As AI takes on more tasks in higher education, it is tempting to ask whether faculty will become less necessary. A more useful question is how their work might change and what parts of teaching depend on human knowledge, judgment, and relationships.
Faculty have always done more than present information. They help students enter a discipline: learning its language, working with its evidence, and understanding how people in the field decide what counts as a good explanation or solution. AI can provide information and practice, but students still need guidance in making sense of what they encounter.
In the near future, AI may become a routine part of course preparation and instruction. It could help instructors generate practice questions, offer alternate explanations, or provide students with additional feedback between class meetings. Faculty could use that support to notice common misunderstandings and devote class time to questions students are still working through.
That would not make the instructor’s role smaller by default. It could shift attention toward designing worthwhile learning experiences, interpreting student work, and deciding what kind of guidance will help a particular group move forward. An AI system might identify a pattern in student responses; the instructor can consider what that pattern means in the context of the course and what to do next.
The work of teaching also includes choices that cannot be settled by a system’s output. What should students learn to do themselves? When is it useful to let them struggle with a problem, and when should support arrive? How should a course respond when a student is discouraged or when a class discussion reveals an assumption worth examining? These decisions depend on educational purpose and on knowing the learners and context.
Over the next decade, faculty roles could become more varied. Some instructors may spend more time coaching students through projects and helping them connect learning across courses. Others may focus on designing simulations, supervising collaborative work, or guiding students as they use AI in professional settings. In many cases, faculty will continue to teach, explain, assess, and advise, while also making more deliberate decisions about where AI fits.
Those possibilities will depend on institutional choices. If AI adoption is driven mainly by reducing costs or increasing the number of students served, faculty may be asked to oversee systems at scale with less time for students. If the goal is to improve learning, institutions will need to invest in faculty expertise, professional development, and time to design and evaluate new approaches.
Faculty also need a voice in those decisions. They understand their disciplines, their students, and the work of teaching. Involving them in AI decisions can help colleges set clearer expectations, identify appropriate uses, and recognize where automation could weaken learning or trust. Students and staff should be part of that conversation as well.
The future faculty role will not be defined by a single title such as mentor, coach, or designer. Most faculty already move among these roles. The more important question is whether colleges create conditions that allow faculty to use their expertise in ways that support student learning.
AI may help with explanations, practice, and routine feedback. Faculty will still need to decide what matters, interpret the context, and help students grow into the work of a discipline. As AI becomes part of more institutional decisions, another question follows: How can colleges maintain trust when these systems are used in teaching and in the decisions that affect students?
Continuing the Conversation
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Series 6: The Purpose of Education in an AI World
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