AI, Questioning, and the Evolving Role of Faculty in Higher Education
Artificial Intelligence (AI) is often described as “thinking for us,” which leads to compelling discussions about how faculty might shape its use in educational contexts. Although AI can generate answers to complex questions, the way it does so differs substantially from human thought. AI is driven by patterns identified in extensive data, lacking the curiosity, intent, or intrinsic purpose that drive human cognition (Chomsky, 2006). In higher education, this gap between AI’s statistical output and the human capacity for inquiry highlights a central priority for faculty: to foster environments that empower students to develop their own questions and solutions.
AI as a Predictive Engine
AI’s strengths stem from its ability to scan massive amounts of information to predict probable outcomes. Noam Chomsky once posed the question of whether submarines “swim,” illustrating that an entity’s operation in an environment (e.g., a submarine in water) does not necessarily mean it possesses the essential qualities or experiences of a creature that actually swims (Chomsky, 2006). Similarly, AI’s capacity to scan data and produce seemingly coherent answers should not be confused with thinking in a human sense. Rather, these outputs are generated according to parameters set by programmers and prompted by individuals. The engine may make “intelligent” suggestions, but without human direction, it lacks both impetus and purpose.
For example, a language model such as ChatGPT relies on a prompting mechanism to generate text. Without an initial question or command, the system remains idle. Even when AI autonomously provides new solutions, it does so within the frameworks originally encoded by human developers. The structure and aim of AI-driven processes are ultimately shaped by faculty and students, whose questions set the course of inquiry.
From Answers to Inquiry
In many traditional settings, a teacher’s role is seen as the “sage on the stage,” handing down fixed information to passive learners (Weimer, 2013). However, emerging models emphasize that the most effective teaching involves helping students acquire, demonstrate, and transfer skills and competencies (National Research Council, 2012). When faculty focus solely on answers, they risk training students to rely on memorization and AI-generated responses. The more impactful path lies in guiding learners to pose their own questions, especially around authentic, real-world problems that demand critical thinking and creative solutions.
Shifting from a teacher-centered approach to a learner-centered one requires an environment where inquiry is encouraged. This involves prompting students to consider not only how to find an answer, but also why a question is worth pursuing. As Säljö (1979) explains, meaningful learning arises from a deeper engagement with the content, which is nurtured by students’ active curiosity. When students investigate their own lines of questioning, they take ownership of their learning. AI then functions as a resource and a springboard for further exploration, rather than a shortcut to right-or-wrong solutions.
Faculty as Architects of Inquiry
Contrary to fears that AI will replace human educators, the faculty role becomes more critical in an AI-infused classroom. Educators guide students to evaluate sources, weigh competing arguments, and frame their own questions effectively, tasks that extend beyond straightforward retrieval of information. Vygotsky’s (1978) emphasis on the social context of learning underscores the importance of dialogue and collaboration between faculty and students. Through strategic questioning, mentorship, and the design of collaborative projects, faculty can elevate the learning experience so that students acquire the competencies required in an AI-rich world.
Moreover, AI’s predictions and recommendations can serve as fertile ground for practicing critical thinking. Faculty can challenge students to assess the strengths and limitations of AI-generated content, prompting a more nuanced understanding of how knowledge is constructed and verified. This interplay fosters analytical skills that are increasingly vital in settings where AI tools frequently permeate professional practice (National Research Council, 2012).
Reframing the Conversation on AI in Higher Education
It is tempting to focus on whether AI “thinks,” but that line of questioning can obscure more pressing considerations about how AI can be harnessed for learning. Instead of concentrating on the risk that students will rely on AI for answers, faculty can empower them to develop the necessary competencies to ask powerful questions, interpret data, and construct novel solutions. As with Chomsky’s submarine analogy, the crucial issue is not whether AI “swims” like a human mind, but how educators can leverage this technology to enrich the learning landscape.
Although AI can surpass human capacity in data processing, it is the faculty who provide broader context and purpose. By centering pedagogical design on inquiry, creativity, and problem-solving, educators frame how AI fits into a robust educational experience. In doing so, higher education professionals reaffirm that AI is a tool to extend human capabilities and never a replacement for the essential human quest to ask, explore, and understand.
References
Chomsky, N. (2006). Language and mind (3rd ed.). Cambridge University Press.
National Research Council. (2012). Education for life and work: Developing transferable knowledge and skills in the 21st century. The National Academies Press.
Säljö, R. (1979). Learning in the learner’s perspective: I. Some common-sense conceptions. Reports from the Institute of Education, University of Gothenburg, 76.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Weimer, M. (2013). Learner-centered teaching: Five key changes to practice (2nd ed.). Jossey-Bass.