AI and the Future of Education

The Role of AI in Education

As graduation ceremonies celebrate a new generation, some wonder if AI will negate the value of education. Certain CEOs believe AI will take over the roles of engineers, doctors, and teachers. One CEO predicted AI would replace mid-level engineers who write computer code. Another declared coding itself unnecessary. One individual acknowledged AI's rapid advancement as potentially alarming but noted it could democratize access to knowledge. He also anticipates AI replacing coders, doctors, and teachers, providing readily available medical advice and education.

The Limits of AI

Despite the excitement, AI currently lacks independent thought or action. Whether AI benefits learning or hinders comprehension depends on whether we allow it only to recognize patterns or require it to explain, justify, and remain rooted in established principles. Human judgment is essential for AI, both to oversee its results and to incorporate guidelines that provide direction, foundation, and clarity. A physicist likened AI chatbots to average students during oral exams, able to provide answers when known but skilled at deception otherwise. According to the physicist, users unfamiliar with a subject might not detect a chatbot's fabrication. This highlights AI's knowledge: mimicking understanding through predicting word order but lacking true conceptual understanding. This is why creative AI systems struggle to differentiate between authentic and artificial content, and questions arise regarding whether language models truly grasp cultural subtleties.

Integrating Human Knowledge

Teachers worry that AI tutors may impede students' critical thinking, and doctors are concerned about misdiagnosis; both concerns highlight a common flaw: machine learning excels at pattern identification but lacks the comprehensive understanding derived from collective human experience and scientific methodology. A growing trend in AI seeks to address this by incorporating human knowledge directly into machine learning processes. Examples include PINNs and MINNs. The core idea is that AI improves when it adheres to established rules, whether physical laws, biological systems, or social norms. This underscores the continued need for humans to not only utilize knowledge but also to generate it. AI is most effective when it learns from us. For instance, instead of allowing an algorithm to speculate based on previous data, we instruct it to adhere to established scientific principles. Consider a local lavender farm where timing is crucial; early or late harvesting reduces essential oil potency, affecting quality and profitability. While an AI might waste time analyzing irrelevant patterns, a MINN uses plant biology, employing equations that connect heat, light, frost, and water to blooming, enabling timely and valuable predictions. This works when the model understands the physical, chemical, and biological aspects, knowledge that is derived from human-developed science.

The Importance of Human Oversight

Consider cancer detection: while AI could analyze thermal images to identify tumors based on data patterns, a MINN uses body-surface temperature data and incorporates bioheat transfer laws into the model. This allows it to understand heat movement through the body, pinpointing anomalies and their causes by using the physics of heat flow through tissue. In one instance, a MINN accurately predicted a tumor's location and size. The key point is that humans remain essential. As AI advances, our role evolves. We must identify inaccuracies, biases, or errors in algorithms. This is not an AI weakness but a human strength. Our knowledge must expand to guide the technology, ensuring its accuracy and benefiting others. The real risk is not AI's intelligence but our potential failure to use our own. Treating AI as an infallible source risks losing our ability to question, reason, and recognize illogical outputs.

The Path Forward

The future doesn't have to unfold this way. We can develop transparent, understandable systems grounded in science, ethics, and culture. Policymakers can fund interpretable AI research. Universities can educate students to combine knowledge with technical expertise. Developers can use frameworks like MINNs and PINNs that ensure models reflect reality. Furthermore, everyone can insist that AI serves scientific accuracy, not just correlations. After years of teaching statistics and scientific modeling, the focus is now on helping students understand how algorithms function by learning the systems themselves rather than passively using them. The goal is to improve literacy in math, science, and coding. This approach is necessary now. We need individuals who grasp AI's logic, code, and math to identify errors. AI will not render education obsolete or replace humans. However, we risk self-replacement if we neglect independent thought and the importance of understanding. The choice is not about rejecting or accepting AI but about remaining informed and capable enough to guide it.