US-20260127440-A1 - System and Method for Progressive Learning Using Iterative Assessment and Generation

US20260127440A1US 20260127440 A1US20260127440 A1US 20260127440A1US-20260127440-A1

Abstract

A system and method for progressive learning using iterative assessment and generation is provided. The system employs transformer-based models to generate domain-specific learning interactions and assess responses, with parameters being continuously updated based on effectiveness metrics and demonstrated mastery. An iterative learning cycle generates interactions, evaluates responses to determine both knowledge states and interaction quality, and updates system parameters accordingly, progressively improving both learning interactions and knowledge states. The system supports dual-mode operation for both human and machine learning, with a meta-learning framework that analyzes patterns across multiple domains, transfers successful learning strategies, and continuously enhances teaching effectiveness through systematic analysis of cross-domain learning outcomes. The invention enables simultaneous improvement of learning materials and learner knowledge, creating synergies between human and machine learning approaches.

Inventors

Theodore Orlin Cochran

Assignees

Theodore Orlin Cochran

Dates

Publication Date: 20260507
Application Date: 20250321

Claims (13)

1 . A computer-implemented method for progressive learning, comprising: receiving a domain specification defining a field of expertise; initializing a transformer-based content generation model and an assessment model; executing an iterative learning cycle by: generating, by the content generation model, a learning interaction comprising at least one of an assessment, an explanation, or an exercise; receiving a response to the learning interaction; evaluating, by the assessment model, the response to determine a knowledge state indicated by the response and a quality metric for the generated learning interaction; updating both parameters controlling subsequent content generation based on the quality metric and a difficulty level for subsequent learning interactions based on the knowledge state; and repeating the iterative learning cycle with the updated parameters and difficulty level, wherein the learning cycle progressively improves both the effectiveness of generated learning interactions and the knowledge state of the responding entity.
2 . A system for unified human and machine learning, comprising: one or more processors; memory storing instructions that, when executed, cause the system to: maintain a transformer-based model capable of generating domain-specific content and assessing responses to the generated content; operate in at least two modes comprising a human learning mode wherein generated content facilitates human expertise development and a machine learning mode wherein generated content trains other artificial intelligence systems; for each mode: generate progressive learning interactions of increasing complexity, evaluate responses to determine both response quality and interaction effectiveness, and adjust subsequent content generation based on the evaluation, wherein the system improves its content generation capabilities through analysis of interactions in both modes.
3 . A method for domain-independent expertise development, comprising: receiving specifications for multiple domains of expertise and corresponding success criteria; initializing a meta-learning framework comprising a cross-domain content generation component using transformer-based language models and a multi-domain assessment component; executing parallel learning cycles across different domains that synthesize common patterns, transfer successful strategies between domains, and optimize teaching methodologies based on cross-domain effectiveness; analyzing learning outcomes across domains to identify universal learning patterns; and automatically adapting content generation and assessment strategies based on aggregate cross-domain performance metrics, wherein the framework continuously enhances its teaching effectiveness through systematic analysis of learning patterns across multiple domains of expertise.
4 . The method of claim 1 , wherein the quality metric comprises: an engagement level with the learning interaction; an effectiveness at knowledge transfer; an appropriateness of difficulty level; and a clarity of presentation.
5 . The method of claim 1 , wherein updating parameters comprises: adjusting attention weights in the transformer model; modifying prompt templates; refining difficulty progression curves; and updating knowledge state representations.
6 . The method of claim 1 , further comprising: maintaining a history of learning interactions; analyzing patterns of successful knowledge transfer; identifying optimal progression pathways; and adapting generation strategies based on historical effectiveness.
7 . The system of claim 2 , wherein operating in human learning mode comprises: generating natural language explanations; providing interactive exercises; offering contextual feedback; and adapting to individual learning styles.
8 . The system of claim 2 , wherein operating in machine learning mode comprises: generating structured training data; creating quantifiable assessment criteria; providing performance metrics; and optimizing learning parameters through reinforcement learning.
9 . The method of claim 3 , wherein the cross-domain assessment component: tracks multiple dimensions of expertise across domains; identifies common knowledge patterns; predicts optimal learning pathways; and measures cross-domain learning efficiency.
