Digital Transformation has reconfigure the quality assurance (QA) landscape in higher education by authorizing data-driven analysis, real time monitoring and transparency in institutional processes. Emerging technologies such as artificial intelligence, learning analytics, blockchain credentialing and integrated management information systems have augmented the dependability of QA mechanisms. This chapter examines the key factors driving digital transformation in QA including policy frameworks, institutional capacity, technological readiness, governance and stakeholder engagement. It further explains how it influences QA outcomes and institutional excellence. Draft on global research and policy literature the chapter showcase the inference for higher education institutions and offers future research directions to consolidate digital QA ecosystems.
Emerging Trends in Global Education refer to the evolving educational philosophies, technological innovations, pedagogical approaches, and institutional transformations that are redefining teaching, learning, research, governance, and lifelong education across the world. Rapid advancements in Artificial Intelligence (AI), Industry 5.0, digital transformation, quantum computing, immersive technologies, intelligent automation, and globally connected knowledge networks are reshaping the objectives and delivery of education. Higher education institutions are no longer confined to traditional classroom instruction; instead, they are becoming dynamic ecosystems that integrate technology, interdisciplinary research, innovation, sustainability, entrepreneurship, and global collaboration. As societies transition toward knowledge-driven and technology-enabled economies, universities must continuously adapt to emerging trends that prepare graduates for professions, challenges, and opportunities that are constantly evolving.The primary objective of emerging trends in global education is to develop educational systems that are flexible, inclusive, technology-enabled, learner-centred, and capable of addressing the complex demands of the twenty-first century. Universities increasingly recognize that graduates require more than academic knowledge to succeed in rapidly changing environments. They must possess critical thinking, creativity, digital literacy, ethical reasoning, adaptability, intercultural competence, entrepreneurial abilities, collaborative leadership, and lifelong learning skills. Emerging educational models therefore emphasize competency-based learning, experiential education, interdisciplinary collaboration, personalized instruction, and continuous professional development while encouraging innovation and responsible citizenship.Artificial Intelligence has become one of the most influential drivers of educational transformation by enhancing teaching, learning, research, administration, and institutional decision-making. AI-powered learning platforms provide adaptive instruction, intelligent tutoring, automated assessment, predictive analytics, multilingual communication, accessibility support, and personalized learning experiences that accommodate diverse learner needs. Universities increasingly employ AI to optimize curriculum design, monitor student progress, improve retention, strengthen academic advising, and support evidence-based institutional planning. AI also accelerates research through intelligent literature analysis, simulation, predictive modelling, data mining, and knowledge discovery. Nevertheless, responsible implementation requires ethical governance that ensures transparency, fairness, accountability, privacy protection, explainability, inclusiveness, and respect for human rights.Industry 5.0 represents a significant evolution beyond industrial automation by emphasizing collaboration between intelligent technologies and human creativity. Within higher education, Industry 5.0 encourages universities to prepare graduates capable of working alongside Artificial Intelligence, robotics, cyber-physical systems, advanced manufacturing, intelligent healthcare technologies, sustainable engineering, and digital innovation ecosystems. Educational programmes increasingly integrate technological expertise with ethical leadership, emotional intelligence, sustainability, and human-centred innovation. This balanced approach ensures that technological progress enhances human well-being while supporting inclusive economic development and environmental responsibility.Digital transformation has fundamentally redefined educational delivery by integrating cloud computing, learning management systems, digital libraries, blockchain, Internet of Things (IoT), virtual laboratories, intelligent communication platforms, big data analytics, and collaborative online learning environments. Students now access educational resources anytime and anywhere through digital devices, enabling flexible learning pathways that accommodate diverse educational and professional needs. Virtual classrooms, hybrid education, online assessment, remote research collaboration, and digital credentialing have become integral components of higher education. Universities continue to invest in secure digital infrastructure that enhances educational accessibility, institutional efficiency, cyber security, and global academic collaboration.Research and innovation remain central to emerging educational trends because universities generate scientific knowledge and technological solutions that address societal challenges. Interdisciplinary research increasingly combines Artificial Intelligence, biotechnology, quantum computing, environmental science, healthcare, economics, engineering, social sciences, and educational technology to solve complex global problems. Universities establish innovation centres, technology incubators, entrepreneurship hubs, interdisciplinary laboratories, and collaborative research networks that encourage creativity, commercialization, and responsible technological advancement. Such initiatives strengthen national innovation ecosystems while preparing students to become researchers, entrepreneurs, and future leaders.Curriculum