AI in Tech Bootcamps in 2023
Written By Liz Eggleston
Edited By Jess Feldman
Last updated on September 13, 2023
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Written By Liz Eggleston
Edited By Jess Feldman
Course Report strives to create the most trust-worthy content about coding bootcamps. Read more about Course Report’s Editorial Policy and How We Make Money.
Course Report strives to create the most trust-worthy content about coding bootcamps. Read more about Course Report’s Editorial Policy and How We Make Money.
The evolution of artificial intelligence or AI is quickly shaping how Software Engineers, Data Scientists, Cybersecurity Analysts, and UX / UI Product Designers do their jobs. So what’s happening with AI in tech bootcamps in 2023? They’re adapting! From hosting free workshops and events at General Assembly to launching AI enhancements to all of Flatiron School’s bootcamp curriculum, coding bootcamps are already working AI into their schools. We invited tech experts from Springboard, General Assembly, Flatiron School, Code Fellows, and Tech Elevator to share how AI is shaping the tech job scene.
The term “AI” might be in the headlines, but what are the actual skills you need to work with AI?
John: The skills to work with and use AI are different from the skills to build AI. The benefit of AI is that it’s an assistance tool for developers, like a great sidekick or copilot. Chatbots are a better Google — it’s ingested the entire internet and made it easy to work with it instead of fighting through it.
Skills to work with AI include learning how to query the machine to get the information you need and asking follow up questions that will narrow in deeper (becoming a better interviewer). AI has enhanced people's ability to think critically and analyze what they're seeing, when AI generates generic code. Plus, AI is making coders better in terms of efficiency, speed, and saving money for companies because they're getting assistance in the mundane so they can focus on the bigger picture.
Matt: When it comes to actually building the AI and thinking about learning AI, there are different brandings of very similar concepts and ideas, such as data science, machine learning, or statistics. You can think of AI as a really wide field. Folks who embark on a bootcamp in data science or who are taking a statistics course may not learn all of the skills needed to build out their own generative AI chatbot, but they are learning AI and will probably get some exposure to building some of those individual pieces.
For those looking for a job as a developer interested in AI, what area should they invest in to be best positioned in the job market?
Josh: If you're entering the market to become a developer it’s important to consider first if you have the foundational understanding of programming to begin leveraging these tools. If you're entering a bootcamp to learn Java, Python, or .NET, emphasize learning those fundamental concepts. You need to be able to work independently of these tools so you can understand what it's going to share back with you or suggest that you do. Try and extract yourself from any type of dependency on those tools.
As you're thinking about where can you begin to leverage AI to improve your confidence or competency as a developer there's two categories:
If you don’t know how to engage with AI tools it will come off as something that you're either scared of or that you’re not sure how to wield something so powerful. There will be skills that you'll have to develop to work with it, but starting first with trying to find ways to learn how to interact with and leverage AI to perform various technical related tasks will help you start to see some of those possibilities and then give you more reason to reach for them when you encounter a more difficult problem.
Do you foresee AI being used more as a tool to boost productivity instead of one that many fear will take away jobs?
Matt: Generative AI is taking the world by storm! Like many aspects of tech, there was a time before we knew how to use it (think of early search engines like Google, Bing, and Ask Jeeves) where we had to learn how to use it more efficiently to get the desired answers. When you look at most job descriptions today they won’t include a prerequisite of knowing how to Google, yet it’s a skill that many jobs rely on!
I can understand the fear of losing your job or being automated out of work, but it’s probably going to be more of a reallocation or a shuffling of people, than a firing. We will probably see that certain industries or jobs that may not be as necessary but that doesn't mean people will be out of work permanently — there will just be new jobs created in their place. For example, before the invention of the airplane there wasn’t a need for airports and all of the staffing involved in its operation, but because of its invention there was a new need for jobs, even if there were fewer employees needed at train stations as a result. It is so critically important to understand how to think, communicate, read, and write — there are so many jobs that you will probably have over your career that might not exist yet! Being able to think critically will prepare you for any evolution in employment.
I think we're going to see people leverage AI to boost their productivity. There are ways to incorporate creativity and thought to leverage AI in interesting ways to harness its power to make life better and more productive.
How has the recent job market changed for software engineers that want to transition into the AI space? Are the only job opportunities right now in AI?
Kara: There are tons of job opportunities in AI! Data continues to support its future across any category, from Big Data to machine learning to IT. The World Economic Forum said there might be 100 million new jobs over the next two to four years! Remember that AI is a really big umbrella — it's a vast field.
Contrary to common belief, AI is not limited to certain technical roles. There's a bigger window around roles like AI Business Analysts or Conversation Designers, if you're thinking about UI/UX. As AI intersects into different fields, it’s creating non-technical roles that are perfect for mid-level professionals curious about changing careers in tech. As industries adopt tools, skills that come with that capability or role will adjust. Tasks may change in roles and develop new roles. There's going to be a major need for AI Engineers, Data Scientists and AI Ethicists, similar to what happened in the 90s when new privacy jobs were created. Attorneys will also have opportunities to evolve their roles! For anyone worried about the future of software engineering, it's still projected to grow 30-40% so there's still tons of tech jobs available.
