Academic certificate fraud is a persistent problem across developing economies, undermining trust between graduates, employers, and higher education institutions. In Nigeria, this has prompted a federal mandate requiring every employer to verify staff credentials; yet existing verification processes remain manual, slow, and easily circumvented. This paper proposed, implemented, and evaluated a fully decentralized academic certificate management framework built on the Ethereum blockchain, leveraging Solidity smart contracts, the InterPlanetary File System (IPFS) for off-chain document storage, QR-code-enabled instant verification, and on-chain revocation.The system was deployed and tested on the Ethereum Sepolia testnet using Hardhat 2.19.4 and ethers.js 6.x. Results show that issuing a single certificate costs a fraction of a cent, that batch issuance lowers this cost further, that verification is free for employers, and that the system correctly detects fabricated or altered certificates in the overwhelming majority of cases while cutting verification time from days to seconds. These findings demonstrate that blockchain-based credential management is technically sound, affordable, and readily deployable in resource-constrained institutional settings, offering a practical path toward restoring trust in academic credentials across Nigerian and other African universities.
Miriam W. Kaara, Jael S. Wekesa, Michael W. Kimwele
The integrity and credibility of educational institutions worldwide are being undermined by the growing issue of academic certificate fraud. The public's trust in the educational system, the integrity of job opportunities, and the legitimacy of authentic credentials are all negatively impacted by academic dishonesty. The majority of academic credentialing systems, both centralized and blockchain-based, focus on academic credential validation and immutability, even though academic institutions have embraced technology to improve academic credentialing procedures. This suggests that their support for intelligent and private fraud detection is minimal. In this work, we suggest an integrated method that combines smart anomaly detection with Federated Learning (FL). This makes it possible for several organizations to build a model for detecting certificate fraud without disclosing private information to other organizations. Each institution's private information is retained and incorporated into a global model. This enables the framework to handle concerns about data ownership and privacy as well as regulatory compliance. The framework uses Extreme Gradient Boosting (XGBoost) to identify anomalies in metadata and Convolutional Neural Networks (CNNs) to detect visual forgeries in certificates. A decentralized node is used to train the models, which are merged using the Federated Averaging (FedAvg) algorithm. The federated model performs well in anomaly detection, according to the experiments conducted. When compared to conventional centralized approaches, it significantly reduces false positives and false negatives, with an accuracy of up to 94% and an AUC of 0.97. The model's findings imply that a federated learning approach would make it possible for institutions to detect fraud in a secure, scalable, and cooperative manner. In this regard, this framework offers a workable way to create digital credential systems that are more trustworthy and protect privacy. Key words: Federated Learning, Blockchain, certificate fraud detection, privacy preservation, distributed datasets, secure collaboration.
Currently, the most significant threat to the validity of academic credentials in the United States is the advanced forgery of transcripts along with diploma mills. This research study addresses the potential of blockchain technology as a decentralized means to protect academic credentials. By integrating recent academic research and technical frameworks, this study analyzes the shift from centralized databases to immutable, distributed ledgers. The integration of various perspectives, including advanced zero-knowledge proof architectures as well as legal frameworks for transnational data circulation, is a major innovation of this study. Using a systematic literature review and a case study approach, the research indicates that though blockchain's potential to enhance security and automate processes through smart contracts is indeed great, a number of legal, compliance, and technical barriers have to be removed for it to be a viable option. This study proposes that the combination of artificial intelligence (AI), along with blockchain technology, provides the most secure option for U.S. higher education institutions.
Generative artificial intelligence is increasingly built into how students prepare, write, and revise bachelor's and master's theses, yet the principles meant to govern responsible use were largely written for research outputs rather than for supervised, assessed, and educational student work. This study asks what responsible AI use requires in the thesis specifically, and develops a framework for it. Guidance on responsible AI use already exists for research - including the eight-principle consolidation of Knöchel et al. (2025): regulations, data security, quality control, originality, bias mitigation, accountability, transparency, and broader impact - but they were written for published outputs, not supervised and assessed student work. Using these established principles as one structured starting point rather than a template to apply, I conducted 28 semi-structured interviews with current students, recent graduates, lecturers and supervisors, program managers, and domain experts, analyzed with Template Analysis, and let the evidence confirm, reshape, and extend them. Quality control and accountability became load-bearing; originality, transparency, and broader impact required substantial reinterpretation; data security and bias mitigation remained normatively important despite limited spontaneous salience. The evidence further pointed to a stake the research framework does not contain: the integrity of the learning the thesis is designed to develop and certify. I define this ninth principle, learning integrity, as alignment between intended thesis learning outcomes, the activities meaningfully performed by the student, and the evidence used to assess them. The resulting ALIGN framework combines nine principles with prospective agreement, process-based supervision, proportionate disclosure, verification, and dialogic defense - a triangulated integrity architecture for use before, during, and at the end of thesis work, rather than any single instrument treated as proof of authorship. The paper closes with a one-page student checklist and a citable disclosure sentence for thesis methods sections. The study contributes to education research and higher education assessment by specifying how generative AI changes the relation between student agency, self-regulated learning, evidence of competence, and academic integrity in capstone thesis work.
