QuantHub
QuantHub
Support
From the MIS Classroom to AI Engineering
For Sam Riddle, the path from the University of Alabama’s Management Information Systems program to developing technology at QuantHub happened alongside one of the biggest shifts the software industry has experienced in decades. After completing his bachelor’s degree and master’s degree in MIS at UA, Riddle joined QuantHub, where he now works as an AI engineer. His role is heavily rooted in software development: building web applications and writing production code that ultimately becomes part of the experience used by learners and educators on QuantHub’s platform.
Riddle’s relationship with QuantHub actually began before graduation. During his senior year, QuantHub contacted a professor Riddle worked for looking for a potential intern. The professor recommended him, and after a successful interview process, Riddle joined the company during his final semester in the MIS program. His internship included work in business, security auditing, and eventually development, giving him an early opportunity to apply what he had learned at UA in a professional environment.
That transition also came at an unusual moment in technology. When Riddle took his first MIS course in spring 2022, learning to program was still an overwhelmingly manual process. Students worked through loops, conditionals, algorithms, software installation, and other programming fundamentals themselves. Then generative AI arrived. Within roughly a year, students went from learning entirely by hand to experimenting with tools that could generate code from a prompt. That timing gave Riddle an unusual perspective: he learned the foundations of programming first and then experienced firsthand how AI could change the way those foundations were applied.
Learning to Code Before Learning to Code With AI
Rather than treating generative AI simply as something students should avoid, Riddle remembers the MIS program being open to responsible experimentation. Students still needed to understand their coursework and demonstrate their own knowledge, but they were also able to explore how AI could enhance the development process. As the technology improved, Riddle began using it more extensively. That experimentation eventually contributed to an AI-native application that he and fellow student Elliot helped build to win a hackathon. For Riddle, the combination proved valuable: the program first taught them how to code and then gave them room to learn how AI could augment those skills.
His capstone project pushed those abilities even further. Riddle’s team developed a prototype inspired by geographic information systems that took satellite imagery and displayed it on a map. The project presented problems that went far beyond simply knowing programming syntax. The students had to grapple with unfamiliar concepts such as satellite imagery formats and map projections while building a complex application. AI became a tool for navigating that unfamiliar territory, helping the team build something they did not initially possess all the specialized knowledge to create.
That experience foreshadowed an important reality of Riddle’s professional work: developers do not need to enter every project already knowing everything. What matters is having enough foundational knowledge to learn, evaluate, troubleshoot, and make decisions as new problems emerge. Today, Riddle has also had opportunities to partner with the MIS program as it considers how to teach students skills that developers are actively applying in the workplace. He sees educational gaps as inevitable—the technology simply changes too quickly for any curriculum to cover everything—but believes schools can shorten those gaps by encouraging exploration while teaching the fundamentals students need to continue learning independently.
What Tomorrow’s Developers Need to Know
For students preparing to enter development, Riddle’s advice is less about mastering one programming language and more about developing durable skills. AI can increasingly generate code in different languages, making syntax alone a less meaningful differentiator. Communication, critical thinking, reasoning, and collaboration, however, remain difficult to replace. Those abilities help developers understand a problem, communicate what needs to be built, assess the quality of an AI-generated solution, and work effectively with the people around them.
Technical practice still matters. Riddle points to habits such as continually building projects and pushing code as useful ways for aspiring developers to demonstrate that they are actively applying their skills. But underneath those technical habits is something even more fundamental: a willingness to learn. Knowledge can become outdated as tools and technologies change. The more important question, in Riddle’s view, is whether someone is willing to continually rebuild and expand that knowledge. The developers positioned to succeed will not necessarily be those who memorized the most at one moment in time, but those who are prepared to keep learning as the profession evolves.
Riddle recommends bringing the same mindset to AI. Developers should experiment with new technology without blindly adopting every new tool. In his own workflow, he thinks about AI as particularly strong at prediction and generation. That allows him to spend less time manually writing every line and more time thinking about what needs to be built and evaluating the result. His process can be summarized as exploration, planning, generation, and review: understand the problem, make a plan, allow AI to help produce the solution, and then put human eyes on the output before considering the work complete.
AI Will Change Development—But Human Judgment Still Matters
The rapid improvement of AI has understandably raised questions about what happens to software development jobs as these systems become more autonomous. Riddle sees the change firsthand. AI has already accelerated his development process and increased his output significantly. Yet greater productivity does not mean he is ready to hand the entire development process over to an autonomous agent. From his experience, AI can generate large amounts of code quickly, but without appropriate human oversight, the quality and direction of that work can suffer.
That distinction is important. A difficult development problem cannot always be solved simply by “throwing AI at it.” Developers need to understand where a tool performs well, where it struggles, what rules or context it needs, and which parts of a workflow are worth automating. Riddle expects AI to continue improving dramatically, but he views it primarily as an accelerator rather than a wholesale replacement for human work. His own job title may change as the industry evolves, but he is not particularly worried about development disappearing. Instead, he expects the profession itself to look different.
One reason is that even as AI’s technical mistakes become less noticeable, Riddle still sees what he describes as “directional” mistakes. A system can produce technically impressive output while misunderstanding what the developer actually wants. Planning, context, prompting, and human review therefore remain essential. The goal is not to keep a person involved in every repetitive task simply for the sake of doing so. It is to maintain the right amount of human involvement so automation saves time without sacrificing the quality or direction of the finished product.
The Skills That Should Never Be Automated Away
Perhaps the most significant lesson Riddle has taken from his time at QuantHub has little to do with code. Looking back on his experience as a full-time developer, he points to interpersonal moments—the Slack messages, code-review comments, conversations, calls, lunches, and team interactions—as some of the most valuable parts of professional development. AI can help automate pieces of those workflows, but completely automating them risks removing the collaboration that allows developers to learn from one another and makes working on a team rewarding in the first place.
That is why Riddle continues emphasizing soft skills when he speaks with students in the MIS program. His experience at QuantHub has reinforced the value of pushing beyond his own comfort zone to talk with coworkers, participate in team activities, and build relationships. Small actions may not seem as significant as learning a new technology or shipping a major feature, but over time they compound. For students preparing for their first development role, success can come from consistently doing those small things well: staying curious, communicating with others, learning continuously, and making deliberate decisions about when and how to use AI.
As AI reshapes development, Riddle’s experience suggests that the future may depend on an interesting combination of increasingly sophisticated technology and fundamentally human skills. Developers will write code differently. Their tools will become faster and more capable, and the knowledge they need will continually evolve. But judgment, curiosity, communication, collaboration, and the willingness to keep learning are unlikely to lose their value. As Riddle puts it, “we’re all just people.” For the next generation of developers, mastering AI will matter—but so will remembering how to do the human parts of the job well.
Prepare Students for the Future of AI-Powered Careers
Sam Riddle’s journey from MIS student to AI engineer demonstrates what’s possible when students develop strong technical foundations alongside the critical thinking, communication, and adaptability needed in a rapidly changing workplace. As AI continues to reshape software development and other data-driven careers, higher education programs have an opportunity to prepare students not just to use emerging technology, but to understand it, evaluate it, and apply it effectively.
QuantHub helps colleges and universities bring these skills into the classroom with curriculum designed for MIS and other higher education programs. Give your students opportunities to build practical AI and data skills while developing the human judgment and problem-solving abilities they’ll need to navigate an evolving workforce.
Explore QuantHub’s curriculum for MIS and higher education programs and discover how you can prepare your students for what’s next.