What AI job roles are students landing after graduating?
Giovanni: At Flatiron School, we regularly place people as Data Analysts, Business Intelligence Analysts, Data Scientists, AI Engineers, and ML Engineers. Someone recently got placed as a Conversation Engineer but it’s less common as a title and more so a set of skills.
Remember: AI is an ocean. What people are talking about now is Gen AI, which is an emerging technology. It will be difficult to know exactly what those roles will be until companies and hiring partners figure out how to incorporate those into business solutions. Broadly speaking, I agree that these tools will be the next Google of the internet where everyone has access to it, but a lot of this will soon be integrated directly into the products that everybody's using, whether they're practitioners or the general public.
Most of the skills for our non-data science students at Flatiron School revolve around interfacing with the existing set of tools and understanding what solutions they work for and what they don't work for. Everybody thinks the GenAI is going to solve all of the problems but it's not! I think the buzz will quiet down as companies realize what it doesn't work for. Instead, jobs will spring up around NLP large language models in the actual business cases in which they make the most sense.
All the foundational skills for data scientists whether it's data engineering or ML engineering those are still powerful toolkits for anyone in this field to have, so if you want to be a software engineer learning at least the basics of what these tools can do for you, what they can't do for you, and what are easily accessible.
How exactly are bootcamps incorporating AI into their programs?
John: At Code Fellows, we are a dual thread bootcamp so we teach software engineering and cyber security. We’re trying to help our students use AI as a co-pilot and focus on how to be efficient with AI. In most cases, AI saves time typing. Writing tests is a difficult process for many students, which is where generative AI can be very helpful. They can present a chunk of code, ask what test to write, and AI will generate an answer. We’re teaching students how to be productive with AI so that when it becomes integrated in the near future, they’re ready to go.
For cybersecurity, we're doing Red Team/Blue Team interactions with AI fighting against us and we're able to use AI tools to hone our skills on the cyber side. We used to have to depend on another human’s skills to test and sharpen our own, but AI is like gold sharpening iron! We want our students to use the tools like professionals so it’s second nature to them. Then we can start thinking about how to incorporate it into apps and code to make people’s lives better.
Kara: At Springboard, we’re incorporating AI into the overall experience of each bootcamp track (cybersecurity, coding, design, and tech sales) in order to ensure a base equity with AI literacy. We’ve incorporated that into learning objectives, and apply that to the projects and assignments so students can start building up that muscle and use case and application.
Part of our overall bootcamp commitment is job placement and we've boosted literacy around that as well. Students learn how to use AI tools to figure out how to amp up cover letters, writing, and content generation, and those things that are outside of the skill set that you developed in bootcamp. Starting to build that has been really powerful for us and we’re seeing a hungry appetite — we launched a webinar last week to talk about how to supercharge your job search with AI and we had almost a thousand people register! We’re incorporating AI on the learning side as well as the job placement/services and career outcome side. We're also looking at how we use AI in our own product and technology to help students more.
Matt: General Assembly has new product offerings under development. We are integrating Gen AI as well as AI more broadly into all of our existing product offerings, both on the tech side (the Python programming, data science, software engineering) and those roles that are tech adjacent, like user experience design and product management. Everybody will get some sort of base fundamental understanding of what generative AI is and how it can best be used in their day-to-day work. On the student side, folks get more practice using Gen AI on a day-to-day basis just like they will in the actual job setting.
Giovanni: At Flatiron School, our data science program covers topics like scientific computing, data engineering, and machine learning. We're integrating a lot of the emerging technology into our existing program. There's no horizon for a long-form exclusively-AI program, but we feel strongly that we cover the bulk of it in the back half of our data science program. We've also developed a handful of small form programs in the short term so people have something they can access immediately.
In terms of the student experience and how we’re integrating AI into it, we have an ethical statement around how to use AI and literacy around what it's good for and not good for.
Each of our programs have been updated to include Gen AI and AI topics. For instance, the projects in our product design program now have multiple steps very similar to our normal production environment, so we've identified key areas that make sense for them.
As a company, we're interrogating the entire student journey, focusing on finding out what areas of the student experience make the most sense to add AI. We prototyped a lot of internal products and just launched a beta AI tutor called Ada that we've integrated into our LMS! Our support system now includes three pillars (instructional staff, after-hours tech coaching, and this AI tutor, Ada) to offer extra hours of support for people working independently. We’re testing the efficacy of different solutions right now.
Josh: Tech Elevator is taking a similar approach. One way software developers use AI on the job is when they have to learn new frameworks or languages and they'll use it to help them adapt, and there are ways to weave things like that into the curriculum when students learn their second or third language. They start by learning Java at Tech Elevator and later learn JavaScript. By then there's a strong foundation already developed and there's opportunities to take what can help you translate from one language to the next, or to help you critique code that you might have felt comfortable writing in one language, and provide some suggestions or feedback. We're using that as a way to help them become more comfortable using that tool so that they'll realize the value that will serve them in their professional career.
