“As the pace of technological change increases, many jobs will require constant adaptation creating less distinction between learning and work.” (O’ Discroll, 1999). This is especially true for today’s workforce if you ask me. In many workforce arenas, people are becoming more and more accustomed to learning in an increasingly dynamic environment using tools such as social media, mobile devices, and other readily accessible technologies to access and update a collective body of knowledge with their peers. Some may even call this a “Just In Time” approach to knowledge acquisition and training. These traits suggest what e-learning and education professionals, and instructional designers have been noticing for quite some time and that is the notion of a collaborative, learner centered approach to training. It allows the worker to perform a task while being guided by an expert peer or mentor and at the same time contributing to the knowledge base.
Of course, this information between peers is managed, facilitated, and often validated by someone who retains the knowledge right? What if that key person was gone tomorrow? Artificial intelligence driven performance support systems are able to excel in two areas.
1) Assisting the user in performing their job while instructing them at the same time thus making the user more proficient.
2) Using the varying knowledge, skills, and experience levels of all individuals who use the system to update its own knowledge base with lessons learned (new information). These lessons learned result in the enhancement and tailoring of the learning experience for each individual.
Let’s say you are a skilled worker in a factory and you experience a problem with the machinery you are running. You work evenings so there are less people for you to run to for advice or resolve. Furthermore, if you stop the machine to reference a manual, you may create a period of downtime that affects the entire company’s profits for that week.
Lucky for you, you have access to a Discovery Machine “Coach” to help you through your problem. You launch the system either by clicking on a screen near you or simply speaking just as you would into your smart phone. The AI system recognizes the problem you spoke about and takes you through a series of analytical steps to resolve the problem. In the process, you may even tell the coach that there is something atypical about the situation. Through a series of questions and answers, the coach can resolve this and add this lesson learned to its knowledge base for the next person that launches the coach. Sounds pretty cool right? The coach could even print out some reference material for you to review describing how it resolved the problem and let you know that you can pick it up in the break room from the printer when it is convenient.
Discovery Machine Inc. has built a few of these tools in recent years with continuous improvements as a result of extensive research and quality driven partners and clients. These systems use proven instructional design delivery methodologies based on work by cognitive scientists and instructional designers in conjunction with the an expandable knowledge base. This knowledge base, includes growing ontologies and functional models of structured knowledge (representations of structured knowledge captured from the client and built into executable software). These functional models use artificial intelligence techniques to reference the ontologies to process information, gain understanding, and respond intelligently to the user just as a human would.
If you were to have something like this at your company, how could it help you?