The lure of artificial intelligence (AI) is appealing to many companies, from the healthcare industry to banks. It is clear this new technology poses numerous benefits, from automating tasks and processes to helping employees. By programming a computer to make decisions for itself, AI systems can rapidly execute almost any human task. In a recent post, we argued that students should learn AI in high school. Teaching innovative technologies in the classroom could establish a more human connection between machines and minds. When learning happens simultaneously with the development of a program or software, students will unconsciously learn the meta-skill of collaborating with computers. The strong link between technology and curious students could exponentially increase the rate of innovation. However, learning AI is hard and time consuming. Intensive programming, large data sets, and complexity make it difficult to learn. In fact, it can take five to six months to learn the foundational concepts that govern AI. For more advanced specializations – like deep learning, reinforcement learning and unsupervised machine learning (a subset of AI) – the training period is much longer. But there is a way to circumvent the complexity of learning AI in the classroom. Instead of establishing one AI curriculum, teachers could add the core principles of AI into their lesson plans. Students would encounter the uses of AI in multiple contexts, from their statistics class to psychology.
Teaching the foundations of AI in college still seems more reasonable than high school. But it really is important to learn AI as early as in high school. Multiple companies lack the talent and expertise to transition their business to the cloud. Thus, these businesses do not have the adaptability and flexibility needed in today’s constantly changing world. Furthermore, people are employed to fix and oversee these unreliable systems, depriving them of creative and challenging tasks most employees yearn for. Skills in AI and machine learning are in high demand because of these technologies’ endless potential in improving human undertakings. But it is not only the technical skill of coding and programming that is influential. As students learn the practical side of controlling software, they also begin to build a different kind of relationship with machinery. AI programs could eventually scratch the surface of human-like creativity. The abilities of digital tools could prompt students to see the similarities between the two intelligences. If high schoolers even get a glimpse of the powers of such computers and the resemblance between man and machine, the class is already worth it.
So, how could teachers implement AI into curricula? Instead of teaching all teenagers the specialized parts of AI, educators could weave the foundations and principles into existing curriculum. The basics of AI include statistics, probability theory, calculus, psychology – even philosophy. In these existing courses, teachers could mention and introduce how a subject applies to AI. Students could understand the real-world applications of a lesson while learning the foundational principles that govern new technologies. A lesson that was previously a theoretical concept commemorated in a lined notebook could now become an interesting, relevant puzzle tested on computers. Armed with practice and understanding of the core tenets of AI, students can either pursue these technologies further or encounter them elsewhere throughout their career.
Although learning AI and related technologies seems like a daunting challenge, incorporating its basic tenets into higher education will bring about employees well trained in the digital world. An AI curriculum does not have to be complicated. With small snippets and encounters with new technologies in a comfortable classroom setting, students will be well prepared for the future.