development is undergoing continuous transformation to reflect emerging technologies, evolving labour market requirements, and global sustainability priorities. Universities increasingly adopt competency-based education, project-based learning, interdisciplinary programmes, experiential learning, micro-credentials, digital certifications, flexible academic pathways, and lifelong learning opportunities. Subjects such as Artificial Intelligence, data science, cyber security, sustainability, digital ethics, entrepreneurship, innovation management, climate resilience, quantum technologies, and global citizenship are being integrated across disciplines. Modern curricula emphasize practical application, collaborative learning, and real-world problem-solving while maintaining strong theoretical foundations.Faculty members remain central to educational transformation despite rapid technological advancement. Their role has expanded from knowledge transmission to mentoring, facilitation, research leadership, innovation management, interdisciplinary collaboration, and ethical guidance. Educators integrate intelligent educational technologies into teaching while encouraging critical inquiry, creativity, collaborative learning, reflective practice, and evidence-based reasoning. Continuous professional development enables faculty members to remain current with emerging technologies, responsible AI, digital pedagogy, sustainability initiatives, and international educational standards, thereby ensuring high-quality learning experiences.Student-centred learning has become a defining characteristic of future education because learners increasingly require personalized educational experiences that reflect their interests, abilities, aspirations, and career objectives. Universities provide adaptive learning pathways, individualized academic support, experiential learning opportunities, entrepreneurship programmes, global mobility initiatives, digital portfolios, career counselling, and interdisciplinary projects that encourage students to take active responsibility for their learning. Such approaches strengthen learner motivation, creativity, resilience, communication, leadership, and lifelong learning capabilities while preparing graduates for dynamic professional environments.Institutional governance provides the strategic framework for implementing emerging educational trends effectively. Universities establish digital transformation strategies, innovation policies, sustainability frameworks, ethics committees, quality assurance systems, cyber security protocols, research governance mechanisms, and international partnership offices that coordinate institutional modernization. Transparent governance promotes accountability, stakeholder participation, financial sustainability, responsible technology adoption, and evidence-based decision-making while ensuring that educational innovation aligns with institutional missions and societal expectations.International collaboration has become increasingly important because educational innovation benefits from global partnerships and shared expertise. Universities participate in multinational research projects, joint degree programmes, student and faculty mobility initiatives, international conferences, virtual exchange programmes, collaborative innovation ecosystems, and global academic networks. These partnerships facilitate knowledge sharing, technological advancement, intercultural understanding, and collaborative problem-solving while supporting the implementation of the United Nations Sustainable Development Goals (SDGs) and strengthening global educational quality.Sustainability remains a fundamental principle guiding emerging educational trends because future educational systems must balance technological advancement with environmental protection, social inclusion, and economic resilience. Universities integrate sustainability into teaching, research, campus operations, innovation, entrepreneurship, governance, and community engagement while encouraging responsible consumption, renewable energy, climate resilience, ethical leadership, circular economy practices, and environmentally responsible technological development. Sustainable education ensures that innovation contributes positively to both present and future generations.Assessment and continuous improvement are evolving alongside technological innovation. Universities increasingly employ competency-based assessment, digital portfolios, project-based evaluation, learning analytics, adaptive testing, peer assessment, reflective learning, and AI-assisted evaluation to measure student achievement comprehensively. Artifici
Game-based teaching (GBT) has gained widespread adoption in modern education, yet teachers bear heavy burdens in designing GBT activities and interpreting student learning performance, while centralized educational data storage brings prominent security and credibility risks. To tackle the above bottlenecks, this paper proposes SmartTA, an integrated teaching assistant system combining GBT recommendation modules, automated machine learning (AutoML), and blockchain. Specifically, SmartTA supplies customized GBT cases and exam scoring suggestions for teachers, and leverages AutoML to automatically mine student learning behaviors with zero coding requirements. Three groups of experiments are conducted to validate the system: AutoML achieves a maximum prediction accuracy of 93% on six public educational datasets; the Hyperledger Fabric-based blockchain prototype enables data insertion with an average latency of approximately 2.2 seconds and query latency of approximately 150 ms; 20 frontline educational practitioners provide 85% positive user feedback. The experimental results suggest that SmartTA may help reduce teachers’ lesson preparation workload, support improved instructional quality, while enabling tamper-resistant data storage via blockchain. This study realizes the practical fusion of AutoML and blockchain for GBT scenarios, and establishes a novel, secure, data-driven teaching assistance paradigm that is accessible to non-technical educators.