Open access
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Artificial Intelligence in Healthcare and Education
Hostels are an essential part of campus life in technical institutions, directly influencing student welfare, safety, and satisfaction. However, traditional hostel grievance management systems often face issues of delayed responses, lack of transparency, and potential data manipulation, resulting in a trust gap between students and administrators. This paper reviews how permissioned blockchain technology can transform hostel grievance systems by ensuring transparency, accountability, and tamper-proof recordkeeping. A hybrid blockchain architecture integrating institutional databases with a distributed ledger is examined for its ability to maintain verifiable complaint histories while protecting privacy. The study highlights the potential educational and administrative benefits of adopting such systems, including faster grievance resolution, fair decision-making, and improved trust within hostel communities. Recommendations are provided for technical institutions aiming to modernize hostel management through blockchain-based digital governance.
Prof. Narde S. A., Yadav N.S., Ghodake D.T., Patil S.J., Salunkhe S. S.
Abstract In recent years, the rapid growth of digital technologies in education has increased the importance of academic certificates for employment, higher studies, and professional validation. However, the issue of fake and forged certificates has become a serious challenge for institutions and organizations. Traditional certificate verification systems are manual, time-consuming, and often lack transparency and security. These systems are also vulnerable to data manipulation and unauthorized access due to centralized storage. To address these challenges, this paper proposes a blockchain-based academic certificate validation system. The system uses blockchain technology to securely store certificate data in the form of cryptographic hash values generated using the SHA-256 algorithm. Since blockchain is decentralized and immutable, once data is stored, it cannot be altered or deleted. The system allows administrators to upload student data and issue results, which are then stored on the blockchain. Each certificate is associated with a unique verification ID and can be validated using QR codes or direct input. The proposed system improves efficiency, enhances data security, and reduces the risk of fraud. The results demonstrate that the system is faster, more reliable, and more secure than traditional methods.
Academic credential fraud poses a critical challenge to Indonesian higher education, with approximately 30% of job applicants providing false academic qualifications while conventional verification processes require 2–4 weeks with significant administrative costs. This research addresses the gap where 77% of blockchain education research remains conceptual by proposing and evaluating a four-layer blockchain system architecture for academic diploma authentication. Using Design Science Research Methodology (DSRM), the study designs and implements a layered architecture comprising a Presentation Layer (React 18.2.0 with client-side SHA-256 hashing), Application Layer (Node.js 18.20.8 with Web3.js), Data Layer (PostgreSQL 14.5 for off-chain metadata), and Blockchain Layer (DiplomaValidator smart contract in Solidity 0.8.19 on Ganache 2.7.1). The architectural design enforces separation of concerns, enabling tamper-evident credential storage through immutable on-chain hash registration and trustless public verification through zero-gas view functions. Comprehensive evaluation through 38 functional tests, performance benchmarking, security auditing, and integration testing demonstrates 100% pass rate across all categories. Performance metrics show registration in 15.23 ms (240,082 gas units) and verification in 9.47 ms at zero gas cost, achieving 51.81 TPS throughput. Security audit yields 95/100 with zero high or medium vulnerabilities. The primary contribution of this research is a formally documented four-layer blockchain architecture for academic credential authentication validated through DSRM providing a replicable architectural model and quantified performance baselines for the Computer Science community and Indonesian higher education institutions considering blockchain adoption
Rabib Jahin Ibn Momin, Ahmed Mahir Sultan Rumi, Rezwana Reaz
Academic examination systems worldwide continue to rely on centralised, opaque record-keeping that is often vulnerable to credential forgery, result tampering, examiner bias, and the absence of transparent re-evaluation pathways. Existing blockchain-based approaches in education focus predominantly on post-hoc certificate storage or online-only examination portals, leaving the complete onsite examination lifecycle, from conducting exams through scrutiny, largely unaddressed. This paper proposes ParikkhaChain, a blockchain-based framework that covers the entire examination lifecycle of an onsite examination system with three distinguishing contributions: (i) anonymous script evaluation through cryptographic hashing of answer scripts before examiner access, thereby eliminating identity-based bias; (ii) a transparent evaluation and scrutiny workflow backed by an immutable on-chain audit trail that records every mark submission and grade revision; and (iii) inclusion of privacy-preserving verification using zero-knowledge proofs and off-chain storage mechanisms. The system is architected around four Solidity smart contracts deployed on the Ethereum blockchain. The proposed architecture is the first initiative to our knowledge to support physical examination process, anonymous marking, and re-evaluation transparency. We successfully simulate full exam cycles of an onsite exam to grade-sheet generation using a working prototype on a large scale of 100 courses and hundreds of teachers and students. The experimental results show that the system can manage online examinations of hundreds of courses, students and faculties efficiently with great throughput, low storage, and transaction cost. Our codebase is available in open source form at https://github.com/AhmedRumi/CSE6608-ParikkhaChain
