While it\u2019s easy to be carried away by AI\u2019s future possibilities, it\u2019s the near-term potential of AI that most people want to understand.\u00a0A unanimous point we\u2019ve seen across industry experts is that AI isn\u2019t here to replace human jobs \u2013 the trainers, developers, and instructional designers. Rather, it would augment them. In fact, we see this in the military, with AI automating data-intensive functions to help warfighters with decision-making, freeing them up for more strategic, higher value activities.<\/p>\n
Fellow panelist, Colonel Robert H. \u201cHammerhead\u201d Epstein, Commander of the Air Force Agency for Modeling and Simulation offered, \u201cAI will not replace our instructors, rather it will enable them to produce better-trained Airmen quicker by automating repetitive tasks and optimizing live instructor contact time. The Air Force is aggressively leveraging cutting-edge artificial intelligence technology to take full advantage of this emerging way to train our people.\u201d<\/p>\n
More efficient, more effective learning with AI<\/strong><\/p>\n The unique ability of AI to process tremendous amounts of data, far beyond our human capacities, is what will make learning so much more efficient.\u00a0By taking the data that\u2019s being collected, but not exploited \u2013 and not just scoring data, but rich data from the field, the classroom, and simulation \u2013 we can use AI to target in very specific and precise ways where we are deficient, and what we need to learn. This is the future of precision learning.<\/p>\n The second aspect of this is the exponential power of machine learning, which is now a thousand times faster than a few years ago. So, instead of waiting hours or days for training feedback, what if we can reduce that gap to fractions of second?\u00a0By adapting training in real-time, to adjust scenarios and learning paths, trainees can focus on improving what they didn\u2019t do well, and not repeating what they did.\u00a0We know from research that delivering feedback in the moment, as learners learn, dramatically improves their performance, and diminishes over time. In this way, AI can help trainees reach higher proficiencies in less time, making training more efficient and more effective.<\/p>\n \u2018Smarts everywhere\u2019<\/strong><\/p>\n To illustrate the point, Sae Schatz, Ph.D., the Director of the Advanced Distributed Learning Initiative, explained the future in terms of a total learning architecture embedded with AI. With AI \u2018smarts\u2019 injected everywhere\u2014 inside simulations, databases, learning record management systems, and more \u2013 AI can enable this feedback.<\/p>\n \u201cWe\u2019re already seeing the first inklings with learning analytics dashboards, which are becoming common in digital learning platforms and at many higher education institutions,\u201d Schatz said. \u201cLike so many jobs, future instructors are likely to be human-AI teams.\u00a0AI can help instructors and institutions understand their students\u2019 states, diagnose hidden issues, and predict future outcomes to support early interventions.\u201d<\/p>\n Schatz makes clear, however, that \u201ca human touch is still needed throughout. It\u2019s the combination of people and algorithms that has the highest potential.\u201d In other words, using AI to personalize learning at scale to help guide students would be like inserting a smart and caring teacher into the learning process, so that even an average trainer can become an excellent teacher with AI.<\/p>\n This represents a profound change to the learning environment, but unless the humans in the loop\u2014the trainers, observers, and commanders\u2014are able to adapt to this new reality, it won\u2019t work as envisioned.<\/p>\n The human element<\/strong><\/p>\n We humans excel at watching and instinctively learning about each other. We subconsciously observe a co-worker, knowing they can be sluggish without their coffee in the morning. But AI learns very differently. And in an aircraft cockpit, an AI-enabled autopilot could come to a very different conclusion, deciding to take over unexpectedly.<\/p>\n Humans have only adapted in this regard when working with other humans. We know how frustrating new technologies can be when they force us to change in ways we aren\u2019t used to.\u00a0If your computer actively tried to adapt to you, you might not only find it annoying but disruptive. If AI observes and changes to better suit you, but you didn\u2019t change in the same way or acknowledge the augmentation, it would be like ignoring a teammate, or working sub-optimally. In other words, the human operator has to know what AI knows<\/em> in order to work as team. This new paradigm of human-machine teams requires mutual feedback loops so that humans and AI can communicate, understand, and learn from each other for concurrent cycles of adaptation.<\/p>\n
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