Abstract Online Education in Higher Education is rapidly evolving through the integration of Large Language Model (LLM)-powered intelligent systems, which enable personalized tutoring, dynamic content generation, and automated assessment. However, the widespread adoption of LLMs in education is hampered due to their inherent limitations, including susceptibility to hallucinations, insufficient domain-specific knowledge validation, and output inconsistency. These deficiencies can lead to misleading or erroneous content, potentially causing significant negative learning outcomes. A core challenge lies in ensuring that such errors are immutably logged and traceable, thereby establishing a mechanism for accountability among the entities deploying these LLM services. To address these challenges, this paper proposes a novel framework that Integrates LLM with consortium blockchain for personalized and verifiable online education. Our design features a synergistic architecture in which LLM based services provide the intelligent educational interface, while a permissioned consortium blockchain serves as a secure and tamper proof ledger. This blockchain infrastructure records critical educational transactions ranging from learning process data and academic credentials to the outputs generated by the LLMs. This integration not only secures academic credentials but also establishes a fully auditable trail, making it possible to trace responsibility for educational deficiencies caused by AI errors. Collectively, this work demonstrates a robust and accountable framework for leveraging LLMs in education, effectively mitigating the risks of AI inaccuracies through the verifiable and immutable nature of consortium blockchain.
Open access
Artificial Intelligence in Healthcare and Education
Online Learning and Analytics
Intelligent Tutoring Systems and Adaptive Learning
The compatibility of faster and faster digitalization of higher education has exerted pressure on the necessity to have a secure, interoperable, and smart academic credentialing system. Traditional centralized record management models are prone to data editing, slowness in verification and inter-institutional identification. In this chapter, the author suggests a decentralized-trust semantic intelligence hybrid model of credentialing and academic data management on a block chain-artificial intelligence (AI) system.The findings show that the combination of AI and distributed ledger technology turns the traditional credentialing of storing records to an active, learner-focused system. The chapter provides a scalable and governance-conscious paradigm in the future of digital universities, enhanced lifelong learning, micro-credential portability and transparent academic data ecosystems.
The contemporary education landscape is often marred by escalating costs and centralized pedagogical structures, which collectively create significant barriers to entry for millions of potential learners worldwide. This paper presents \textbf{Skill Link}, a sophisticated decentralized platform designed to democratize skill acquisition through a specialized credit-based barter system. Unlike conventional e-learning platforms that rely on traditional currency transactions, Skill Link enables a frictionless exchange of knowledge by utilizing a virtual credit economy where participants earn and spend "learning credits." To address the critical issue of credential fraud in decentralized environments, the platform integrates Ethereum-based blockchain technology to ensure the absolute immutability and verifiable authenticity of all earned certificates. Key innovations include a multi-tiered course classification system, an automated mock assessment framework with negative marking capabilities, an intelligent context-aware AI assistant powered by advanced language models, and a rigorous verification mechanism for professional social links (LinkedIn, GitHub, Indeed). Developed using the robust Django framework, Python-based Web3 utilities, and a secure PostgreSQL/SQLite back-end, Skill Link provides a highly secure, transparent, and scalable ecosystem for peer-to-peer knowledge sharing, ultimately fostering a global community of experts and lifelong learners. The system's architecture emphasizes data integrity through atomic transactions and cryptographic verification, ensuring a trustless environment for global skill exchange.
