The Role of Faculty in the University of the Future

Ask almost any adult to list the most influential people in their lives and a teacher always appears near the top—often more than one. Ask them about those teachers, and they rarely describe their teaching in terms of lecturing or helping students master a body of knowledge. Instead, they talk about how those teachers took an interest in them as young human beings, how they gave them their time and attention and demanded more of them while supporting their efforts to meet those demands. And usually, they describe how those teachers helped them dream bigger dreams for themselves. In short, they made their students feel like they mattered.

"Mattering," a term central to social psychologist Greg Elliott's work, refers to the sense that one is a significant part of the lives of other people, institutions, or society.^Footnote1 Elliott argues that it is a core existential human need. When we feel we do not matter, humans will find ways, even deeply destructive ways, to assert themselves. Donna Beegle, who writes about people who escape extreme poverty, finds that people rarely cite a program or policy but rather another person who made them feel like they mattered and that they had a better future ahead of them.^Footnote2 Research on developmental resilience by scholars like Emmy Werner and Michael Rutter confirms this: the presence of just one caring adult often predicts whether a young person can thrive despite hardship.^Footnote3 One cannot truly transform a life if that person does not feel like they matter to you.

Artificial intelligence (AI) makes it possible to bring that principle back to the center of higher education. Today, the work of knowledge transfer is often done better, faster, with more precision, and more patiently by AI. These systems can provide nonjudgmental, individualized learning opportunities twenty-four hours a day, seven days a week. Think of AI as a "genius teaching assistant" who assumes much of the work of basic knowledge transfer, unlocking learning when students get stuck and providing real-time assessment. Such a genius TA would offer faculty dashboards that update student progress, flag those who are struggling, and recommend targeted interventions. These tasks free faculty to focus on building genuine relationships with students, using the classroom to foster human skills, and curating community. This may be the great gift of AI to education. But it requires a profound reimagining of faculty roles—perhaps the single biggest hurdle to reimagining higher education, and equally its greatest opportunity.

This is not entirely uncharted territory. The flipped classroom model, popular since around 2010, pioneered by educators such as Harvard physicist Eric Mazur, already moves the delivery of content outside the classroom, transforming class time into a forum for higher-order cognitive work, collaborative problem-solving, Socratic debate, and real-time coaching.^Footnote4 AI makes the flipped model dramatically more powerful. A genius TA can generate robust personalized learning plans and a granular analysis on each student's progress. Most importantly, class time can become fully human time, with laptops closed and technology present only as the instructor chooses, and classrooms can become spaces reserved for being fully present with one another.

Consider a computer science professor whose genius TA pinpointed the exact moment a student's logical model breaks down—a persistent error in understanding whenever a concept is being taught. Rather than repeating the concept for the whole class, the professor delivers a three-minute personalized lesson directly to that student's desk. Across campus, an architecture professor whose genius TA has handled stress tests and building code compliance is now free to focus entirely on the harder, more human questions of form, meaning, and cultural impact—the kind of tacit, experiential judgment that only human expertise can convey. Or, consider a philosophy module embedded in an AI course, in which students use a large language model to comprehend Kant's categorical imperative before gathering in class to grapple with "the scenario of the lying robot": an AI programmed never to lie that must decide whether to withhold truth that could trigger civil unrest. No algorithm resolves that question. Only a skilled teacher can build the crucial bridge between knowledge and wisdom in ways that are fundamentally human and deeply owned by the learner.

A concerned faculty member might hear all this and conclude they are becoming obsolete. The opposite is true. The evolution of faculty roles demands more—not less—of what makes a great teacher.

This means intervening in high-impact moments when the genius TA has not unlocked learning; curating class time to lift students from knowing material to applying it in contexts that require critical thinking, judgment, and discernment; and cultivating the human skills that will be most prized in the age of AI: effective communication, constructive dialogue, empathy, creativity, and professional disposition. Most importantly, it means building genuine relationships with students—that make them feel like they matter—the kind that fuels transformation.

