D. Chandravathi, M. Swetha, S.K. Khaja Shareef, Apathi Haripriya · 6 authors
Mass adoption of online learning and remote assessment has created significant challenges to maintaining exam integrity, verifying examinees, and ensuring veracity in scores. Existing solutions are frequently not based on consistent and robust validation mechanisms, and it is easy to commit impersonation, cheating and manipulation of the results. This research proposes deployment of a blockchain-based trust architecture to improve security in online examination systems by using smart validation. The primary objective is to create an all-inclusive system that ensures the veracity of exam takers and the validity of exam records when integrating biometric, behavioral, and the use of smart contract automation. We created a dataset of 50 scenarios from exams that included real world elements (face match confidence, keystroke patters, gaze tracking difference, device information and time logs). Deep learning algorithms were used on the dataset for session classification while dynamic validation criteria were applied by smart contracts and a private Ethereum blockchain for validated results maintained. Four main methods were applied to review the proposed model: rule-based logic, random forest, logistic regression and support vector machine (SVM) models. The outcome showed that the proposed technique gave an accuracy of 92%, an F1-score of 0.92 and a ROC-AUC of 0.95 which is lower than the other approaches. By examining confusion matrices and performing statistical tests, it was proved that the suggested model is both robust and generalizable and it has an unbelievably low p-value of 1.95 × 10−20in comparison with the weakest baseline. This research offers a scalable framework for handling e-proctored high stakes assessments, which is secure and auditable to overcome the current limitations and advances reliable digital educational platforms.
Academic integrity and plagiarism
Intelligent Tutoring Systems and Adaptive Learning
This paper examines the growing challenges of verifying and validating micro-credentials within increasingly fragmented educational ecosystems. As traditional educational pathways diversify into modular learning experiences, the need for reliable verification mechanisms becomes critical for workforce mobility and lifelong learning recognition. Through comparative and inductive analysis, this research identifies key vulnerabilities in current micro-credential systems and proposes a conceptual framework for a multi-layered validation infrastructure. The proposed approach integrates interoperable verification protocols, distributed ledger technologies, and reputation-based validation mechanisms to establish trust across educational institutions, employers, and learners. The framework addresses fundamental challenges including standardization gaps, informal learning validation, and credential falsification risks. While implementation barriers exist related to scalability and cross-cultural acceptance, the multi-faceted validation ecosystem outlined offers promising pathways toward a more flexible, secure recognition system for modular qualifications. The establishment of trusted microcredential ecosystems could significantly enhance labor market efficiency by enabling precise skill verification while democratizing access to recognized qualifications beyond traditional academic institutions.
The rapid advancement of artificial intelligence (AI) and large language models (LLMs) is profoundly reshaping higher education, shifting from institution-centered paradigms to learner-centric personalized learning environments (PLEs). However, PLEs face critical challenges in identity management, including data breaches, unauthorized access, and interoperability barriers, which undermine security and trust. This study proposes the Blockchain-based Student Identity Management System (BSIMS), a conceptual model integrating blockchain technology, xAPI standards, and OAuth2 protocols to uphold confidentiality, integrity, availability, authenticity, and non-repudiation (CIAAN) principles. Grounded in the Technology Acceptance Model (TAM) and Information Systems Success Model (ISSM), BSIMS was validated through mixed-methods research involving 90 students and 16 experts from five Yunnan Province universities. Results demonstrate BSIMS' superiority in user satisfaction (explaining 81.7% variance), CIAAN performance (M=4.58 vs. 3.18 for traditional systems, p<0.001), and reliability (zero downtime, 0.3 ms response time). Ethical and legal implications, such as immutability conflicts with privacy rights, are addressed via zero-knowledge proofs and off-chain storage. BSIMS offers a scalable framework for secure PLEs, advancing educational informatization in Yunnan and beyond.
