Heidegger’s concept of technology as a mode of revealing human nature (The Question Concerning Technology, 1977) offers a valuable perspective on how artificial intelligence (AI) might reshape teaching and learning in higher education. In Heidegger’s view, technology does more than merely perform tasks; it reveals something about human priorities, values and ways we engage with the world. Applied to the classroom, artificial intelligence tools reveal both hopes and anxieties. Faculty members fear losing control, worry about rampant cheating, question the ethics of AI and wonder whether their own roles may be devalued. These concerns, per Heidegger’s theory, are revealing teachers’ fears, but these need not dominate the conversation. By reframing AI as an enabler rather than a threat, higher education can focus on the deeper question of how best to foster genuine student learning and development.
A significant source of faculty apprehension lies in the perceived threat that AI poses to academic integrity. Tools capable of generating essays, solving equations or summarizing research challenge traditional assessment methods and raise concerns about student learning. In effect, AI forces educators to confront an uncomfortable truth: If the primary goal of an assignment is only to produce information or replicate existing knowledge, then AI can often do the job more efficiently. However, this very disruption can spur innovation in the design of student learning experiences. Instead of purely testing recall or cursory comprehension, assignments can shift toward assessing students’ ability to apply, synthesize and create. And these are skills that require deeper engagement and can be more readily observed.
From Heidegger’s standpoint, this shift in perspective could be seen as letting AI reveal a need for deeper, more meaningful forms of student work. Rather than relying on rote memorization or superficial outputs, educators can focus on skill and competency development demonstrated through observable behavior. These might include collaborative project work, presentations, research proposals or simulations that clearly show how students apply the content they have learned. By moving toward a system of student learning assessment in which students must showcase their competencies in tangible ways, faculty make learning outcomes more relevant to students’ lives and easier to measure for themselves as instructors.
AI becomes a powerful ally in this process of skill demonstration and measurement. Intelligent tutoring systems, analytics platforms and adaptive learning tools can personalize the educational journey, helping students set goals, monitor progress and reflect on their own competency development. For instance, an AI-powered platform might track how many practice problems a student has completed, indicate skills and competencies with which they struggle most, and show how their performance improves over time. Students, by observing their skills and competencies in action, can see concrete evidence of their growth, while faculty gain actionable insights to tailor instruction. In this framework, AI does not replace the educator but supports a more individualized approach, one that values the learner’s agency and fosters self-efficacy.
This emphasis on student agency, another facet of Heidegger’s notion of revealing, aligns well with competency-based education (CBE) models, which prioritize explicit learning outcomes and skills that students can demonstrate through performance such as project-based assessments or any student evaluation environment where faculty can observe their students’ behavior. When learners understand exactly what they need to achieve, why it matters and how it connects to their future goals, they are far more likely to engage with the learning process. AI can streamline some of the more time-consuming aspects of monitoring proficiency and offering feedback, enabling educators to concentrate on more complex tasks such as coaching, mentoring and designing meaningful experiences.
In this way, faculty members transition from gatekeepers of content to mentors and facilitators. They guide students in navigating complex questions, framing meaningful projects and reflecting on their progress. AI can handle much of the routine feedback that, while crucial, can overwhelm instructors who teach large classes or multiple sections. Freed from the pressure of grading every quiz or proofreading every draft, educators can dedicate their energy to higher-order tasks: discussing the ethical implications of AI, encouraging critical thinking and helping students draw connections across disciplines. It is precisely in these areas, ethics, creativity, moral reasoning and collaborative innovation, that human expertise remains indispensable.
Ultimately, why a student learns remains at the heart of this conversation. Students are far more motivated when they can see the immediate relevance of their studies and perceive themselves as agents of their own growth. If the only objective is to reproduce textbook answers or turn in essays for a grade, AI’s capacity to automate such tasks can undermine the perceived value of the learning process. But when instruction focuses on clear competencies, such as collaborative problem-solving, creative design, research acumen or effective communication, students understand these competencies’ practical importance, and their engagement and sense of ownership increase. Human beings naturally invest time and energy in activities they value and see as personally or professionally beneficial. This human trait must be observed and taken advantage of in the classroom.
This is where Heidegger’s thinking can offer one final insight: Technology, including AI, does not dictate outcomes on its own. Rather, it reveals our educational priorities. If we seize this moment to realign our teaching methods around competency, agency and meaningful engagement, AI can become an instrument for positive transformation instead of a source of fear. Institutions that embrace this perspective will likely see students who not only perform well in assessments but also emerge with the ability to transfer their skills across contexts, adapt to new challenges and contribute thoughtfully to society. In that sense, higher education’s real value in preparing students for life beyond the classroom becomes more evident than ever.
By leveraging AI as a supportive tool within well-crafted instructional design, higher education can move beyond worries about cheating or job displacement to foster a robust, learner-centered paradigm. Faculty can reclaim their primary roles as mentors, guiding students through complex intellectual terrain, fostering genuine curiosity and helping individuals connect their skills and competencies to real-world applications. Students, in turn, will not simply leave with a transcript but with the demonstrable skills, confidence and habit of mind they need to thrive in a rapidly changing world. Rather than undermining higher education, AI stands poised to reveal its most vital purpose: nurturing thoughtful, capable and intrinsically motivated learners who understand both what they are learning and why it matters.
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