AI Training Content Selection for User Engagement and Compliance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Institutional and educational training systems suffer from a lack of user engagement and innovation, often relying on generic and uninspiring content that fails to drive meaningful behavioral change, leading to user apathy and ineffective dissemination of institutional policies.
Innovation Solution
A digital training system utilizing a web-based interface that employs artificial intelligence to tailor training content to individual users, incorporating peer and non-peer expert videos, monitoring user behavior, and refining algorithms based on engagement metrics to provide personalized and engaging training experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generic training videos with quizzes are used, then training coverage is achieved, but user engagement deteriorates
Solution Approach 1:
The training system dynamically adapts content based on user behavior, transitioning from static generic videos to dynamic personalized content delivery. The system monitors user interactions and adjusts content selection in real-time, making the training experience adaptable rather than fixed.
Solution Approach 2:
The system changes content parameters by selecting from multiple videos on the same topic with varying engagement characteristics. When a user shows disengagement, the system changes the content parameter by selecting a different video that may have higher engagement potential while covering the same training material.
2Ease of operation
If AI-based personalized content selection is implemented, then user engagement is improved, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically monitoring user behavior and selecting appropriate content without requiring manual intervention. The AI algorithm autonomously analyzes engagement metrics and makes content selection decisions, reducing the need for complex manual configuration and management.
Solution Approach 2:
The system implements continuous feedback loops where user behavior is monitored and fed back into the content selection algorithm. This feedback mechanism allows the system to learn from user interactions and improve content personalization over time, managing complexity through iterative optimization rather than complex upfront design.
3Reliability
If multiple training videos are monitored and selected based on behavior, then training effectiveness is improved, but data processing requirements increase
Solution Approach 1:
The system applies partial monitoring by focusing on key behavioral indicators rather than analyzing every user action in detail. It monitors essential engagement metrics sufficient for content selection without excessive data collection, balancing effectiveness with processing efficiency.
Data Source
AI summary
According to one example, there is a training system including a processor and a non-transitory computer-readable medium including computer executable instructions for configuring the processor to carry out a method including: providing an electronic user interface for a trainee to login and remotely access training materials via a web-based content access user interface; matching a trainee identification to at least one of a group of parameters; selecting a first training video for the trainee to view based upon the matching step, the first training video having a first topic and a first time of play; initiating play of the first training video; monitoring behavior of the trainee as they view the first training video; selecting a second training video for the trainee to view based upon the monitoring step; initiating play of the second training video; and monitoring behavior of the trainee as they view the second training video.