Adaptive Media Content Modification Using Comprehension Feedback
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Solution Overview
Problem
Devices presenting content, such as e-books or stories, may consume unnecessary resources when users are unable to comprehend the text, leading to a suboptimal user experience and increased power consumption.
Innovation Solution
A device dynamically modifies content based on user comprehension by generating questions related to the text, adjusting complexity, and using generative models to simplify or enhance content accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the device presents content continuously without modification, then the content delivery is simple and device complexity is low, but user comprehension may be insufficient and resource consumption increases
Solution Approach 1:
The system dynamically modifies content based on real-time user comprehension assessment. Questions are generated and evaluated during content delivery, and the system adjusts content complexity adaptively. This dynamic approach ensures energy efficiency by stopping content delivery when comprehension is achieved, while maintaining manageable complexity through automated AI-based evaluation and modification processes.
Solution Approach 2:
The system implements a feedback loop where user responses to comprehension questions are evaluated, and content modification decisions are made based on this feedback. The AI model assesses user answers and determines whether to continue, modify, or stop content delivery. This feedback mechanism optimizes energy consumption by avoiding unnecessary content delivery while the feedback processing is automated to prevent excessive system complexity.
2Reliability
If the device generates and evaluates questions to assess user comprehension, then user engagement and comprehension improve, but device complexity and processing requirements increase
Solution Approach 1:
The system uses an AI model as an intermediary to generate and evaluate comprehension questions. The AI model acts as a mediator between the content delivery system and the user assessment process. This intermediary approach ensures reliable comprehension evaluation by leveraging advanced AI capabilities while preventing excessive device complexity by offloading the complex question generation and evaluation tasks to the AI model rather than requiring complex native device implementation.
3Adaptability or versatility
If the device modifies content complexity based on user performance, then adaptability to user needs improves, but system complexity and processing requirements increase
Solution Approach 1:
The system modifies content by changing parameters such as text complexity, question difficulty, and content delivery pace based on user performance. The AI model evaluates user responses and adjusts content parameters dynamically. This parameter-based approach ensures high adaptability to individual user needs while maintaining manageable system complexity by using automated AI-driven parameter adjustment rather than requiring complex manual content modification systems.
Data Source
AI summary
A method includes displaying, on a display, text that corresponds to a portion of a media content item. The method includes, after displaying the text on the display, displaying, on the display, a question that relates to the text in order to determine whether a user of the device is comprehending the text. The method includes receiving a user input in response to displaying the question. The method includes modifying content of the media content item based on an evaluation of the user input.