Search arXiv⌕ Search

arXiv · 2312.12268

Web 3.0 and a Decentralized Approach to Education

Abstract

With the natural evolution of the web, the need for decentralization has rendered the current centralized education system out of date. The student does not "own" their credentials, as the only way their accomplishments are directly linked to their person and considered valuable is by verification through a stamp of an expensive, prestigious institution. However, going to a university is no longer the only way to acquire an education; open-source learning material is widely available and accessible through the internet. However, our society does not deem these methods of education as verifiable if they do not include a degree or certificate. Additionally, a valid certificate for the vast majority of open-source courses costs a few hundred dollars to obtain. The centralized nature of education inadvertently places students in underprivileged communities at a disadvantage in comparison to students in economically advantaged communities, thus a decentralized approach to education would eliminate the vast majority of such discrepancies. In the present paper, we integrate Decentralized Identity (DID) with Web 3.0 to upload credentials linked directly to the user. Each credential is appended to an Ethereum blockchain that, by design, cannot be altered once uploaded. We include DID document based access controls to display the candidate's upload and verification history. Finally, we utilize TLS protocols to provide a secure connection to the internet for ensuring non-fungibility of credentials and authentication of users.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sarah A. Flanery, Kamalesh Mohanasundar, Christiana Chamon, Srujan D. Kotikela, Francis K. Quek. 2023-12-19. Web 3.0 and a Decentralized Approach to Education. https://arxiv.org/abs/2312.12268

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

The Cross-Section of Stock Returns and AI Exposure

We study 380 trillion tokens of realized AI consumption across more than four hundred LLMs. We build a high-frequency AI factor and show that a long-short strategy based on firms' AI exposure earns significantly positive returns. The average strategy return is larger based on intensive, frontier-oriented AI consumption but smaller based on casual or open-weight usage. Internationally, the return spread is significant in developed countries but insignificant in emerging markets. Examining occupational AI exposure, we find more positive exposure in occupations intensive in nonroutine interactive tasks and more negative exposure in those intensive in nonroutine analytical tasks.

cs.CY↗

What fidelity metrics miss: a structural check on synthetic educational data

Secondary use of educational records is increasingly mediated by platforms that share a differentially private synthetic version of a dataset and validate specific findings against the real data on request. The synthetic version is evaluated by comparing summary statistics of each variable, yet reported confirmation rates suggest that such comparisons do not predict which findings survive. We propose a structural check: the number of connected components of a weekly proximity graph over learners, tracked across a term. Across four annual cohorts of lower-secondary study-habit logs, the synthetic versions reproduced the level of this quantity and the shape of the weekly partition, but its variation across the term was between 2.6 and 4.9 times smaller than in the real data at a common working point, without exception, and those changes fell in different weeks: the synthetic cohorts single out the term's examination weeks and the real cohorts do not. We also show that a routine rule for setting the graph threshold makes naive comparisons between two datasets invalid, and illustrate this with an error of our own. The real curves are also distinguishable from marginal-preserving surrogates of themselves in all four cohorts, where three of the four synthetic ones are not, a comparison that needs no real data; these differences trace to what the generator was given.

cs.CY↗

AI-Moderated Interviews for Market Research and Digital Twins Calibration

AI-moderated interviews are emerging as a scalable market-research method for generating consumer insights and building consumer "digital twins." Yet it remains unclear whether they match human-moderated interviews or improve on simpler, static data collection methods. In a pre-registered, between-subjects study (N = 317) with three industry partners, we compare AI-moderated (N = 139), human-moderated (N = 24), and static interviews (N = 154). AI moderation matches human moderation in depth, covers more themes, and, holding budget constant, recovers significantly more customer needs than human moderation or static interviews. However, participants sound more emotionally engaged when speaking to a live human. We then create digital twins using interview data and evaluate each twin against the participant's own held-out responses to six real-world marketing stimuli. We find that digital twins created from AI-moderated interviews predict consumer responses better than demographics-only personas. However, the additional richness from AI moderation does not translate into better quantitative predictions compared to static interviews. By analyzing open-ended thoughts generated from humans versus their twins, we find that prediction errors are connected both to differences in (self-reported) thinking styles between twins and humans, and to gaps between training and validation data (i.e., asking questions that are too far out of distribution).

cs.CY↗