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University of Alabama instructor Jeff Lucas explains how AI is transforming programming courses, database instruction, capstone projects, employer expectations, and the role QuantHub plays in helping students build future-ready skills. The Same Skills Still Win—But the Learning Environment Has Changed
Artificial intelligence is transforming the technology workforce at an unprecedented pace, challenging universities to rethink how they prepare students for careers in business and technology. Traditional programming, database management, systems analysis, and business analyst roles are rapidly evolving as AI becomes embedded in nearly every workplace tool. Rather than simply adding AI courses to existing degree programs, universities are reimagining how students develop technical expertise, critical thinking, and business problem-solving skills in an AI-first world.
The University of Alabama’s Management Information Systems (MIS) program is embracing that challenge. Through redesigned coursework, AI-integrated learning experiences, and a renewed focus on judgment and problem solving, the program is preparing graduates to thrive in a workplace where artificial intelligence has become a daily collaborator rather than a future possibility.
To explore how the program is adapting, QuantHub Marketing Coordinator Abbie Merker sat down with her former professor, Jeff Lucas. Merker graduated from the University of Alabama’s Management Information Systems (MIS) program in 2021, giving her a unique perspective on how dramatically the curriculum has transformed in just a few short years. Lucas teaches MIS 221 and MIS 321—Business Programming I and II. He also oversees the MIS curriculum at Alabama and serves as the committee chair for AI in the business school’s curriculum as a whole. Their conversation explored how AI is reshaping classroom instruction, employer expectations, student projects, and the role QuantHub plays in helping students develop the skills needed for the future.
University of Alabama MIS Program at a Glance
As artificial intelligence rapidly reshapes the technology workforce, the University of Alabama’s Management Information Systems (MIS) program faced a clear challenge: prepare students for an AI-driven future without sacrificing the programming, business, and critical thinking skills that have long defined a successful MIS education.
To meet that challenge, the department redesigned core coursework to integrate AI throughout the curriculum while partnering with QuantHub to deliver hands-on learning experiences that reinforce AI concepts, programming, data literacy, and business analytics through interactive, continuously updated content.
Key Responses To the AI Shift
- AI integrated across six core MIS courses: MIS 200, MIS 221, MIS 321, MIS 330, MIS 421, and MIS 440.
- Programming, database, and systems analysis courses redesigned to reflect modern AI-assisted workflows.
- Greater emphasis on evaluating and improving AI-generated code, requirements, and business solutions.
- Curriculum aligned with employer demand for graduates who combine business expertise with AI and data skills.
- Interactive QuantHub learning experiences provide immediate feedback while keeping course content aligned with rapidly evolving AI technologies.
Teaching Students to Think While AI Is Already in the Room
Despite the technological disruption, Lucas believes the purpose of an MIS education has remained remarkably consistent. Borrowing a quote from legendary Alabama football coach Bear Bryant, he said, “The same things win that have always won. We just have different excuses when we lose.”
For Lucas, those winning qualities are creative problem solving, communication, critical thinking, and the ability to explain complex ideas in a way others can understand. “Those are the same skills that will win tomorrow that won yesterday,” he explained. What has changed isn’t the destination—it’s the path students take to get there. Faculty must now find ways to teach those timeless skills while students are actively working alongside AI.
That challenge begins in MIS 221. Years ago, Lucas could place students in front of a blinking cursor on a blank screen and require them to decompose problems one algorithm at a time. The process wasn’t glamorous, but it forced students to think critically before writing code. “I can’t replicate that any longer,” Lucas said. “AI is built into every tool.”
The Specifics
Modern development environments have fundamentally changed how students learn to code. In Visual Studio Code, students may begin typing the word “while,” and instead of simply autocompleting the syntax, the editor can generate the entire loop. Drop a CSV file into the project, and the software may recognize the file, infer the student’s intent, and recommend all of the code necessary to read that data into the application with a single press of the Tab key.