Three key developments will be required for this evolution to succeed. First, faculty must learn to work with AI teaching assistants—shaping how these agents interact with students, directing them toward specific learning goals, and knowing when to override or supplement the machine's recommendations. No matter who provides the AI agent—the institution, a third-party vendor, or the faculty member—trust and transparency will be essential. Second, faculty must shift the fundamental focus of their instruction. Moving from knowledge transfer and routine assessment toward the higher-order work described above will require new pedagogical skills and a willingness to reimagine what happens in the classroom. Centers for teaching and learning, which have long supported faculty development at many institutions, will be among the busiest places on campus in the years ahead. Third, and most challenging, faculty must learn to love students again—or find in themselves the resources to do so more visibly and consistently. So much of higher education works against deep relational engagement. At research-intensive institutions, recognition and reward structures favor scholarship over student contact. At teaching-intensive institutions, heavy loads and large class sizes leave little time for knowing individual students in any meaningful way. Most faculty care deeply about students, yet only a small number have the time to get to know more than a few students well in any given term. A new paradigm that asks teachers to embrace a relational mode of engagement requires not so much new pedagogy as a change in mindset and habits of the heart.

Faculty also perform a third role—institutional service and stewardship—that AI can support but never replace. Curriculum committees, tenure boards, and policy deliberations require ethical judgment and political discernment that no algorithm can supply. And in a civil society increasingly defined by misinformation, eroded trust in expertise, and the rise of authoritarianism, colleges and universities—and the faculty within them—have never been more necessary as public intellectuals and moral stewards, translating specialized knowledge to help address the pressing needs of society. Today, ideology and commercial interest have eroded public trust in science, vividly illustrated by the vaccine debates and the gutting of America's public health apparatus. If we are to rebuild a rational, evidence-based approach to our collective challenges, we need our scholars and researchers to lead that effort and to model what reasoned debate looks like.

Beyond the classroom, the genius TA's counterpart for scholarship is a "genius research assistant." The laborious work of archival sifting, manual data processing, and bibliographic search—work that once consumed years—can now be performed by AI in moments. But the essential scholarly task is transformed, not eliminated, shifting from gathering information to discerning meaning. Humans must still ask the generative questions, determine what is missing, frame problems in ways AI cannot, and think across disciplines to recognize patterns that lie outside any single domain. In a genomics lab, AI may flag thousands of genetic variants, but the researcher must decide which ones warrant the profound cost of clinical intervention. In history, an algorithm can reveal patterns across millions of colonial documents, but the scholar's value lies in interpretation and moral context, asking why patterns persist and what they mean for contemporary justice. AI has supercharged scholarship, but humans must direct and shape the work.

In a world where machines will increasingly drive breakthroughs in climate, medicine, energy, and warfare—often while simulating human knowledge and emotion—the faculty member's ultimate function is to teach students and the public how to understand that work and to lead public debates about what to do with what technology makes possible. The age of AI does not diminish the faculty member's role; it expands and elevates it. Higher education becomes one of the last social institutions capable of cultivating the habits of mind a democracy requires. Faculty must serve as specialists in human judgment, ethical reasoning, and the cultivation of discernment—helping students weigh competing truths, evaluate evidence in context, and recognize the moral stakes embedded in every technological advance. In this future, the value of colleges and universities is secured not by their ability to compete with AI on speed or at scale, but by their unwavering commitment to what the machine can never replicate: the human work of generating, governing, and contextualizing knowledge to advance a just and flourishing society. The age of AI calls for reclaiming an elevated role for higher education institutions and a commensurate elevation of the faculty who make that role possible.

Notes

1. Gregory Elliott, Suzanne Kao, and Anne-Marie Grant,"Mattering: Empirical Validation of a Social-Psychological Concept,"Self and Identity 3, no. 4 (2004), 339–354.Jump back to footnote 1 in the text.↩
2. Donna Beegle, See Poverty . . . Be The Difference! (Communication Across Barriers, 2007).Jump back to footnote 2 in the text.↩
3. Emmy Werner, "Vulnerable but Invincible: High-Risk Children from Birth to Adulthood," Acta Pædiatrica: Nurturing the Child 86, no. S422 (July 1997), 103–105; Michael Rutter, "Psychosocial Resilience and Protective Mechanisms," American Journal of Orthopsychiatry 57, no. 3 (1987), 316–331.Jump back to footnote 3 in the text.↩
4. Eric Mazur, Peer Instruction: A User’s Manual (Pearson, 2013). Jump back to footnote 4 in the text.↩

Tanya Gamby is Vice President of AI Learner Development at Southern New Hampshire University.

David Kil is CEO and Founder of CML Insight.

Rachel Koblic is a Learning Architect and Product Strategist.

Paul LeBlanc is Visiting Scholar and Special Advisor at the Harvard University Graduate School of Education.

Mihnea Moldoveanu is Director of Desautels Centre for Integrative Thinking and the Marcel Desautels Professor of Integrative Thinking at University of Toronto.

George Siemens is Chief Artificial Intelligence Officer at Southern New Hampshire University.