Founder and CEO at Fusemachines, Adjunct Associate Professor at Columbia University — on a mission to democratize Artificial Intelligence.
Whether we know it or not, we are interacting with AI on a daily basis. Be it in the form of AI-powered smart devices, robotics-enabled toys or through our interactions with chatbots and social media platforms. It is also evident from reports showcasing increased AI adoption that the new generation is embarking on a world waiting to be transformed by AI. However, are they appropriately being prepared for this future? And if so, by whom? The hard truth is that AI may be omnipresent, but AI educators are not.
The Current State Of AI Educators
Foundational AI education can provide students with a critical edge for a future powered by the technology. But many school teachers aren’t aware of the practical applications of AI themselves. Add to this the fact that an already short supply of AI faculty in schools and colleges is further shrinking. A 2020 study found that over the last 15 years, 153 artificial intelligence instructors/professors in North American universities left their posts for industry jobs.
Despite the shortage of AI teachers, there are a lot of them who are well-suited to be AI educators. For example, those who are well-versed in the disciplines of computer science and mathematics are best suited to be upskilled in AI. Additionally, those who teach related fields such as physics or economics and are interested in AI would also make good candidates.
But how can we effectively add to the pool of quality educators? This is where AI training for educators comes in.
Steps That Go Into AI Training For Educators
As someone who plays a critical role in shaping a student’s future, an educator—irrespective of their AI background—must undergo training in phases.
• An introduction to AI: This entails providing aspiring AI trainers with a clear understanding of how AI stands to benefit society. It’s often easier to help get anyone acquainted with AI by putting the technology’s applications in the context of one’s line of work. For example, educators themselves can benefit from AI, which enables student performance analysis or automation of routine tasks. This phase should also offer an understanding of the technology’s shortcomings, such as data dependencies, bias, continued research and challenges, so as to set correct expectations. But the most important element in this phase is to ensure educators learn the skill sets needed to first learn and then teach AI, including different programming languages, advanced mathematics and analytics.
• Integrating AI into the existing curriculum: Not all AI educators have the same audience group to whom they will deliver the AI training. Similarly, they are not all at the same proficiency level for delivering AI training. A key element to successfully integrating AI into students’ learning experience is to identify courses that need to be added to the existing curriculum. For example, if a school already has courses running in computer science, foundations in data science would be a good addition. Similarly, integrating a computer science curriculum with mathematics and computation can be helpful. Educators also need to know how to devise curricula for the different levels they teach, such as:
• Early AI education: Today, educators have an opportunity to introduce AI as early as primary education. When teaching young minds, educators must focus on sparking and nurturing curiosity around the technology. They can do this by talking about the different AI-related things their students have already been exposed to and introducing game-based learning concepts. For example, children can be encouraged to learn about robot toys or smart devices they are interacting with in their homes.
• Mid-high school education: For educators catering to mid-high school-level students, focusing on nurturing students’ interest in the right subjects becomes key. Helping them focus on programming languages such as Python, advanced mathematics, data science and analytics would be important. Educators must also help these students envision an AI career roadmap and share about the full breadth of AI jobs and career opportunities—from AI and data strategists to AI research and academics. It also goes a long way if a teacher can help enable access for their students to resources outside of the traditional school curriculum. This includes encouraging them to partake in AI expos, workshops and career fairs.
• College/university: For university-level students, AI learning is much more nuanced and specialized. So for teachers training to teach students at this level, it is critical to help them see industry-specific applications of AI, what AI trends are shaping the future as well as where there is a clear vacuum for talent.
• Understanding the nuances of teaching AI: This entails a thorough understanding of areas where students will likely struggle in their learning journey. Educators need to pre-empt problem areas and proactively come up with solutions. One of the most valuable tools educators can rely on for this is the AI technology itself. AI-enabled learning management systems create an opportunity for educators to deliver a personalized learning experience. Educators can use AI to track and analyze students’ progress, identify teaching mechanisms that improve engagement, curate learning materials that students will find valuable and generate customized assignments depending on where each student is in their AI learning journey.
• Identifying resources and tools to effectively teach AI: Not unlike the resources that they will need to equip their students with, educators, too need to stay ahead of the trends in the ever-evolving field of AI. By actively participating in AI expos, trade shows and workshops, educators can stay current on AI’s advancements. This is also valuable as they devise the syllabi and curricula for the levels they are teaching and when they navigate the syllabus approval process at schools’ district levels.
AI as a field inherently evokes a continuous learning journey for everyone working with the technology—developers, students and educators alike. The most important step to ensuring we are creating a reliable set of mentors for the new generation is to empower them to visualize the future applications of the technology.