Using AI to enhance workforce development.
AI will improve workforce development by enhancing the role of the educator, not replacing them. Everyday news headlines read ‘Intelligent machines will replace teachers within ten years[i].’ Or ‘Imagine how great universities could be without all those human teachers[ii].’ They position the goal of AI as replacing educators with machines. There were similar predictions about radio, television, computers, and, in the last few years, also about Massive Open Online Courses. None of these technologies alleviated the need for educators, but they all enhanced the learning experiences educators could curate. AI offers the same potential to transform learning by supporting educators, rather than replacing them.
AI can support educators to identify blind spots and to be more effective. One example of this is the use of biometric or eye tracking data to measure student collaboration, attention, and teamwork[iii]. Using AI to analyze this type of data, can help educators see what students are doing, why they are doing it, who is engaged, and who might need extra support. By enabling data-driven feedback loops for learning designers and educators using learning technologies, AI can also help identify opportunities for improvement. An early tool embodying this idea is the Open Learning Initiative. This tool, created by Stanford Professor Candace Thille, “…delivers scientifically designed, formatively and summatively evaluated, and iteratively improved eLearning[iv].”
Personalized learning is a powerful use of AI for workforce development. Personalized learning’s greatest strength is skilling declarative knowledge with adult learners. This includes knowledge such as instruction, processes, procedures, and other similar workplace learning. Personalized learning requires a standardized data set, structure, and measurable knowledge. Yet, information that fits into a structure that is easily measured isn’t always representative of all knowledge. Personalized learning systems can limit or falsely inflate learners understanding of a concept by assuming that getting an answer correct equals understanding[v].
Educators can help us navigate key ethical issues. Many ethical questions that arise with AI’s application to learning. In educational, the risk of incorrect prediction is more consequential than say, one provided by an online retailer. What happens if the personalized learning agent predicts the wrong thing? If the AI’s goal is to reduce error, what happens if it increases learning on one metric to the detriment of another[vi]? And finally, will learners that are subject to training the algorithms, face a higher risk of error? To mitigate these risks we can use AI as a tool to support educators, rather than to replace them. Those often involved in workforce development such as transitioning service members and disrupted workers are likely to be vulnerable. We need to be diligent in making sure that AI enhances their educational opportunities so as not to further disadvantage them.
[iii] Presentation by Paulo Blikstein, AI’s Impact on Education, Training, and Learning: Potential and Limitations, at the Media X November 13th 2017 Symposium Innovation Ecosystems for AI-Based Education, Training and Learning
[v] Presentation by Paulo Blikstein, AI’s Impact on Education, Training, and Learning: Potential and Limitations, at the Media X November 13th 2017 Symposium Innovation Ecosystems for AI-Based Education, Training and Learning
[vi] Presentation by Paulo Blikstein, AI’s Impact on Education, Training, and Learning: Potential and Limitations, at the Media X November 13th 2017 Symposium Innovation Ecosystems for AI-Based Education, Training and Learning