Earlier this summer, the Department of Education released guidance regarding using federal grant funds for the implementation of AI. The guidance encourages educators to responsibly integrate AI to improve outcomes for learners, with high-level examples of areas where AI could support education initiatives.
Universities have increasingly been integrating AI into their curricula, gaining experience with what works, where students have the most to gain from AI, and where humans will continue to play an irreplaceable role.
AI provides efficiency and directionality, but the core of experiential learning—empathy, relationship-building, and problem-solving—remains fundamentally human.
AI can process information, recognize patterns and generate insights, but true understanding—the foundation of experiential education—requires direct human engagement. Mission-driven innovation, particularly in problem discovery, necessitates firsthand interaction with stakeholders to truly comprehend challenges beyond theoretical knowledge.
With these considerations in mind, how can AI be leveraged as a tool to enhance learning without displacing the human experience?
Irreplaceable role of humans in AI-augmented education
The relationship between humans and technology is changing. While traditionally only providing easier access to information, technology tools can now provide a level of analysis, crossing a critical divide in the role they play in our decision making and learning.
While smartphones gave us instant access to knowledge, generative AI represents a shift toward automated synthesis of information.
Although AI can assist in information processing and play a greater role in today’s curriculum, it still lacks the ability to replicate the depth of human interaction and contextual understanding. It cannot truly “understand” or interpret lived experiences.
This is especially true in mission-driven learning, which requires a human connection. Solving real-world problems demands empathy, sensory awareness and an understanding of the human condition—areas where AI falls short.
Beneficiary discovery is a critical component of innovation, requiring real engagement to assess the full scope of challenges. Deep learning comes from lived experience, which must continue to remain a central element, even in AI-supported curriculums.
In this type of environment, students learn that assumptions about a problem often collapse once they see the lived experiences of those directly affected.
For example, a case study may outline the existence of food deserts, but only by engaging with residents can illustrate how limited access to fresh food shapes daily choices, health and dignity. These perspectives—rooted in context and human experience—cannot be replicated by textbooks or AI but are essential for developing solutions that truly matter.
Academic programs should encourage the strategic use of AI to enhance, rather than replace, human engagement. The focus should be on immersing students in the entrepreneurial process with hands-on experience and with AI-supported coursework.
Students should learn that problems are inherently human-centered and removing people from the process risks relying on secondhand or incomplete information. Through direct engagement, they can gain a framework for dissecting problems, capture insights from beneficiaries and iterate toward solutions that are not only feasible but also adoptable and impactful.
AI supports this work by surfacing patterns and synthesizing data, but students should leave with the recognition that real innovation begins and ends with people at the center.
A classic moment in the movie “Good Will Hunting” represents this well: You can know everything there is to know about Michelangelo, but that isn’t the same as knowing the feeling and smell of standing beneath the Sistine Chapel. Often, it’s the experience that teaches the most.
AI as a collaborator, not a replacement
Especially when it comes to education, AI’s greatest value is as a collaborator. AI can identify patterns, summarize information and suggest hypotheses, but humans must validate insights through direct interaction with stakeholders.
Successful cybersecurity approaches serve as a valuable analogy. Despite technological advancements, people remain the first line of defense to provide interpretation and validate the findings or recommendations of AI tools.
Similarly, education must remain human-centered, requiring a person to validate and supplement curriculums developed using AI.
When it comes to AI serving as a collaborator for curriculum development, it can act as an instructor for teaching videos or create podcasts, lesson plans and discussion points to support learning. It works best when faculty use it to accelerate iteration, not to replace judgment.
AI can surface ideas or draft materials quickly, but only an instructor—grounded in classroom realities and domain expertise—can interpret, refine, and contextualize those outputs. In this way, AI becomes a force multiplier, helping educators move faster while keeping human experience at the center of teaching and learning.
With a human in the loop to validate and supplement such efforts, AI saves professors valuable time so they can focus more on making the hands-on, experiential learning experiences for their students richer and more powerful.
Balancing AI and human-led experiential learning
There is a clear risk to over-reliance on AI in education, from concerns about potential detachment from real-world complexity to ethical concerns about AI-generated knowledge.
But it is undeniable that AI has a role in the future of higher education. As we use AI to free up time for deeper engagement, students and educators must remain at the core of learning. Educators should embrace AI as a powerful assistant while keeping human-driven experiential learning central.
