The AI Generation Still Needs Human Mentors

Dr. Kai Dupe • July 1, 2026

Artificial intelligence has become the newest member of nearly every software development team.

Artificial intelligence has become the newest member of nearly every software development team. It can generate code, explain algorithms, summarize documentation, and even help debug applications in seconds. For today's students, AI is becoming as common as the calculator once was for mathematics.


Yet there is something AI cannot replace.


It cannot be the mentor who sees potential in a student before that student sees it in themselves.


Throughout my career—as a software developer, technical trainer, and now professor of computer science—I have watched technology evolve at an astonishing pace. Languages have come and gone. Frameworks have risen and fallen. Entire industries have transformed.


But one thing has remained remarkably constant: people change people's lives.


Many of us can point to a teacher, manager, colleague, or mentor who opened a door we did not know existed. They challenged us, encouraged us, and sometimes simply believed in us during moments when we doubted ourselves.


No AI model can replicate that relationship.


In my classroom, I encourage students to use AI responsibly. I want them to learn how to ask thoughtful questions, evaluate generated code critically, and use these tools to accelerate learning rather than avoid it. AI can make someone more productive, but productivity alone does not create great software engineers.


Great engineers develop curiosity.


They learn resilience when debugging problems that seem impossible.


They practice empathy when designing software for real people.


They communicate ideas clearly.


They collaborate with teammates who think differently.


These are deeply human skills.


Ironically, the more capable AI becomes, the more valuable these uniquely human qualities will be. Organizations are no longer hiring people simply because they know a programming language. They are looking for professionals who can solve problems, exercise judgment, communicate effectively, and continue learning throughout their careers.


This is where mentorship matters more than ever.


Whether you are an experienced engineer, a professor, or a student just beginning your journey, consider investing in someone else's growth. Answer a question. Review a résumé. Encourage an intern. Speak to a classroom. Share your failures as openly as your successes.


Technology changes quickly.


Character develops slowly.


And while AI may help write the next function, it is still people who inspire the next generation of innovators.


As we embrace the future of artificial intelligence, let's remember that our greatest contribution may never be the code we write—but the people we help become confident enough to write their own.

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Stepping onto the campus of Morehouse College this past weekend for Admitted Students Day was more than a visit—it was a moment of reflection. As I watched young Black men walk with purpose across the yard, I found myself asking a simple but profound question: What would it have been like for me to study computer science here? My journey into computing was shaped in environments where I was often the only Black man in the room. That reality brings with it an unspoken weight—the need to prove you belong, the awareness of being watched, and sometimes, the quiet isolation that comes with underrepresentation. Standing at Morehouse, I realized that this burden is not a given. It is a condition of the environment. At Morehouse, the environment is different by design. Here, Black men are not anomalies—they are the standard. I imagined what it would feel like to learn algorithms, data structures, and software development in a space where my identity was not questioned but affirmed. Where excellence is expected, not in spite of who you are, but because of it. As a computer science professor, I understand the academic rigor required to succeed in this field. There is no shortcut through recursion, no bypass around debugging, no substitute for disciplined problem-solving. But what struck me during my visit is how much context matters. When students are free from the psychological burden of proving they belong, they can redirect that energy toward mastering the material. They can collaborate more openly, ask questions more freely, and take intellectual risks without fear. I also thought about legacy. At Morehouse, students walk the same grounds as Martin Luther King Jr.. That kind of history does something to a person. It raises the bar—not just academically, but personally. It invites students to see their education not just as a pathway to a career, but as preparation for impact. Leaving campus, I felt inspired—but also reflective. I cannot rewrite my journey, but I can appreciate what spaces like Morehouse offer the next generation. For a Black male pursuing computer science, it is more than a degree. It is an opportunity to develop skill, confidence, and identity in alignment. And that combination is powerful.
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If you walk into most computer science classrooms today, you might assume that computing has always been a male-dominated field. As someone who has spent decades in the industry and now teaches the next generation of developers, I can tell you—that assumption is not only common, it’s historically inaccurate. In the early days of computing, many of the first programmers were women. Ada Lovelace is widely recognized as the first computer programmer, having written what we would now call an algorithm for Charles Babbage’s Analytical Engine. Fast forward to the 1940s, and women were programming some of the first electronic computers, including ENIAC. These were not peripheral roles. These women were solving complex computational problems, often inventing programming techniques as they went (Abbate, 2012). So what happened? From a systems perspective, the answer is not mysterious—it’s structural. In its early stages, programming was considered clerical work. It required precision, patience, and attention to detail—qualities that, at the time, were socially assigned to women. But as computing became more central to business, government, and innovation, its status changed. What was once seen as routine work became prestigious and lucrative. And when that shift happened, the demographics shifted with it. By the 1980s, we see a clear inflection point. Personal computers entered the home—but they were marketed primarily to boys. This created an early access gap that translated into confidence, experience, and eventually career pathways. At the same time, hiring practices and workplace cultures began to favor men, reinforcing a feedback loop that pushed women out of the field (Hicks, 2017). Over time, the narrative changed. Computing was no longer something women had built—it became something they were seen as entering late. But that narrative is not just incomplete—it’s a distortion. Understanding this history is not about nostalgia; it’s about accuracy. When students learn that women were foundational to computing, it reshapes how they think about the field. Diversity is no longer framed as a modern intervention—it is recognized as part of computing’s original DNA. In my classroom, I’ve seen what happens when students encounter this truth. It disrupts assumptions. It broadens participation. And perhaps most importantly, it changes who students believe belongs in this space. So, if women were the original programmers, what happened? Part of the answer lies in systems—education, marketing, hiring, and culture. But another part lies in storytelling. The stories we tell about computing shape who feels invited to participate in it. As educators, technologists, and leaders, we have an opportunity—and a responsibility—to tell that story more accurately. References Abbate, J. (2012). Recoding Gender: Women’s Changing Participation in Computing. MIT Press. Hicks, M. (2017). Programmed Inequality: How Britain Discarded Women Technologists and Lost Its Edge in Computing. MIT Press. Evans, C. L. (2018). Broad Band: The Untold Story of the Women Who Made the Internet. Portfolio. Shetterly, M. L. (2016). Hidden Figures. HarperCollins.