What does the AI Engineer’s career path actually look like? Can someone learn what they need to know to start an AI engineering career in a three-month bootcamp?
Matt: The brief answer is: yes, you can learn enough in a three-month bootcamp to be able to move into a new job that has some component of AI engineering in it, if not an AI engineering role. It makes sense for us to recognize that there's so many different things that include AI engineers and the job titles and responsibilities mean different things at every company. AI engineering might mean business analysts, data analysts, data engineers, machine learning engineers, data scientists, software engineers… there's so many different things that can go under this massive umbrella of things!
There's a couple of different pieces to that that I want to call out:
Whatever your background and whatever time in life you’re embarking on this career change, tech bootcamps have resources like career coaches and outcomes professionals that can help you leverage your past experience to tailor your new career to where you want to go. I won’t deny there is ageism and a flock of other -isms in tech, but if you know what you want to do, there are people to help you get there. A bootcamp won’t take you from no technical background to Chief Data Officer in three months, but there are so many things that you are going to be able to do with that intensive 12-week bootcamp that can help you get to where you want to go without sacrificing the background of experience you have.
What should students look for in an AI/Machine Learning curriculum and/or instruction when considering a bootcamp?
Giovanni: For a good machine learning curriculum, I would expect a strong foundation in general science, clear description of the machine learning covered, career trajectory, and what kinds of support systems they have in place.
From an instructional standpoint you should ask about their approach to instruction: Do they scaffold topics well? Do they provide projects that make sense for the real world and that employers are looking for? Do you have adequate facetime with the instructor? Do they have support systems that make sense?
If AI can replace some skill sets, will you continue teaching those skills or replace those skills with AI-related skills?
Josh: The skills we teach will remain relevant:
John, how do you think about this as a curriculum developer?
When you design a curriculum for AI, it’s important to reiterate the human element. You cannot just rely on this thing to be magical for you. As a curriculum developer, I know students can cheat. Ultimately, you’ve got to be a better human. Cheating will get you the right answer but it won’t teach you how to use it consistently in a job.
We’re teaching more hardcore computational thinking, going deeper in data structures and algorithms so that students can not only assess what they see but also become the problem-solvers. AI looks like it solves problems but it doesn't. It's just giving you regurgitated answers in a way you can understand. Humans still solve the problems — I want to free our students to solve the problems. I'm more interested in giving them an assignment that seems really hard and seeing how they would break that assignment down into steps. If AI solves a step for you, that's actually really good, but I want to see you as a person break down the steps. Those are the things that we strive for in making students become better thinkers.
Kara, how is AI being used to improve student outcomes?
We're typically serving adult learners who are busy and juggling a lot and we see so much opportunity with looking at how we can create more conversational elements. We already have mentors, coaches, and human conversation going on, and of course online peer learning, but at its core we’re wondering how we can look at creating more real-time feedback. People are studying at very odd hours and getting stuck in different places, so having that opportunity to engage really matters. We also want to ensure we're meeting every student where they are and building AI-driven capabilities in our digital learning experience to give them that real-time feedback. I've been in edtech about 25 years but personalization has always been hard to figure out how to pull off and there's just so much more promise now. I think about the idea of how you can get down to really profiling through the algorithms and work in education and being able to recommend like hey you're you're stuck here and take a look at this and really be able to have that engagement at a whole new level! Companies are productizing and we want to leverage that more. We want to personalize much more and make recommendations that make the students feel more confident and supported and get to the outcomes they're looking at.
If you had a crystal ball, what do you think AI will look like in 5 years time, and what impact will it have for the public?
Giovanni: Most people don't realize that AI is already integrated into many of the things they're using and the tools that have emerged more recently have brought all that to the surface. In five years, most companies will find solutions to create that more conversational personalized experience that Kara was describing not only in education but across the board. That might be through personalized text generation, chatbots, or any number of things which will depend on the sector or company. Basically, it will become another part of the background just like it is now but you'll have that conversational and direct interaction with it.
John: Five years from now your life is just going to get better and you're not going to know why — that's where AI’s taking you! Things that used to be a drag won’t be anymore. Airport lines will be shorter because the cameras got better at seeing what was in your bag. Cars won’t have steering wheels because they’ll be self-driving. I can’t wait to turn 60 and kick back and see what y’all are doing. In five years, we will undoubtedly need a lot of engineers and folks to guide that technology and define that product.
This article was produced by the Course Report team in partnership with General Assembly, Springboard, Flatiron School, Code Fellows, and Tech Elevator.
Liz Eggleston is co-founder of Course Report, the most complete resource for students choosing a coding bootcamp. Liz has dedicated her career to empowering passionate career changers to break into tech, providing valuable insights and guidance in the rapidly evolving field of tech education. At Course Report, Liz has built a trusted platform that helps thousands of students navigate the complex landscape of coding bootcamps.
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