Knowledge Harvesting System (KHS): A Next-Generation Framework for Inclusive and Accessible Recognition of Prior Learning in the Digital Knowledge Era

This concept paper introduces the Knowledge Harvesting System (KHS)—a Next-Generation Recognition of Prior Learning (RPL) framework developed by Gqira “Israel” Kabane of Stone of David Projects Primary Co-operative and the SA College of Business & Language. The KHS redefines RPL as both a process of recognition and knowledge creation. It integrates Learning Management Systems (LMS), Synthetic Intelligence (SI), and Distributed Ledger Technology (DLT) to form an inclusive, transparent, and ethically governed platform for lifelong learning and knowledge contribution. The system is designed for universal accessibility, ensuring equitable participation for persons differently abled to communicate or perceive. Participants may demonstrate competence through writing, speech, gesture, sign language, or physical demonstration—broadening the scope of what counts as knowledge in both academic and professional domains. Aligned with UNESCO’s Global Convention on the Recognition of Qualifications (2023), CEDEFOP’s European Guidelines (2023), and South Africa’s CHE (2016) and DHET (2016; amended 2023) RPL Policies, the KHS advances the UN Sustainable Development Goals (SDG 4 and SDG 8) by enabling inclusive, digitally verifiable recognition for all forms of learning. The paper calls for strategic partnerships among educational institutions, government agencies, and technology developers to build Africa’s first open, ethically governed digital knowledge infrastructure—empowering every capable individual to contribute meaningfully to the global body of knowledge.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-21
DOI
https://doi.org/10.5281/zenodo.17410360
Primary Topic
Diverse Scientific and Economic Studies
Type
article
Field-Weighted Citation Impact
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article

Knowledge Harvesting System (KHS): A Next-Generation Framework for Inclusive and Accessible Recognition of Prior Learning in the Digital Knowledge Era

Zenodo (CERN European Organization for Nuclear Research)
Diverse Scientific and Economic Studies
article

Knowledge Harvesting System (KHS): A Next-Generation Framework for Inclusive and Accessible Recognition of Prior Learning in the Digital Knowledge Era

article en

Abstract

This concept paper introduces the Knowledge Harvesting System (KHS)—a Next-Generation Recognition of Prior Learning (RPL) framework developed by Gqira “Israel” Kabane of Stone of David Projects Primary Co-operative and the SA College of Business & Language. The KHS redefines RPL as both a process of recognition and knowledge creation. It integrates Learning Management Systems (LMS), Synthetic Intelligence (SI), and Distributed Ledger Technology (DLT) to form an inclusive, transparent, and ethically governed platform for lifelong learning and knowledge contribution. The system is designed for universal accessibility, ensuring equitable participation for persons differently abled to communicate or perceive. Participants may demonstrate competence through writing, speech, gesture, sign language, or physical demonstration—broadening the scope of what counts as knowledge in both academic and professional domains. Aligned with UNESCO’s Global Convention on the Recognition of Qualifications (2023), CEDEFOP’s European Guidelines (2023), and South Africa’s CHE (2016) and DHET (2016; amended 2023) RPL Policies, the KHS advances the UN Sustainable Development Goals (SDG 4 and SDG 8) by enabling inclusive, digitally verifiable recognition for all forms of learning. The paper calls for strategic partnerships among educational institutions, government agencies, and technology developers to build Africa’s first open, ethically governed digital knowledge infrastructure—empowering every capable individual to contribute meaningfully to the global body of knowledge.

Zenodo (CERN European Organization for Nuclear Research)
Co-operative College (GB)
Partnerships for the goals
Openalex Percentile: Top 32%
Diverse Scientific and Economic Studies
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