“Students don’t even recognize it as AI a lot of times because it’s just built into the tools,” Lucas explained. “They’re literally just using VS Code.” Preventing students from using those capabilities would require instructors to take what he described as drastic measures, such as requiring students to code in a simple text editor. “You have to go out of your way not to use the tools because they’re just baked into everything.”
Rather than fighting that reality, the University of Alabama teaches students how to think critically while using AI responsibly. “We still make the first class an algorithms class,” Lucas said. Students spend time working on paper and completing exercises outside of coding environments before transitioning into AI-assisted development. After that foundation is established, however, the expectation changes. “You can no longer teach them to do the thing and then evaluate the AI’s output of the thing,” he said. “You have to teach them to evaluate AI’s output of the thing while they’re learning the topic.”
Reimagining the MIS Curriculum
That philosophy extends well beyond introductory programming. Rather than simply adding AI lessons to existing courses, the department has redesigned multiple classes to reflect how technology professionals actually work today.
Lucas explained that employers no longer need graduates who can simply produce code or write SQL from scratch. They increasingly need professionals who understand business problems, know how AI systems work, and can evaluate, improve, and validate AI-generated solutions. That shift has transformed nearly every core course within the MIS program.
How AI Has Reshaped the MIS Curriculum
- Business Programming I: Students still learn algorithms and programming fundamentals, but they develop those skills while learning to evaluate AI-generated code and build critical reasoning alongside AI tools.
- Business Programming II: Redesigned into an AI-embedded applications course focused on retrieval-augmented generation (RAG), prompt engineering, token management, and building applications that responsibly integrate AI.
- Database Management: Expanded from primarily teaching SQL into understanding the complete lifecycle of data—from collection and cleansing to storage, retrieval, visualization, and decision-making.
- Systems Analysis & Design: Shifted away from writing exhaustive software requirements toward AI governance, validation, monitoring, guardrails, and responsible data sourcing.
- Enterprise Architecture: Now emphasizes cloud systems, Azure, CI/CD pipelines, and modern software architecture practices.
- Data Engineering: Introduces students to Databricks and modern data engineering concepts such as bronze, silver, and gold medallion data architectures.
Those changes reflect just how dramatically AI has accelerated software development. Discussing MIS 321, Lucas laughed as he compared today’s classroom with only a few years ago. “Your entire group project that you spent an entire semester on when you went through school—we can do it in half a lecture now.”
The database course has experienced an equally dramatic transformation. Students still learn SQL, entity relationship diagrams, and data modeling, but those topics no longer consume an entire semester. Instead, students spend much more time understanding how organizations collect, cleanse, store, retrieve, visualize, and ultimately use data to make business decisions. The emphasis has shifted from simply producing code toward understanding the complete data lifecycle—and increasingly, evaluating AI-generated work rather than creating every query manually.
Systems Analysis and Design has evolved just as significantly. Requirements once focused on highly detailed specifications—what a button should do, where it should appear on a page, and what color it should be. Today, prototypes can be generated so quickly that those detailed documents often become obsolete before they’re finished. Instead, students spend more time thinking about where AI systems obtain data, what guardrails should exist around those systems, how outputs should be validated, and how applications should be monitored over time.
Reinventing Projects for an AI-First Workforce
Perhaps nowhere has AI changed the curriculum more dramatically than in student projects. “I would say AI has fundamentally broken the capstone,” Lucas said. Projects that once required an entire semester can now be generated in less than a lecture using AI-assisted development. At first, faculty responded by assigning larger projects with more features, but they quickly realized AI could produce additional functionality just as easily.
Instead of rewarding students for building bigger applications, the program shifted its focus toward deeper learning. Students now complete a manageable project through repeated cycles of feedback from instructors and corporate partners. They continually refine user experiences, improve underlying code, and strengthen AI-generated solutions. “We’re going to keep getting feedback and keep getting feedback,” Lucas explained, “and we’re going to find ways to make it better every time.”
That change reflects what employers are asking for. Rather than hiring graduates to complete routine first-level work, organizations increasingly expect new employees to evaluate AI-generated requirements, review code, and apply sound judgment. “We no longer need people to do that first level of work in most cases,” Lucas said. “The AI can do that.” Universities are now challenged to prepare graduates who can contribute more like professionals with several years of experience by teaching students not simply to produce work, but to critique and improve it.