Online examinations that companies rely on more frequently have made traditional centralized Learning Management Systems (LMS) vulnerable to security threats and authentication problems and result manipulation issues. The place of storing examination data in a central location creates risks for intentional changes which compromises the process fairness as well as integrity. Currently deployed blockchain solutions are ineffective because they fail to deliver both economical solutions and scalable systems that work well with AI proctoring functions. This study introduces the BlockchainBased Examination Framework (BEF) as an integrated system which unites multi-LMS operation with blockchain-based safe storage along with AI-powered examination surveillance features for real-time academic dishonesty discovery. The system utilizes Ethereum together with Hyperledger Fabric and Solana blockchains to guarantee result security and activates Zero-Knowledge Proofs (ZKP) and ECDSA signatures for authentication privacy and implements AI models for live examination monitoring. A thorough examination analyzed speed and scalability and financial efficiency together to evaluate these aspects of the three platform frameworks. Solana demonstrates superior performance through its$\mathbf{6 5, 0 0 0}$Transactions Per Second along with its affordable transaction fee of $0.00025 that makes it the best scalable and efficient choice. The AI-proctoring system demonstrated a 97.8% accuracy level together with a$\mathbf{2. 2 \%}$false positive error rate which improved examination security. The research demonstrates blockchain implementation as a critical enhancement for exam security as well as transparency levels. The future project will concentrate on Ethereum Layer-2 scaling alongside deep learning improvements to AI proctoring systems for better cost reduction and flexibility.
Abdul Razzaq, Ahmed B. Altamimi, Wilayat Khan, Mohammad Alsaffar · 7 authors
CONTEXT: Metaverse is an emerging technology that synchronizes physical and virtual things. It is used to communicate and simulate the virtual world with the physical world through human actions in real-life scenarios. Combining blockchain and metaverse technologies produces an archetype shift in the educational technology domain regarding online certification, largely due to the impact of synchronizing educational technologies. The combined technology elevates the security measure, ensures transparency, enhances accountability, and reduces costs for the online certification process. Proposed Solution: The suggested solution (MetaEduTech) accelerates the certificate verification process by (i) extenuating the risks of misuse by leveraging decentralized storage of the InterPlanetary File System (IPFS), (ii) securing the certificate, and (iii) providing the metaverse environment for certification. We perform experiments and evaluate the MetaEduTech solution by deploying a blockchain-based smart contract model on Ethereum on the Microsoft Windows platform. RESULTS AND IMPLICATIONS: The evaluation results show (i) the efficiency of the query response (5 ms-50 ms), (ii) and the performance of the query execution (CPU utilization between 2%-6%). The findings in this research underscore the effectiveness of the proposed solution with the potential to modernize the certification exam process. The proposed solution and its evaluation can provide insights into how to address the persistent issues surrounding certificate authenticity related to academic verification in a metaverse environment.
Academic publishing, integral to knowledge dissemination and scientific advancement, increasingly faces threats from unethical practices such as unconsented authorship, gift authorship, author ambiguity, and undisclosed conflicts of interest. While existing infrastructures like ORCID effectively disambiguate researcher identities, they fall short in enforcing explicit authorship consent, accurately verifying contributor roles, and robustly detecting conflicts of interest during peer review. To address these shortcomings, this paper introduces a decentralized framework leveraging Self-Sovereign Identity (SSI) and blockchain technology. The proposed model uses Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to securely verify author identities and contributions, reducing ambiguity and ensuring accurate attribution. A blockchain-based trust registry records authorship consent and peer-review activity immutably. Privacy-preserving cryptographic techniques, especially Zero-Knowledge Proofs (ZKPs), support conflict-of-interest detection without revealing sensitive data. Verified authorship metadata and consent records are embedded in publications, increasing transparency. A stakeholder survey of researchers, editors, and reviewers suggests the framework improves ethical compliance and confidence in scholarly communication. This work represents a step toward a more transparent, accountable, and trustworthy academic publishing ecosystem.