Renangi Sandeep, Thupakula Leena Sri, K Ananda Rutvik Reddy, Nimmakayala Kethana · 6 authors
This growth of digital learning platforms has presented a twin need: to deliver a learner a highly personalized educational journey and to deliver academic credentials that are verifiable, safe, and unchangeable. The current systems tend to address these goals separately and as a result, there are disjointed ecosystems with complex recommendation engines without trusted credentialing systems and sound certification systems that do not provide any course selection guidance. To fill this gap, this paper presents the Integrated Adaptive Learning and Certification Framework (IALCF), a new architecture that integrates into a LightGBM-based recommendation system a blockchain-based digital certification protocol in a synergistic manner. The recommendation module is an active learner profile analyzer that uses past performance, real-time interaction metrics and dynamically recommenders, predicting course selection with an accuracy of 98.7 and mean absolute error (MAE) of 1.18. The certification module is based on a more advanced X.509 standard with a delegated Proof-of-Stake (dPoS) blockchain, which forms a tamper-evident credential storage and an efficient verification algorithm, which has a verification success rate of over 95 percent in high-load conditions. The experimental findings reveal that the IALCF is a scalable, efficient and safe end-to-end solution to contemporary e-learning settings and is effective in integrating personalized learning with credible management of credentials.
Online Learning and Analytics
Information Systems Education and Curriculum Development
Student elections in African universities are frequently marred by violence and fraud, thereby undermining democratic participation. This paper presents the first blockchain-based voting system specifically designed for student elections at Cheikh Anta Diop University (UCAD) in Dakar, Senegal. The proposed hybrid architecture combines the Ethereum blockchain to ensure vote immutability, MySQL for user data management, and IPFS for decentralized document storage. The system integrates multi-layer biometric authentication (facial recognition and WebAuthn), an eight-phase automated smart contract, and PySpark for real-time blockchain data analysis. Implementation results demonstrate 100% voting accuracy with the automatic generation of tamper-proof electoral records. The storage of IPFS hashes on the blockchain guarantees document integrity while optimizing storage costs. This solution effectively addresses the recurring electoral violence at UCAD while establishing a reproducible framework for democratic modernization within African higher education institutions.
Su.Suganthi, Shanmuga Sundar.G, Felix Rainston.V.G, R. Hariharan · 6 authors
To revolutionize the educational scene, this project proposes a Web3 Token Incentive Mechanism, an Interactive Doubt Clarification Chatbot, and an advanced Intelligent Course Recommendation System. The system uses AI algorithms to analyze technical and non-technical input to recommend courses that suit students' interests and professional objectives. Using voice interaction for answering student queries and adding relevant video content will enhance accessibility and engagement. This AI-powered real-time support will be provided by the chatbot. A token-based incentive system, based on Web3, motivates students after they have completed courses and assessments. Once completed, students will be bridging the gap between education and work. They can use these tokens to avail free internships or get help in finding a job, thus adding utility to their educational process. This integrated strategy involves personalized learning, interactive participation, and incentive rewards to create a strong foundation for schooling focused on careers. AI, Web3 tokens, chatbots, personalized learning, career counseling, educational incentives, interactive meta learning, job assistance, and skill development are some of the keywords.
Intelligent Tutoring Systems and Adaptive Learning
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.
This article presents cutting-edge developments in data-passing architectures that are revolutionizing AI-driven learning systems. By examining recent breakthroughs in streaming data architectures, data lakehouse designs, and feature stores, the article identifies how these innovations overcome traditional bottlenecks in distributed training environments. It explores critical challenges in multi-platform data passing, including data quality maintenance, security considerations, and performance optimization. The discussion extends to self-healing architectures that significantly enhance system resilience through autonomous fault detection and recovery mechanisms. Additionally, emerging trends in data-sharing protocols, from blockchain-based decentralized architectures to federated learning approaches, demonstrate how collaborative AI ecosystems can maintain privacy while maximizing data utility. Through a comprehensive analysis of these architectural innovations, the article illustrates how organizations can create more powerful, resilient, and collaborative AI-driven learning systems that operate seamlessly across previously siloed environments.
