Adaptive Learning and Personalization Within the Enterprise

Content Curation

Ambitions for the enterprise that perfects even some of these techniques for adaptive learning and personalization are decidedly lofty. Not surprisingly, some of them entail agents or, what Scott Lingenfelter, principal project manager of operations at Laserfiche AI, termed “autonomous agents.” Such agents exist in the back end of IT systems, constantly assessing user behavior, results, and the very fabric of how a person or department works—all based on the input signals that an individual chooses. 

Someone in sales may want agents to inform them every time a document becomes available that pertains to a particular customer. Instead of simply basing those results on the keywords, the agent would have to understand the underlying content to draw parallels and glean how a document could relate to that customer or business objective. “That takes reasoning; that takes intelligence,” Lingenfelter explained. “Then, my content is constantly curated for me. I don’t have to go look for updates and changes in the system. I start to have things that are ready for me to work with as soon as I walk in.”

Positive Feedback

Whether the use case involves content curation, information retrieval, application building, or use of an application in the most meaningful way, the crux of adaptive learning—and its personalized results—is a feedback mechanism. Intelligent systems—relying on agents, knowledge graphs, vector stores, content repositories, document management systems, and more—must determine which results are helpful to users and which aren’t. Reinserting that knowledge back into the underlying system becomes the basis for an intelligent “learning system that learns who’s interested in what [or] why not,” Aasman concluded. 

Personalization and adaptive learning, from the KM perspective, revolve around human knowledge needs, which can change as projects, perspectives, and products change. Plus, given rapid technological advances that accelerate the ability of enterprises to understand those changes that affect personalization, people’s expectations have heightened. Being able to tailor knowledge to individual preferences is suddenly the norm, although individuals may have to inform chatbots, language models, and knowledgebases of what has changed.