Prior Learning Credit Assignment Using NLP Equivalency Matching

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Solution Overview

Problem

Existing academic systems fail to efficiently assign credit for prior learning, such as previous academic, work, or volunteer experiences, leading to redundant course-taking for students.

Innovation Solution

A method and system that utilizes natural language processing (NLP) to compare a student's academic, work, and volunteer histories with prospective course curriculums, determining equivalencies through embeddings and cosine similarity, and assigning credits based on these comparisons.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If students re-take courses to ensure mastery of material, then learning reliability is improved, but time consumption increases

Engineering Contradiction:

Improvelearning reliabilityVSAvoidtime consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of student prior learning experiences (work history, volunteer history, academic history) before formal course enrollment. By pre-comparing these experiences against course outcomes using NLP and embedding techniques, the system identifies equivalent prior learning and assigns credit in advance, eliminating the need for students to re-take courses they have already mastered through experience.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If manual review processes are used to evaluate prior learning, then assessment accuracy is improved, but processing efficiency deteriorates

Engineering Contradiction:

Improveassessment accuracyVSAvoidprocessing efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system replaces manual mechanical review processes with automated NLP-based analysis. The system parses student history data, generates embeddings from text descriptions of experiences, and compares these embeddings with course outcome descriptions using cosine similarity calculations. This automated approach maintains assessment accuracy by using sophisticated language understanding while dramatically improving processing efficiency and reducing administrative burden.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If comprehensive course curricula are maintained to ensure educational quality, then educational standards are improved, but student workload increases

Engineering Contradiction:

Improveeducational standardsVSAvoidstudent workload

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system extracts and identifies specific portions of the curriculum that students have already mastered through prior learning experiences. By comparing student histories against course outcomes, the system determines which specific courses or course components can be removed from the student's required curriculum, allowing comprehensive educational standards to be maintained for courses that are actually needed while eliminating redundant coursework.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20260017740A1Method and system for assigning credit for prior learning

Publication Date: 2026.01.15 KNOWMEQ INC

AI summary

The disclosure is directed at a method and system for assigning credit for prior learning where the learning may be academics, from working or from life experience. In some embodiments, the disclosure may be seen as a system for awarding credit for prior learning. The disclosure assists administrators accelerate the process of reviewing applications and helps students immediately know how their experience would translate to course credits.