In recent years, Learning Management Systems (LMS) have acquired substantial appeal, notably because to the COVID-19 pandemic, delivering greater efficacy and efficiency. Within LMS, online tests have evolved as a crucial instrument for measuring students’ performance and knowledge of course content, playing a vital part in deciding their development. It is critical that online test results be both trustworthy and easily accessible. Students' grades might be negatively affected by any security flaw, such hacking. Conventional online test systems frequently store data centrally in databases like MySQL, leaving them subject to unwanted access and modification. Secure, peer-to-peer administration and assessment of academic tests is made possible in this article using a blockchain-based infrastructure. To guarantee data integrity, the framework utilises hashing algorithms. To strengthen security, it incorporates proof of stake processes. Blockchain effectively protects data integrity due to its decentralised data storage and the use of cryptographic hashing for each block. The study illustrates the usage of blockchain for designing online tests, recording each question and response directly on the blockchain. We were able to do this by developing a module that communicates with Moodle, an LMS. By comparing it to Moodle's default centralised storage, our addon alters the storage of exam results, making the data stored on the blockchain safe and impenetrable. Exam data is securely encrypted using the blockchain, which prevents tampering and ensures its integrity. Based on our findings, there are no inconsistencies when comparing data saved on the blockchain to Moodle's conventional method. To safeguard student information from tampering, the blockchain network offers a trustworthy and unchangeable platform. Finally, our blockchain-based paradigm provides a strong answer to the problem of how to make online test scores more secure and trustworthy. We guarantee data integrity and transparency by using blockchain's decentralised and tamper-proof nature. This allows for a more reliable evaluation of academic achievement.
Fei Ren, Bo Zhao, Jun Wang, Juxiang Zhou · 5 authors
With the rapid development of information technology, blended learning has become a crucial aspect of modern education. However, the fragmented use of various teaching platforms, such as Xuexitong and Rain Classroom, has led to the dispersion of teaching data. This not only increases the cognitive load on teachers and students but also hinders the systematic recording of teaching activities and learning outcomes. Moreover, existing blended learning evaluation systems exhibit significant shortcomings in large-scale data storage and secure sharing. To address these issues, this study designs a blended teaching evaluation management system based on blockchain and searchable encryption. First, an on-chain and off-chain collaborative storage model is established using the Ethereum blockchain and the InterPlanetary File System (IPFS) to ensure secure and large-scale storage of student work data. Next, a role-based access control scheme utilizing smart contracts is proposed to effectively prevent unauthorized access. Simultaneously, a searchable encryption scheme is designed using AES-CBC-256 and SHA-256 algorithms, enabling data sharing while safeguarding data privacy. Additionally, the smart contract comprehensively records students’ grade information, including weekly regular scores, midterm scores, final scores, overall scores, and their rankings, ensuring transparency in the evaluation process. Based on these technical solutions, a general-purpose teaching evaluation management system (B-Education) is developed. The experimental results demonstrate that the system accurately records teaching activities and learning outcomes, improving the transparency of teaching evaluations while ensuring data security and privacy. The system’s gas consumption remains within a reasonable range, demonstrating good flexibility and usability. Educational institutions can flexibly configure course evaluation criteria and adjust the weighting of various grades based on their specific needs. This study provides an innovative solution for blended teaching evaluation, offering significant theoretical value and practical implications.