M Anjali, Sk. Ashraf Vali, J. Sai Divya, B. Lokesh
Online exams are a general aspect of education nowadays. However, they are still suffering with several security problems such as data breaches, result-changing cheating, and impersonation scams. Some of the modern systems utilize blockchain technology to improve security but come with problems such as being difficult to integrate, taking time to process, being costly, and lacking adequate privacy protection. This work introduces a blockchain-based structure that addresses these concerns. It utilizes smart contracts in a more improvised manner, employs zero-knowledge proofs to secure privacy. The system provides safe sharing of questions, decentralized storage of answers, and tamper-evident grading. It also has seamless integration with learning management systems (LMS). In comparison to traditional and blockchain-based examination systems, this method provides upgrades in security, efficiency, and speed. With the integration of decentralized technology and state-of-the-art security methods, this system provides internet-based exams that are secure, transparent, and economical.
Said Hamisi Said, Ramadhani Sinde, Efraim M. Kosia, Mussa Ally Dida
The prevalence of fake educational credentials poses a threat to the meritocratic nature of the education system and job markets. Verification of certificates to combat forgery has been a challenging endeavor due to the weaknesses of the current methods. Blockchain, capitalizing on its unique attributes, can provide an optimal solution to certification and verification problems by ensuring disintermediation, immutability, tamper-proof, efficiency, and security. Efforts to explore its potential in addressing these problems continue to gain momentum. However, the existing blockchain-based initiatives do not offer a holistic solution to the forgery problem, as they solely focus on a single education level or institution. Furthermore, these initiatives lack the essential features required to fully address this problem. This paper proposes a comprehensive blockchain-enabled system for issuing certificates from different educational levels and institutions in the country, providing a one-stop center for verifiers, such as employers, to verify all certificates a candidate possesses. As a proof of concept, a decentralized application (DApp), ElimuChain, has been developed, utilizing smart contracts and the InterPlanetary File System (IPFS). The system is deployed on the Binance Smart Chain (BSC) blockchain to evaluate its applicability in addressing the problem in the Tanzanian context. The results demonstrate that the proposed solution successfully manages the certification and verification process, and it is cost-effective, scalable, and efficient. Moreover, its performance was compared with the previous solutions in terms of latency and throughput. The comparison results show that it performs better than the counterpart for transactional operations.
Academic honesty is the cornerstone of educational excellence. But challenges related to fraud and record tampering remain. This article explores the application of blockchain technology as a transformative solution to enhance academic integrity in educational institutions. Blockchain's decentralized and immutable ledger provides a secure framework for managing academic credentials. This reduces fraud and ensures transparency. The study begins with an overview of current challenges in managing academic records. It highlights vulnerabilities to counterfeiting and inefficiencies in the verification process. We then propose a blockchain-based system to automate and secure certificate issuance and authentication through smart contracts to existing academic records management systems. A Proof-of-Stake consensus mechanism. It is used to balance network security and integration efficiency. This ensures that stakeholders with significant investments in the system are encouraged to act honestly. Empirical results show that blockchain systems improve data security. Increase transparency and increase efficiency of record management Performance indicators such as transaction throughput Inspection time and the efficiency of the consensus mechanism It emphasizes the system's ability to handle large volumes of data while maintaining operational integrity. This research concludes that blockchain technology offers a robust solution to contemporary challenges in academic integrity. By providing a transparent method effective and more secure academic record management. Additionally, this article suggests avenues for future research. Including scalability and integration with lifelong learning certification.
K C Ankit, Deepesh Bhandari, Rojalina Priyadarshini, Pramod Kumar Meher
Fraudulent educational degree certificates can have a catastrophic impact on employers. This can also cause serious problems for educational institutions during admission into higher-degree programmes. Although some research has been done to validate the authenticity of certificates, the challenge persists in having a tamper-proof and low-cost solution where the certificate issuer and the certificates are validated in a single integrated platform. This paper proposes an integrated blockchain-based decentralized model for certificate verification and issuer validation. Along with this, it will protect the certificates from being tampered with. The proposed solution is tested by creating an Ethereum-like transparent blockchain. The performance of the proposed solution is measured by the execution and transaction cost in terms of Gas consumption and a comparison of the proposed approach against existing methods is provided. It is observed that the cost of adding a certificate is the lowest in the case of the proposed system.
