Customized Learning Program Generation via Role-Based Material Selection
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Solution Overview
Problem
Conventional systems are inadequate in generating customized learning programs that cater to the specific needs of new team members, leading to increased burden on team leaders and lower training completion rates due to differences in prior knowledge and team roles.
Innovation Solution
A system and method for generating customized learning programs by selecting and arranging learning materials based on user roles, generating tailored learning steps, and deploying multiple customized programs using processors, which includes creating verification processes, setting visibility properties, and updating learning program ontologies for progress tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual direction is used to assign learning materials to new members, then customization to individual needs is improved, but the burden on team leaders increases
Solution Approach 1:
The system enables self-service by automatically assigning learning materials based on user profiles and roles. The platform autonomously processes user information, selects appropriate learning content, and delivers customized programs without requiring manual intervention from team leaders, thus resolving the contradiction between customization and managerial burden
Solution Approach 2:
The patent replaces the mechanical manual assignment process with an automated electronic system. The system uses algorithms to process user data, select learning materials, and deliver customized programs, substituting human manual operations with automated computational processes that achieve the same customization goal without the associated burden
2Device complexity
If a single standardized learning program is used for all new members, then the system complexity is reduced, but the training quality and completion rates decrease
Solution Approach 1:
The system applies local quality by tailoring learning programs to specific user characteristics, roles, and needs rather than applying a uniform standardized program. Each user receives customized learning materials and pathways appropriate to their individual circumstances, improving completion rates while maintaining manageable system complexity through automated personalization
Solution Approach 2:
The patent implements dynamics by creating adaptive learning programs that adjust based on user progress, preferences, and roles. The system dynamically modifies learning pathways and material selection in response to user interactions and performance data, transforming static standardized programs into dynamic personalized experiences that improve completion rates
3Measurement precision
If learning materials are manually selected and organized, then the customization accuracy is improved, but the time required for program generation increases
Solution Approach 1:
The system performs preliminary action by pre-organizing learning materials into a structured repository with metadata tags and classification systems. This pre-processing enables the automated system to quickly select and assemble customized programs without time-consuming manual organization, achieving both high customization accuracy and efficient program generation time
Solution Approach 2:
The patent replaces manual material selection and organization with automated computational processes. The system uses algorithms to search, select, and assemble learning materials based on user profiles, substituting manual curation with automated decision-making that achieves precise customization rapidly without human time investment
Data Source
AI summary
Methods and systems for generating customized learning programs include performing the operations of: receiving learning materials for a target platform; selecting a first set of learning materials based at least in part upon a first role; generating a first customized learning program which includes one or more first learning steps using the first set of learning materials; selecting a second set of learning materials based at least in part upon a second role, the second role being different from the first role, the second set of learning materials being different from the first set of learning materials; generating a second customized learning program which includes one or more second learning steps using the second set of learning materials; and deploying customized learning programs which include the first customized learning program and the second customized learning program.