Adaptive AI Course Templates Using Real-Time Learner Feedback

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Solution Overview

Problem

Existing course authoring tools lack the ability to dynamically adapt course content to diverse learner needs, leading to suboptimal designs and inefficient manual update processes, and fail to effectively utilize real-time data for personalized learning experiences.

Innovation Solution

An AI-driven system that utilizes real-time feedback to generate adaptive course templates, employing predictive analytics and machine learning algorithms to personalize learning paths and continuously refine content based on learner interactions and performance data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If manual course design and update processes are used, then educators can create customized course content, but the process is time-consuming and inefficient

Engineering Contradiction:

Improveease of course creationVSAvoidtime for course updates

Core Design Contradiction:

Ease of manufactureVSLoss of time

Solution Approach 1:

The system enables self-service through AI-driven automated course template generation. The AI engine autonomously creates course templates by analyzing learner data, identifying patterns, and generating recommendations without requiring manual educator intervention for each course creation task, thereby reducing time investment while maintaining customization.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements continuous feedback loops where learner interactions and performance data are collected in real-time, analyzed by the AI engine, and used to dynamically update and refine course templates. This automated feedback mechanism eliminates manual update cycles and enables continuous improvement of course content based on actual learner outcomes.

Inventive Principle:

Principle #23Feedback

2Stability of the object's composition

If static course templates are used, then course structure is maintained, but the content cannot adapt to diverse learner needs

Engineering Contradiction:

Improvecourse structure stabilityVSAvoidadaptability to learner needs

Core Design Contradiction:

Stability of the object's compositionVSAdaptability or versatility

Solution Approach 1:

The system transforms static course templates into dynamic, adaptive structures. The AI engine continuously modifies template elements based on real-time learner data, enabling the course structure to evolve and adapt to diverse learner needs while maintaining overall organizational stability through systematic update processes.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system achieves adaptability by dynamically changing template parameters such as content recommendations, learning paths, and resource allocations based on analyzed learner patterns. The AI engine adjusts these parameters in real-time to optimize course content for individual learner needs while preserving the fundamental course framework.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If traditional course authoring tools are used, then implementation is straightforward, but real-time data utilization for personalization is insufficient

Engineering Contradiction:

Improveease of tool usageVSAvoidreal-time data utilization

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The system replaces traditional mechanical course authoring processes with AI-driven automated systems. The AI engine autonomously processes learner data, identifies patterns, generates recommendations, and updates templates without manual intervention, thereby capturing and utilizing real-time data for personalization while maintaining ease of operation through automated workflows.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Adaptability or versatility

If personalized learning paths are implemented, then learner engagement improves, but system complexity increases

Engineering Contradiction:

Improvepersonalization capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system achieves personalization through a universal AI engine that handles multiple functions: data collection, pattern recognition, recommendation generation, and template updates. This multi-functional approach enables personalized learning paths across diverse courses and learner types without proportionally increasing system complexity, as the same core AI infrastructure serves all personalization needs.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250378518A1Systems and methods for generating adaptive artificial intelligence-based course templates using real-time feedback

Publication Date: 2025.12.11 PEARSON EDUCATION INC

AI summary

Systems and methods for adaptive artificial intelligence-based course template generation. One system may include a processing system configured to: receive a request to generate a first course template for a course; identify, with an artificial intelligence (AI) engine, user data that is contextually relevant to the request; synthesize, with the AI engine, the user data to determine a set of patterns for the user data; generate, with the AI engine, a set of recommendations based on the set of patterns; generate, based on the set of recommendations, a first course template for the course; generate a first set of learning course content that adheres to the first course template for the course; and transmit the first set of learning course content to a client device for display as a learning course content rendering via a graphical user interface.