Nowadays, Learning Management Systems (LMS) have become increasingly popular, particularly due to the COVID-19 pandemic, offering enhanced effectiveness and efficiency. Online exams have emerged as a critical feature within LMS, serving as a means to evaluate students’ performance and assess their understanding of the course material. These exams play a crucial role in determining students’ eligibility for progression to the next grade or level of study. It is imperative that online exam results meet the standards of reliability and transparency. Any lack of reliability, such as the vulnerability to hacking, can have detrimental effects on students’ overall grades. Traditional online exam systems typically store data in centralized locations like MySQL databases, leaving them susceptible to unauthorized access by malicious individuals who may alter students’ exam results. This paper aims to propose a blockchain-based framework that facilitates the secure and peer-to-peer conduct and evaluation of academic exams. The framework employs hashing techniques to ensure the integrity of the data and utilizes proof of stake mechanisms to enhance security. Blockchain technology has proven to be effective in safeguarding data integrity by virtue of its decentralized data storage approach and the use of cryptographic hashing for every block within the chain. This paper demonstrates how online exams can be developed using blockchain technology, with each question asked and answered being directly stored on the blockchain. To achieve this, we have developed a module that integrates with the Moodle learning management system. Through a comparative analysis of the default centralized storage approach in Moodle, our module modifies the exam results’ storage method, ensuring secure and tamper-proof data storage on the blockchain network. By leveraging the blockchain network, the data associated with exam results is reliably secured, ensuring its integrity, and making it immune to manipulation. Our results indicate that the data stored through the blockchain achieved complete accuracy, with no discrepancies observed when compared to the standard approach employed by the Moodle LMS for storing results. The blockchain network provides a reliable and immutable platform that prevents unauthorized alterations or manipulations of student data. In conclusion, our blockchain-based framework offers a robust solution for enhancing the security and reliability of online exam results. By leveraging the decentralized and tamper-proof nature of blockchain technology, we can ensure the integrity and transparency of student data, ultimately providing a more trustworthy and accurate assessment of their academic performance.
In December 2021, one of the authors of the present paper (AR) took part in the peer review of the paper “Safety and immunogenicity of an inactivated virus particle vaccine for SARS-CoV-2, BIV1-CovIran: findings from double-blind, randomized, placebo-controlled, phase I and II clinical trials among healthy adults” for the BMJ Open1, 2. The manuscript described clinical phases I and II of the COVID-19 vaccine BIV1-CovIran by Shifa Pharmed Industrial Group. The article was accepted for publication in March 2021 after three review rounds, with a total of six reviewers involved. On May 2022, AR received an email from Yeganeh Torbati, a Washington Post reporter who was investigating the development of BIV1-CovIran. Torbati asked AR for a general opinion about the data presented in the above article. AR replied that no serious anomalies were highlighted, although he specified that the peer review process was too superficial to guarantee complete integrity. Subsequently, through an article published in the Washington Post in August 2022, Torbati disclosed serious misconduct dynamics3. In support of her claims, an official correction was published in the BMJ Open in November 2022, in which the authors were forced to admit various conflicts of interest and the occurrence of vaccine-related adverse effects1. The relevant fact is that not even six peer reviewers and one editor have discovered such a hidden scenario. This is not intended to blame the journal or the reviewers but only to denounce that the world of scientific publication is currently subject to easy ethical violations. Although financial relationships can markedly bias biomedical research, marginal importance is given to this aspect4, 5. In this regard, this letter proposes a set of practices to counteract some major integrity problems.What can authors do?A1. Authors should facilitate research reproducibility to boost peer-review speed and accuracy. This includes i) sharing codes, calculations, and data (raw and elaborated), and ii) providing a step-by-step description of the ideas that led to the realization of the project, the implementation of the methods, and the procedures to assess the tests' assumptions.A2. Authors should adopt frameworks for enhancing quality in preclinical data since this can significantly increase transparency and trust in results and allow errors to be prevented rather than detected too late6.A3. Regarding clinical trials, authors should publicly share audit/monitoring documents, information about the contract research organization that monitored the study, and data submitted to regulatory agencies (at least on the clinical testing front, which does not seem to threaten