Preparing Graduates for Jobs That Are Being Redefined
The department’s curriculum isn’t changing in isolation. Over the past year and a half, Lucas and his colleagues have met extensively with employer partners to understand how AI is changing workforce expectations. More recently, those conversations have expanded across the Culverhouse College of Business through listening sessions with leaders in finance, accounting, marketing, and other disciplines.
The message has been remarkably consistent. Companies aren’t simply searching for AI experts. They’re looking for professionals who combine strong business expertise with AI and data skills. “What they want are people that have great domain knowledge and these data and AI skills to go along with it,” Lucas explained. “That’s the winning combination.”
MIS has long occupied the space between business and technology, and Lucas believes that balance is becoming more valuable than ever. Businesses no longer have the luxury of passing requirements from analysts to developers and waiting weeks for revisions. Development cycles are dramatically shorter, requiring technical professionals to understand business strategy and business professionals to understand technology.
What Employer Partners Are Looking For
Industry listening sessions revealed several major shifts in employer expectations:
- Business professionals who combine strong domain expertise with AI and data skills.
- Graduates who can evaluate, critique, and improve AI-generated work, not simply create first drafts themselves.
- A deeper understanding of prompt engineering, context, token management, and responsible AI use rather than surface-level prompting.
- Professionals who can move comfortably between business strategy and technical implementation.
- Strong communication, judgment, and critical thinking skills that help validate AI outputs before they reach customers.
- Graduates capable of contributing more like professionals with three to five years of experience as traditional junior-level responsibilities continue disappearing.
Lucas sees this transformation playing out in real-world workflows every day. A meeting transcript can now be dropped into one AI tool to generate software requirements. Those requirements can then be entered into another tool that produces working code. “It’s not perfect,” he said, “but man, that’s a whole lot faster way to get feedback.” That speed fundamentally changes the role of business analysts and developers alike. Rather than spending days documenting requirements before seeing a prototype, teams can review working software almost immediately and focus their energy on improving, validating, and refining AI-generated work.
QuantHub’s Role in Redesigning Curriculum Towards these Shifts
Supporting that transformation requires learning resources that evolve as quickly as AI itself. Lucas said QuantHub has become an important part of the MIS curriculum across MIS 200, MIS 221, MIS 321, MIS 330, MIS 421, and MIS 440 by helping faculty build interactive learning experiences that reinforce critical reasoning, data literacy, and AI concepts.
He believes one of QuantHub’s greatest advantages is its ability to provide students with immediate feedback while keeping content current. “The life cycle of a textbook is just way too long to be of any use right now,” Lucas said. Because AI technologies evolve so rapidly, students benefit from learning materials that can be updated in real time rather than years after industry practices have changed.
Lucas also believes higher education currently has a unique opportunity. After more than three decades spanning industry and academia, he remarked, “This is probably the first time in my career that I feel, in some ways, academia is ahead of industry.” Universities can experiment with emerging AI tools more freely than many businesses constrained by production systems, customer data, and privacy requirements, allowing faculty to help shape best practices rather than simply reacting to them.
Looking Ahead
The conversation made one thing clear: the University of Alabama’s MIS program isn’t simply adding AI to existing courses—it is rethinking how future technology professionals are educated. While the tools continue to evolve, the program’s mission remains the same: develop graduates who think critically, communicate effectively, and solve complex business problems.
For Merker, the interview offered a unique perspective as a recent alumna returning to speak with her former professor. In just a few years, she saw how dramatically the curriculum had evolved to reflect the realities of an AI-first workplace. Through continuous innovation, close collaboration with industry, and partnerships with organizations like QuantHub, the University of Alabama is preparing students not just to use AI, but to evaluate it, improve it, and lead alongside it throughout their careers.
“Technology will continue to evolve, but our responsibility is preparing students to adapt with it.”
–Jeff Lucas
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