Remote examination platforms have experienced exponential growth, yet centralized architectures remain susceptible to data manipulation, unauthorized record alteration, and deficient audit mechanisms. This work introduces a federated, permissioned blockchain framework built upon Hyperledger Fabric, integrated within an AI-driven online examination platform designated as Evalon. The proposed architecture distributes ledger maintenance across multiple authorized institutional peers, recording cryptographic digests of examination lifecycle events—including candidate authentication, session boundaries, proctoring anomalies, and grade finalization—without exposing personally identifiable information on-chain. A Byzantine fault-tolerant ordering service coupled with endorsement policies ensures that no single administrative entity can unilaterally modify committed records. The blockchain substrate operates alongside a microservices backend deployed on serverless cloud infrastructure, facilitating real-time event validation through RESTful APIs and deterministic smart contracts. Complementing the integrity layer, computer vision models perform continuous behavioral analysis, detecting multi-face presence, gaze deviation, and anomalous motion patterns during live sessions. Experimental evaluation across 12,000 simulated examination sessions demonstrates a 99.7% hash verification success rate, sub-second ledger commit latency under concurrent loads of 500 transactions per second, and a 34% reduction in undetected integrity violations compared with conventional centralized logging. The combined framework establishes a tamper-resistant, auditable, and scalable ecosystem suitable for academic, certification, and enterprise assessment deployments.
Academic certificate verification in many institutions is still carried out using centralized and manual systems, which are prone to forgery, data manipulation, high administrative costs, and delays in verification. These challenges reduce the reliability and efficiency of credential validation. This research work focuses on the development of a Decentralized Certificate Verification System (DCVS) that improves security, transparency, and trust in academic credential verification. The proposed system uses blockchain technology to represent academic certificates as Non-Fungible Tokens (NFTs) based on the ERC-721 standard deployed on the Polygon blockchain. Certificate documents and metadata are stored off-chain using the Interplanetary File System (IPFS), while cryptographic references are recorded on the blockchain to ensure data integrity and prevent tampering. The system adopts Self-Sovereign Identity (SSI) principles, allowing students to own and share their credentials while enabling employers and institutions to verify certificates without relying on a central authority. Evaluation of the system on the Polygon test network showed a reliable result with a minting confirmation time of 3.2 seconds, less than the 10-second standard benchmark, verification latency of 0.8 seconds, less than the 2-second benchmark, and a transaction cost of 0.00021 MATIC, less than the 0.01 MATIC standard cost. The study demonstrates that blockchain-based solutions can effectively address the challenges of traditional academic certificate verification systems.
The increasing incidents of forged academic certificates and the inefficiencies of traditional verification systems highlight the urgent need for a secure, transparent, and reliable credential management mechanism. Conventional systems are largely centralised, time-consuming, and prone to manipulation, resulting in high administrative overhead and verification delays. We prepared an AI-Based Decentralized Academic Credential Verification System that leverages blockchain technology, smart contracts, and artificial intelligence to provide a tamper-proof platform for issuing, storing, and validating academic records. Artificial Intelligence is integrated to perform anomaly detection during certificate issuance and AI-based facial authentication for students, enhancing security and preventing fraudulent entries before blockchain storage. Students gain permanent, secure access to their verified credentials, while verifiers, such as employers, can instantly authenticate certificates using blockchain records or QR code scanning, eliminating the need for intermediaries. By integrating Ethereum, Solidity, Web3.js, IPFS, React.js, and AI models, the proposed system delivers a decentralized, scalable, and cost-effective solution that enhances trust, reduces verification time, and effectively combats academic credential fraud.
