July 8, 2026
When Bruce McLaren (CS ’84G, ISP ’99) fell in love with artificial intelligence (AI), the field was little more than a bold promise. Despite concerns about an 'AI winter,' and a few career pivots later, he never once considered walking away.
McLaren, now a professor at the Carnegie Mellon University (CMU) Human-Computer Interaction Institute, has a passion for AI that began in a college classroom in the late 1970s, where he wrote a term paper on it for a course on the social consequences of computing.
“Reading the early AI literature sparked a fascination that has stayed with me ever since,” McLaren recalled. “At the time, AI felt almost like science fiction, a bold vision of what computers might someday be able to do.” That early experience helped set the course of his career.
The more McLaren worked in AI, the more he found himself drawn not just to building the technology, but to questioning it. Should AI emulate human thinking, or simply accomplish tasks that would be considered intelligent, regardless of how?
In 1990, juggling a full-time job and a growing family, McLaren enrolled part-time at the University of Pittsburgh. He completed a master’s degree in Intelligent Systems in 1994 and a PhD in Intelligent Systems in 1999, after previously earning a master’s degree in Computer Science, and described the program as transformative.
“The ISP program exposed me to multiple perspectives on intelligence, from computer science and cognitive science to philosophy and psychology. That interdisciplinary view has shaped my entire career,” said McLaren.
What researchers dubbed as the “AI winter” in the late 1980s, was a period of dashed expectations and defunding when the technology fell far short of its early promise.
“My enthusiasm for the field never diminished,” McLaren said simply. “I remained convinced that AI would eventually fulfill much of its promise, and I wanted to spend my career helping make that happen.”
By that point, McLaren had spent years working on AI-driven factory scheduling systems at CMU before moving to Carnegie Group, a CMU spin-off developing expert systems to solve real-world problems like diagnosing industrial machinery and translating documents between languages.
McLaren grew up in a home where knowledge was revered, his mother a teacher, his father a minister. Today, that upbringing still drives him in his work at CMU.
His work over the past two decades has extended across intelligent tutoring systems, digital learning games, and now cutting-edge applications of large language models in the classroom. Over the past two decades, McLaren has become an internationally recognized researcher in Artificial Intelligence in Education, studying how AI can support learning, reflection, and problem solving. In that time, McLaren has published over 225 papers and served as President of the International Society for Artificial Intelligence in Education. But what he is most enthusiastic about right now isn’t AI that gives students answers, it’s AI that makes them think deeper.
One project that excites him involves using large language models to engage students in Socratic dialogue after they have solved a problem. Rather than simply confirming the accuracy of an answer, the system prompts students to explain their reasoning, confront their own misconceptions, and arrive at a deeper understanding of the material.
“I find this especially exciting because it combines decades of research on intelligent tutoring systems with the new capabilities of generative AI,” McLaren said.
As for McLaren’s connections to SCI, he speaks warmly about mentors like Kevin Ashley, his PhD advisor, who taught him to go deep rather than broad, to conduct careful studies, and to write introductions that pull readers in.
“I emulated Kevin's approach with my PhD students and would always think back fondly to Kevin's mentorship in doing so.” McLaren spoke with equal warmth about collaborators and mentors at both Pitt and CMU. He recalls attending lectures by pioneering AI figures such as Herb Simon and Allen Newell and later collaborating with leading researchers including Kevin Ashley, Ken Koedinger, Vincent Aleven, Sandy Katz, Erin Walker, and others.
“The transition away from being a student felt less like leaving a community and more like growing into a different role within it,” he reflected. Now, McLaren is mentoring the next generation of AI researchers himself. He credits much of his success to the uniquely collaborative AI ecosystem shared by Carnegie Mellon and the University of Pittsburgh.
As someone who has watched the AI field evolve, the recent explosion of generative AI has surprised even him. Natural language processing, he recalls, seemed for a long time to be “limping along.” Then, suddenly, it wasn’t.
“GenAI has truly taken AI to another level,” McLaren said. But he’s quick to note that the foundations built over decades, knowledge representation, expert systems, search algorithms, are still the foundation beneath it all.
And about the fears? The job losses, the disruption, the existential hand-wringing? He’s measured, not dismissive.
“I don't believe [AI] will lead to the catastrophic decline in jobs and work that the popular media is mostly portraying,” McLaren said. Instead, he sees the real challenge ahead as integration: figuring out how to combine the remarkable fluency of generative AI with the rigor, transparency, and reliability that earlier AI traditions built so carefully.
McLaren’s advice to the next generation of researchers captures something essential about who he is: “Stay curious. Build strong technical skills, but also learn how to communicate, collaborate, and think critically about the societal implications of the technology you create. AI is ultimately a human endeavor.”
The advice reflects a career defined by intellectual curiosity, interdisciplinary thinking, and a lifelong belief in the promise of artificial intelligence.