AI-Generated Instructor Avatars for Scalable Personalized Feedback
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Solution Overview
Problem
Conventional instructional applications fail to provide customized feedback to users when instructional media is livestreamed to a large number of users, and users cannot receive personalized feedback when viewing recorded versions of the media.
Innovation Solution
A computing system generates user-customized portions of instructional media using a computer-implemented model based on user data and audiovisual data of the instructor, allowing for personalized feedback without the instructor's manual input, utilizing deepfake technology to create synchronized audio and video content that appears as if the instructor is addressing the user individually.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If instructional media is livestreamed to a large number of users, then the reach and accessibility of instructional content is improved, but the ability to provide customized feedback to each user deteriorates
Solution Approach 1:
The system creates synthetic copies of the instructor's audiovisual appearance and voice to deliver personalized feedback to multiple users simultaneously. Instead of requiring the actual instructor to provide individualized feedback to each user, the system generates customized feedback messages and synthesizes them through AI-generated audiovisual representations of the instructor, enabling scalable personalized interaction.
Solution Approach 2:
An intermediary system comprising audio synthesis and video synthesis components is introduced between the instructor and the users. This intermediary automatically generates personalized feedback messages based on user performance data and synthesizes audiovisual content that appears to come from the instructor, resolving the conflict between serving many users and providing customized feedback.
2Adaptability or versatility
If the instructor provides manual feedback to each user, then the quality of personalized interaction is improved, but the time and resources required deteriorate
Solution Approach 1:
The system enables self-service by automatically generating personalized feedback without requiring instructor intervention. The feedback generation system collects user performance data, formulates customized feedback messages, and synthesizes audiovisual content autonomously, freeing the instructor from time-consuming manual feedback tasks while maintaining personalized interaction quality.
Solution Approach 2:
The manual mechanical process of the instructor providing feedback is replaced with an automated computational system. Instead of the instructor manually crafting and delivering feedback to each user, the system uses algorithms to generate feedback messages and AI synthesis to create audiovisual content, substituting human effort with automated processing.
3Ease of operation
If recorded instructional media is provided to users, then the accessibility and availability of content is improved, but the ability to provide customized feedback deteriorates
Solution Approach 1:
The system transforms static recorded instructional media into dynamic, adaptive content by overlaying synthesized audiovisual feedback on top of the recorded material. While the base instructional content remains fixed and accessible, the system dynamically generates personalized feedback messages and synthesizes them through AI to match each user's performance, making the experience adaptive without sacrificing accessibility.
Data Source
AI summary
A computing system causes instructional media to be played on a device to a user. An instructor in the instructional media provides guidance as to how to perform an activity when the instructional media is played on the device. The computing system obtains user data pertaining to performance of the activity by the user. The computing system generates a user-customized portion of the instructional media based upon the user data and a computer-implemented model. The computing system causes the user-customized portion to be played on the device to the user, where the device emits audible words reproduced in a voice of the instructor, where the audible words are based upon the user data, and further where the device displays generated images of the instructor depicting the instructor speaking the audible words as the device emits the audible words.