Abstract The ever-changing global educational landscape, coupled with the advancement of Web3, is seeing rapid changes in the ways pedagogical artificially intelligent conversational agents are being developed and used to advance teaching and learning in higher education. Given the rapidly evolving research landscape, there is a need to establish what the current state of the art is in terms of the pedagogical applications and technological functions of these conversational agents and to identify the key existing research gaps, and future research directions, in the field. A literature survey of the state of the art of pedagogical AI conversational agents in higher education was conducted. The resulting literature sample (n = 92) was analysed using thematic template analysis, the results of which were used to develop a conceptual framework of pedagogical conversational agents in higher education. Furthermore, a survey of the state of the art was then presented as a function of the framework. The conceptual framework proposes that pedagogical AI conversational agents can primarily be considered in terms of their pedagogical applications and their pedagogical purposes , which include pastoral , instructional and cognitive , and are further considered in terms of mode of study and intent . The technological functions of the agents are also considered in terms of embodiment (embodied/disembodied) and functional type and features . This research proposes that there are numerous opportunities for future research, such as, the use of conversational agents for enhancing assessment, reflective practice and to support more effective administration and management practice. In terms of technological functions, future research would benefit from focusing on enhancing the level of personalisation and media richness of interaction that can be achieved by AI conversational agents.
Open access
AI in Service Interactions
Online Learning and Analytics
Intelligent Tutoring Systems and Adaptive Learning
With the rapid development and increasing maturity of emerging technologies such as 5G communication, artificial intelligence, blockchain technology, and computer supported collaborative work, the internet is entering a new era - Web3. This transformation brings both opportunities and challenges to the field of education. Addressing current issues faced by universities, such as security risks in resource storage, lack of platform creation incentives, complexity in copyright confirmation, unsatisfactory user interaction experiences, and insufficient supply of high-quality educational resources, this paper explores and proposes an innovative solution - the “CoTeach” smart education platform. This platform integrates the core concepts of Web3, and data-driven artificial intelligence technology. It aims to reshape the teaching and learning experience, as well as the collaborative development of professional knowledge. Specifically, the “CoTeach” platform ensures secure storage and immutability of educational resources through blockchain technology, reducing risks associated with resource storage. By utilizing collaborative livestreaming mechanisms, it provides economic incentives for creators, contributors, and learners, fostering greater community participation. The transparency and smart contract functionality of blockchain simplify the copyright confirmation process, safeguarding creators' rights. The platform enhances user interaction through gamification design, making the learning process more engaging and enjoyable. Finally, artificial intelligence technology optimizes resource recommendations and personalized learning paths, addressing the shortage of high-quality educational resources.
Alexander Mikroyannidis, Allan Third, John Domingue
Today’s fast-paced economy and the impact of the COVID-19 pandemic on the job market have made more prominent the need for flexible accreditation and lifelong learning. This paper introduces a novel approach for supporting lifelong learning using Smart Badges as Blockchain-based decentralised micro-accreditation. The implementation of this approach is based on granular verification, which has been achieved via LinkChains MerQL. A user evaluation has been conducted with stakeholders from the education community, the results of which have indicated the strengths and weaknesses of this approach. Liaising with stakeholders from the education community has provided valuable insights into the different aspects of lifelong learning that require support, as well as into the training needs of the education community related to the use of decentralisation technologies.
Abstract Like an online carnival, Web3 aims to turn the internet’s social order upside down. Unlike a carnival, Web3 wants to be more than a weeklong party and morph into a legitimate substitute for the internet’s status quo. Web3’s secret sauce for upheaval is decentralized, permissionless technologies, in particular blockchain technologies. In this exploratory paper, we draw on the concept of institutional isomorphism to muse about Web3’s future and to highlight the inherent tension between striving to be different from Web2 yet wanting to become more legitimate. We argue that technical merits are hardly enough to realize Web3’s high aspirations. Regulatory pressures, rampant uncertainty, and the professional norms of Web3 participants drive the space to adopt many of the organizational structures and practices that it aims to displace. To maintain divergence from Web2, despite isomorphic pressures, we suggest that it is important to increase the overall diversity of people in Web3, to double down on the value of decentralization, and to reaffirm Web3’s commitment to creatively re-imagine various institutional arrangements.