intellectual property or industry secrets).A4. Authors should release a preprint version so as to allow the scientific community to review the results independently and rapidly.What can academic journals do?J1. Journal editors should evaluate the paper’s health sensitivity and decide whether it is a high-sensitivity topic. All research involving novel drugs, vaccines, and therapeutic strategies should be considered at high sensitivity.J2. For high-sensitivity topics, journals should compulsorily require A1-A4. Any draft version that has passed peer review should be released at the very moment of approval, explicitly indicating that it is an unedited peer-reviewed version. Reviewers' reports and authors' responses should always be published, ensuring easy citability (e.g., DOI). Reviewers' names and affiliations should also be published unless they express reasonable fears for their safety. This should help reduce the problem of coercive citations7. Finally, journals should allow authors to publicly share editorial rejection decisions, including reviewers' reports.J3. For high-sensitivity topics, journal editors should stratify the peer review to ensure the validity of the key elements. Alongside a general assessment, each methodological aspect (e.g., design, population, data collection, statistical analysis, and results) should be carefully evaluated by one or more independent specific experts, especially when dealing with high-complexity data or multidisciplinary approaches. Regarding clinical trials, editors should involve expert figures to evaluate pharmacological and public health aspects (e.g., adverse reaction reports). Journals should also include a specific mandatory section in which reviewers declare the limitations of their review (e.g., “I'm not an expert on Bayesian methods”), so that editors and readers have a clear understanding of what the reviewers assessed. Finally, double-blind review should be required to reduce authorship bias8.J4. For high-sensitivity topics, journal editors should create a dedicated section made up of two or more journalists experienced in detecting ethical violations. Such supervision should extend to the authors but also the reviewers, who could voluntarily influence the publication process. The inquiry must only concern researchers' professional relationships and activities, without affecting the private sphere, in order to safeguard their privacy. The academic journal should propose to the reporters to sign a non-disclosure agreement regarding the data found and guarantee the quality of the investigation.J5. For high-sensitivity topics, journals should compulsorily require that the data are suitably standardized to allow decentralized analysis through automated tools, software, or artificial intelligence algorithms6, 9. Specific guidelines should be provided to help authors with the A1 point. Means for decentralized analysis should be provided to reviewers. Should a unique international standardization be chosen by regulatory agencies, journals would have to adhere to it.J6. For high-sensitivity topics, journals should pay peer reviewers and editors. Indeed, paying peer reviewers – a sustainable practice, as shown by the editorial policies of various journals – would foster excellence thanks to an economic reward proportional to the reviewer's skill (competition mechanism). One of the main obstacles to publication, namely the difficulty in finding available reviewers, would be quickly overcome. Scientists could play this role on a permanent and ongoing basis thanks to the benefits of true job performance. Paid work would increase the actual responsibility of peer reviewers and editors.What can abstracting service groups do?I1. Tiered indexing should be introduced by abstracting services. The top rank should only be granted to academic journals that meet J1-J6. Indeed, since indexing in recognized databases is a source of prestige (so much so that, in most cases, journals reserve a special section of their websites to this scope), doing so would drive health journals to adjust to the new standards. Moreover, this would help the public to identify the most authoritative and reliable journals. Similar initiatives are already underway10.What can regulatory agencies, funders, and institutions do?R1. Funders and regulatory agencies should require necessary authors' compliance with points A1-A3.R2. Regulatory agencies should agree on a unique international data standardization (see point J5) so as to strengthen and accelerate scrutiny by the whole scientific community.R3. Institutions and employers should actively encourage and support scientific refereeing. Moreover, funders should be willing to finance an extra amount to properly perform points J3, J4, and J6.In conclusion, we do ask the scientific community to take a clear position and make itself heard with a stentorian voice to protect public health from ethical misconduct. This renewal would lead not only to direct benefits to the research but also to the public image of the whole scientific world, thanks to a novel, more transparent, efficient, and effective procedure of academic publication. We are aware that these guidelines are tailored to the medical field and that some of our requests could be not applicable or not stringent enough. Therefore, if needed, specific recommendations should be added or lifted based on the research field.
