Open international standards related to Knowledge Token® and blockchain in the field of education are developed by Knowledge Foundation members through Working Groups (WGs) and Technology Working Groups (TWGs) that are chaired by faculty, administrators, and researchers.
TWGs develop, deliver and maintain technological materials such as technical reports, design documents, specifications, reference implementations, software implementations, conformance test suites, best practices, curricula, usage guides and reviews of deliverables produced by other TWGs.
In addition to the following Technology Working Groups the Legal Working Group (L.WG) and Economics and Tokenomics Working Group (ET.WG) address these related non-technical matters.
Learner incentive and reward mechanisms, including the specification of dynamic learning activity rewards. The Knowledge Token® system incentivizes and rewards learners with cryptographically secure digital tokens in amounts that correspond to the amount or units of knowledge, skills and/or credits that learners earn through the successful completion of tasks and activities using digital software (i.e., websites, software applications, mobile apps, etc.) and/or non-digital means (traditional non-digital classes or courses, hands-on training, pencil-and-paper assignments and examinations, etc.). LIR.TWG addresses the corresponding learner incentive/reward mechanisms and the mechanisms necessary to enable reward values that may be dynamically adjusted on an ongoing basis.
Artificial Intelligence (AI) applied to the field of education with a particular focus on AI as it pertains to the construction, dissemination and analysis of personalized educational content in the context of Knowledge Token®. Members of AIE.TWG are responsible for evaluating, developing and standardizing open platforms, policies and procedures for: AI-based learning guides and companions; AI-generated learning plans, learning paths and learning experiences; AI-mediated instructional systems and personalized tutoring systems; AI-powered chatbots, virtual tutors and related learner engagement and assistance systems; AI-enabled adaptive learning experiences that dynamically and continually adjust to individual learning needs; AI-enabled curriculum design tailored to the specific needs of the individual learner; AI-based grading, evaluation and assessment of learner progress and performance; applying AI to the dynamic and ongoing construction, delivery and mediation of personalized and deeply engaging immersive learning experiences.
Blockchain data analytics and visualization capabilities to provide insights into learner behaviors and fine-grained usage of Knowledge Tokens on a learner-by-learner basis, specifically for the personal benefit of learners. AAV.TWG members determine the analytics and visualization features and capabilities that give rise to token spending insights, education path finding, career path finding, and macro/micro learning trends and forecasting. It is understood that the same level and approach to security and privacy that is required for the Knowledge Token® project will likewise be required for any and all analytics and reporting features of the system.
Publishing and protecting student (learner) data on public blockchains to ensure that learner identity and learner records, including academic transcripts and achievements (scores, grades, performance, and so forth), remain the private property of the learner while also conforming to corresponding regulations such as the European Union General Data Protection Regulation (GDPR). To this end the members of PLD.TWG also address the need of learners to privately share their learning data, in whole or in part, with individuals or organizations in a manner consistent with the overarching goals of this TWG.
Scaling blockchain platforms, and corresponding non-fungible token (NFT) architectures, to handle "millions to billions" of learners concurrently transacting Knowledge Tokens at a high frequency. M2B.TWG assumes that each learner conducts multiple token transactions daily, resulting in extreme token velocity at scale. To this end M2B.TWG members also address NFT denominations to enable Knowledge Tokens to be aggregated (into larger denominations) and divided (into smaller denominations, such as by "spending" a fraction of a token) in a manner similar to traditional currency.
Authenticating learners to reduce fraudulent activities. Members of M2B.TWG address the periodic and continuous authentication of learners in order to reduce, to the extent possible, fraudulent use of the system. Periodic one-time authentication measures enable ad hoc learner sign-in (to participate in a learning activity or to view their account, for example), while continuous authentication authenticates learners over the entire duration of a learning activity. In both cases a variety of authentication techniques will be investigated for suitability, including the use of unique biometric data (voice, fingerprints, facial features, and so forth) and learner behavior patterns (such as mouse activity and keystroke patterns, for example).
Backup and recovery of critical blockchain data and contents, including 3rd party custodial provisions to accommodate learners who lose access to their sign-in credentials and/or device(s). BAR.TWG members establish the technology measures and corresponding procedures necessary to enable system-wide disaster recovery (reconstituting the entire production blockchain on a backup blockchain platform, for example) as well as individual learner account and token backup and recovery. In this way, learner accounts and corresponding tokens and learner data must be considered to be “recoverable” such that learners are not in danger of permanently losing access to their tokens or data due to mishandling, loss, or destruction of access credentials.