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Many courses rely on overly simplistic rules for creating customized learner journeys. The most common are dictated by learner choices such as desired content or job role. Slightly more effective techniques use progress/scoring to determine a path through pretests, remediation or repetition. These methods simply create gates to pass, or lead to additional supplemental information. In this session you will learn how to use more effective methods to dynamically customize a learner's journey through the use of AI, regardless of your authoring tool, LMS, LRS, or LXP.
The topics will include the best factors to use, how to collect data on those factors, and how to implement the data to dynamically create the optimal learning path for each user.

This program will focus on:
• Factors should be used to adapt a learner journey
• Tool agnostic methods for creating adapting learning
• Methods for collecting data for adaptive learning
• How to use that data to best create an effective leaner journey
• Adaptive techniques can be applied to context, feedback, activity design, pacing, complexity, timing, etc.
• Case studies of organizations that have created tools and workflows to ensure adaptive learning environments.