10 . The method of claim 3 , wherein analyzing learning outcomes comprises: identifying universal teaching patterns; optimizing progression strategies across domains; adapting to diverse learning approaches; and improving content generation through meta-analysis.
11 . The method of claim 3 , wherein the framework: automatically transfers successful learning strategies between domains; identifies domain-independent expertise development patterns; continuously optimizes teaching methodology based on cross-domain results; and improves assessment accuracy through pattern recognition.
12 . The system of claim 2 , further comprising a real-time adaptation component that: continuously monitors learner engagement; dynamically adjusts difficulty levels; adapts content generation parameters in real-time; and incorporates immediate feedback into the learning cycle.
13 . The method of claim 1 , further comprising collaborative learning capabilities that: share effective teaching strategies across multiple instances of the system; aggregate effectiveness metrics across different domains; identify universal learning patterns through statistical analysis; and optimize knowledge transfer through collaborative refinement.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS [Not applicable—this is a new application with no related applications] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT [Not applicable—no federal sponsorship] NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT [Not applicable—no joint research agreement] BACKGROUND OF THE INVENTION Field of the Invention The present invention relates generally to computer-implemented learning systems and, more particularly, to systems and methods for progressive learning through iterative content generation and assessment. Description of Related Art Traditional learning systems typically employ static content delivery methods that fail to adapt effectively to individual learning needs or evolving knowledge domains. Many existing systems separate content generation from assessment, treating them as distinct processes rather than complementary components of a unified learning cycle. Existing adaptive learning technologies often rely on pre-defined content repositories with limited ability to generate novel learning materials. While some systems incorporate basic personalization, they typically lack sophisticated mechanisms for progressive difficulty adjustment based on demonstrated mastery. Machine learning approaches have increasingly been applied to educational technology, yet most focus exclusively on either human learning or machine training, without recognizing the potential synergies between these domains. Traditional systems that do employ machine learning often rely on conventional neural network architectures rather than leveraging transformer-based models for both content generation and assessment. There remains a need for learning systems that can seamlessly integrate content generation and assessment in a continuous, iterative cycle that progressively improves both the quality of learning interactions and the knowledge state of learners, whether human or machine. BRIEF SUMMARY OF THE INVENTION The present invention provides systems and methods for progressive learning through iterative content generation and assessment. In various embodiments, the invention employs transformer-based models to generate domain-specific learning interactions and assess responses, with parameters being continuously updated based on effectiveness metrics and demonstrated mastery. In one aspect, the invention comprises a computer-implemented method for progressive learning that executes an iterative learning cycle. The cycle generates learning interactions, evaluates responses to determine both knowledge states and interaction quality, and updates system parameters accordingly. This cyclical process progressively improves both the effectiveness of the learning interactions and the knowledge state of the responding entity. In another aspect, the invention provides a unified system for both human and machine learning. The system maintains a transformer-based model capable of operating in dual modes: a human learning mode that facilitates expertise development and a machine learning mode that trains other artificial intelligence systems. The system improves its content generation capabilities through analysis of interactions in both modes. In yet another aspect, the invention implements a meta-learning framework for domain-independent expertise development. The framework analyzes patterns across multiple domains, transfers successful learning strategies, and continuously enhances its teaching effectiveness through systematic analysis of cross-domain learning outcomes. The present invention represents a significant advancement over prior art by integrating generation and assessment in a unified learning framework, supporting both human and machine learning through the same underlying architecture, and implementing meta-learning capabilities for cross-domain optimization. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram illustrating a system architecture overview. FIG. 2 is a flow diagram depicting an iterative learning cycle. FIG. 3 is a block diagram showing dual-mode operation for human and machine learning. FIG. 4 is a graph illustrating progressive difficulty adjustment over time. FIG. 5 is a block diagram depicting knowledge state tracking through interconnected nodes. FIG. 6 is a block diagram illustrating a meta-learning framework with cross-domain transfer. FIG. 7 is a flow diagram showing the parameter update process. FIG. 8 is a block diagram depicting the layered system implementation architecture. DETAILED DESCRIPTION OF THE INVENTION The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which preferred embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the sco