Md Rahat Ibne Sattar, Md. Thowhid Bin Hossain Efty, Taiyaba Shadaka Rafa, Tusar Das · 8 authors
Nowadays, the online platform has been used by many educational institutions, to conduct tests, especially for secondary to tertiary level students. The most popular online test program is run by providing a user id and password to the candidates, and subsequently, they log in to the given web page to answer the questions. However, this system has a lot of bugs, the password can be misused followed by cheating in the test. This shows the importance of a secure system being implemented to avoid such a problem. This paper presents a blockchain framework that secures the online examination system. The proposed framework has been used to secure a data management system that connects to existing educational data. Institutions can simply compile their data history without requiring a copy from the central servers. The proposed blockchain framework improves data security and removes any potential cheating between users or third-party institutions that access applications and services. In this regard, this study provides a secured framework for conducting and evaluating subject tests to ensure consistency between student and server, and secure delivery of questionnaire from the server.
Open access
Academic integrity and plagiarism
Artificial Intelligence in Healthcare and Education
We are presenting a fast and innovative approach to performing approximate pattern-matching for plagiarism detection, using an NDFA-based approach that significantly enhances performance compared to other existing similarity measures. We outline the advantages of our approach in the context of blockchain-based non-fungible tokens (NFTs). We present, formalize, discuss and test our proposed approach in several real-world scenarios and with different similarity measures commonly used in plagiarism detection, and observe significant throughput enhancements throughout the entire spectrum of tests, with little to no compromises on the accuracy of the detection process overall. We conclude that our approach is suitable and adequate to perform approximate pattern-matching for plagiarism detection, and outline research directions for future improvements.
Major sectors are employing the use of blockchain technology to enhance their day-to-day operations. Educational services is one such sector with the government of Singapore developing a large-scale blockchain certificate verification system to improve the education sector. This project will further boost the adoption of blockchain technology in the education space with the development of a peer review decentralised application. This application will be developed with the decentralised, transparent and secure nature of the Ethereum blockchain as the foundation. This peer review decentralised application will allow an administrator to start peer review sessions. The administrator creates teams and assigns peers to their respective teams in the peer review session. The administrator can then start the peer review session where peers use typed evaluations or use a rating system to evaluate/review their fellow peers. Reviews made are final and cannot be modified. This project aims to produce a peer review system that is transparent and immutable which is the key to tackling the problems of fake reviews plaguing traditional scholarly peer reviews, and at the same time providing the security and confidentiality features for student peer evaluation processes.
Since Bitcoin appeared in 2009, various other cryptocurrencies have also begun to attract attention and supporters. At the same time, because many cryptocurrency investors want to make a profit using cryptocurrencies, cryptocurrency exchanges list them without any special or technical verification. Cryptocurrency developers often write white papers to describe their cryptocurrency's special techniques and expected future worth. However, based solely on the contents of the white paper and/or a surface inspection of the source code, we cannot know if the developer has simply leveraged existing code bases without incorporating novel functionality. In order to address this problem, we present a framework to measure the similarity between the source codes of cryptocurrencies for detecting plagiarism.