ML Content Adaptation for User Engagement
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Solution Overview
Problem
Existing methods for tailoring content complexity to different audiences are manual, time-consuming, and subjective, failing to dynamically adjust to user engagement levels, which can lead to decreased comprehension and retention.
Innovation Solution
A machine learning-based system that evaluates user lexical knowledge and engagement, selecting and adjusting content complexity in real-time to maintain audience engagement by processing documents through clustering and analyzing lexical complexity and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual tailoring of content complexity is performed, then content can be customized for different audiences, but the process is time-consuming and subjective
Solution Approach 1:
The system enables self-service by automatically analyzing user profiles, determining lexical knowledge levels, and selecting appropriate document complexity without manual intervention. The machine learning model autonomously performs content customization based on user characteristics, eliminating the need for presenters to manually tailor content while maintaining adaptability to different audiences.
Solution Approach 2:
The patent replaces the manual mechanical process of content tailoring with an automated machine learning system. The ML model processes user data and document characteristics to automatically select appropriate content complexity, substituting human judgment and manual effort with computational analysis and automated decision-making.
2Adaptability or versatility
If manual content customization is performed, then content can be adapted to audience, but the modifications are subjective and depend on presenter preferences
Solution Approach 1:
The system incorporates feedback loops where user engagement metrics and comprehension data are continuously collected and used to refine content selection. The machine learning model learns from user responses and engagement patterns, objectively adjusting content complexity based on actual performance data rather than subjective presenter assumptions about audience capabilities.
Solution Approach 2:
The patent replaces subjective human judgment with objective machine learning algorithms that analyze user profiles, lexical knowledge indicators, and engagement metrics to determine appropriate content complexity. This substitution eliminates presenter biases and personal preferences, providing consistent, data-driven content adaptation.
3Device complexity
If static content selection is used, then content delivery is simple, but user engagement decreases when content complexity does not match audience level
Solution Approach 1:
The system implements dynamic content selection that adapts to individual user characteristics and real-time engagement levels. The machine learning model continuously adjusts document complexity based on user lexical knowledge assessments and ongoing engagement metrics, transforming static content delivery into a dynamic, responsive system that maintains user interest and comprehension.
Solution Approach 2:
The patent changes the parameter of content complexity dynamically based on user profiles and engagement data. The system adjusts lexical difficulty, document length, and content depth as variables that are modified in real-time according to user capabilities and engagement levels, rather than using fixed content selections.
4Adaptability or versatility
If content complexity is increased for graduate students, then complex discussions can be delivered, but simplified content is needed for young children
Solution Approach 1:
The system segments the audience into distinct groups based on lexical knowledge levels, age, education background, and engagement patterns. The machine learning model creates separate content pathways for different user segments (e.g., young children, graduate students), selecting from appropriate document complexity levels for each segment rather than using a one-size-fits-all approach.
Solution Approach 2:
The patent applies parameter changes by adjusting content complexity variables according to user segment characteristics. The system modifies lexical difficulty, sentence structure, and conceptual depth parameters based on the target audience's educational level and comprehension capabilities, enabling appropriate content delivery across diverse user groups.
Data Source
AI summary
Techniques for machine learning analysis are provided. A machine learning (ML) model is trained to identify appropriate documents based on lexical knowledge of target groups. A lexical knowledge of a set of users is determined. Additionally, a first document of a plurality of documents is selected by processing the determined level of lexical knowledge using the ML model. The first document is presented to the set of users. A level of engagement of the set of users is then determined. Upon determining that the level of engagement is below a predefined threshold, a second document of the plurality of documents is selected using the ML model.