Dental billing fraud costs the United States an estimated $5.2 billion to $17.4 billion annually, yet detection relies entirely on retrospective rule-based auditing that leaves the real-time claimsubmission window unaddressed. The laboratory-to-clinic workflow governing prosthetic manufacturing remains paper-based, dispute-prone, and disconnected from insurance adjudication. This paper presents DentaChain, a permissioned blockchain framework chaining dental laboratory workflow management directly to insurance claim verification. Three Solidity smart contracts deployed on Hyperledger Besu automate milestone-driven prosthetic manufacturing with escrowbased payment release, enforce a causal provenance chain that structurally eliminates phantom billing prior to any machine learning inference, and maintain an immutable, regulator-accessible compliance ledger. A dual-model machine learning pipeline completes the system: a Random Forest classifier with synthetic minority oversampling predicts prosthetic defect risk before manufacturing begins (AUC = 0.991, recall = 0.949), while a Random Forest and Isolation Forest ensemble detects claims across four fraud typologies in real time (AUC = 0.986, recall = 0.927), evaluated under partial-signal fraud injection for conservative performance estimates. At the recommended operating threshold, 95.5% of claims are auto-approved, reducing adjudicator workload. A synthetic dataset of 50,000 Current Dental Terminology (CDT) claims and 12,000 laboratory orders is released publicly to support reproducibility.
ABSTRACT The generation and verification of academic credentials, particularly diplomas, is crucial for the secure functioning, enhanced legitimacy and confidentiality of higher education institutions (HEIs). Digitising the internal services of HEIs is of great importance, as it mitigates the potential abuse of diplomas and other important documents. The prevalence of forgery attempts highlights the need for robust verification methods. In this regard, blockchain technology can be considered as a solution due to its capability to ensure the immutability, decentralisation, security and transparency of all documents stored on the blockchain network or storage. In this paper, we aim to move from concept to practical implementation of a blockchain‐based framework for generating and verifying diplomas, named Diploma Integrity Authentication Record (DIAR), which, in addition to the digitalisation of diplomas and academic documents, also enables their fast, secure, and cost‐effective verification. Through the paper, we describe in detail the process of practical implementation of the DIAR framework, presenting the smart contract for generating and verifying diplomas, the process of encrypting and storing diplomas as non‐fungible tokens on the blockchain network or storage. At the same time, the challenges of practical implementation are also described, which are predominantly related to the scarcity of blockchain solutions for generating and verifying diplomas in HEIs. Significant attention and emphasis are placed on the legal matters pertaining to the implementation and safeguarding of students' identification and data privacy.
Abdul Razaque, Saule Amanzholova, Galimkair Mutanov, Olga Ussatova · 8 authors
This article focuses on developing an anti-corruption system for certifying students’ academic achievements in Kazakhstani higher education institutions by utilizing blockchain and artificial intelligence AI technologies. We specifically propose the Academic Integrity Verification System (AIVS), a revolutionary system that combines blockchain’s tamper-proof storage with AI’s anomaly detection capabilities. The system reduces major risks in traditional academic record management while ensuring transparency, precision, and proactive fraud detection. The simulation was conducted at the International Information Technology University (IITU) using Ethereum-based blockchain and AI models. In simulated testnet experiments, AIVS achieved an 85% reduction in verification time compared to traditional processes and delivered a 95% overall model accuracy in record validation. These results demonstrate the potential of blockchain and AI integration for improving efficiency and integrity in academic verification workflows. These findings demonstrate that our proposed AIVS enhances academic transparency, reduces corruption, and provides a scalable framework for secure academic record management. The proposed strategy marks a significant step forward in the governance of digital education in Kazakhstan and abroad.
The crisis of research integrity triggered by academic misconduct, such as scientific fraud and paper retractions, has emerged as a critical issue demanding urgent resolution within the academic community. Blockchain (BC), with its core features of distributed ledger, peer-to-peer transmission, consensus mechanisms, timestamps, and smart contracts, offers novel technical solutions for research institutions seeking efficient models of research credit supervision. By incorporating the psychological factors of risk perception among decision-makers and the dynamic evolution of behavioral decision-making, and drawing on prospect theory, this study has constructed an evolutionary game model involving researchers, scientific research institutions, and governmental entities to examine BC-enabled research credit supervision. This model analyzes the key determinants influencing scientific research institutions’ adoption of blockchain regulation (BC regulation), elucidates the behavioral characteristics and boundary conditions of research integrity among researchers under this new regulatory paradigm, and reveals the dynamic evolutionary trajectory of collaborative supervision between governments and scientific research institutions. The findings indicate the following: (1) Compared to traditional regulation, the BC regulation demonstrates superior regulatory effectiveness at equivalent levels of researcher integrity and misconduct costs, as well as under identical settings for reputational loss and penalties. (2) In addition to cost considerations and government subsidies, factors such as loss aversion coefficient, risk preference coefficient, and privacy breach losses are critical in influencing research institutions’ decisions to implement BC regulation. (3) The evolution of blockchain-empowered regulatory models encompasses three distinct evolutionary patterns. This study provides a theoretical foundation and a simulation case to optimize regulatory strategy formulation and resource allocation, thereby enhancing the effectiveness of research credit supervision.
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.
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.
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.
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.