Contribution:This research explores the effectiveness of a proposed teaching strategy in blockchain education, finding that it enhances learning outcomes, cognitive well-being, and student engagement in tertiary education, ultimately resulting in a shallower learning curve for STEM knowledge.Background:In the context of Industry 4.0, blockchain technology has emerged as a key driver of transformation in data management and system automation across a range of industrial applications. Despite its significance, the intricate theories and concepts associated with blockchain often serve as a deterrent for novice learners, inhibiting their ability to appreciate the value of industrial blockchain. Consequently, there is a pressing need to develop interactive teaching content that alleviates the steep learning curve.Intended Outcomes:The teaching strategy for the gamification in blockchain education is proposed, which positively influence students’ cognitive well-being in terms of knowledge retention, cognitive curiosity, and heightened enjoyment.Application Design:Based on the experimental learning theory, the gamification of blockchain education, namely “BlockTrainHK”, is implemented in the experimental learning cycle. Therefore, the gamified learning in experimental learning (GEL) strategy is proposed to examine the effectiveness of concrete experience, reflective observation, abstract conceptualization and active experimentation by two case studies.Findings:The results of the two-year study on the gamified blockchain education are encouraging: test groups using the GEL strategy were better in the cognitive well-being, and students’ cognitive well-being is positively proportional to the level of individual technical knowledge and skills.
R. Sabitha, M Madhini, Priya Sethuraman, S. Vijayalakshmi · 5 authors
A key component of a blockchain is its distributed digital ledger of transactions, which is effectively a record of all transactions recorded in the network. A key component of a blockchain is its distributed digital ledger of transactions, which is effectively a record of all transactions recorded in the network. The process of altering images is been simplified. In a web-based program, a validation system for student certificates is created. The issue statement suggests that universities keep records of students who are unable to attend class in the form of a certificate. To skip class and get a doctor’s note is just too simple. A few pupils have been caught using forged certificates to skip class. Many people nowadays are dishonest and would buy or make fake certificates from websites that claim to provide them. Having to verify and validate certificates is a pain for the company and the institution. For safekeeping of certificates on the blockchain, we provide a method we term Blockchain Powered Student Certificate Validation (BPSCV). A standard Optical Character Recognition (OCR) model is used for cross-validation in order to assess how well the suggested task works. Digitization of the paper certificates is the initial step. When creating the certificate’s hash code, the suggested algorithm is utilized. Certificates are then recorded in the blockchain. Furthermore, the mobile app verifies these credentials. The use of blockchain technology allows us to validate digital certificates in a more efficient and safe manner.
Technology formally entered academia around 1450s AD when the printing press started being used for making books available to learners. Long after about seven centuries, gradual adoption of information, communication, and digital technologies brought overwhelming changes for speedier, more effective, and impactful teaching, learning, training, and evaluation. These rendered both synchronous and asynchronous modes much smarter and more exciting particularly for generations Y and Z. The advent and evolution of Web1 to Web3 has made globally accessible distance learning an indispensable part of contemporary education management development systems. Efforts have been made to narrate certain major dimensions and the collective effects of all these technologies culminating in EdTech. It has also deleneated the taxonomies behind and the impacts of AI and Generative AI on EdTech.
This research paper explores the intersection of zero-knowledge proofs (ZKPs) and machine learning (ML), presenting a comprehensive overview of recent advancements, applications, and challenges in this fast growing area. The jointers of ZKPs and ML techniques shall go a meter further to fuse privacy, security, and integrity in a number of solutions, which include forming of groups for data sharing and safe machine learning. Through the investigation of the well-respected sites in that area and also the thorough description of formulas and their experimental outcome, this paper looks for the clarification of the current state of affairs and the possible future directions of ZKPs in the AI world. By inserting the verification mechanism of ZKPs into machine learning ecosystem, it allows devising novel solutions for the problems of privacy and confidentiality that have for long been not solved. With this approach, the concatenation of parties collectively performs the process of dealing with private inputs without revealing any of these data and this, in return, opens the possibilities of secure multi-party computation. Furthermore, ZKPs protect data sharing as it gives people the opportunity to construct confidential data and share them to model training without compromising any one’s private details. Being a part of the dynamic conversations, which focus on the game-changing capacity of transparent zero-knowledge proofs (ZKPs), this paper brings the role of ZKPs in preserving the confidentiality and integrity of artificial intelligence (AI) applications into the centre of attention. As scientists still fight to improve protocols and circumvent computational complications, ZKPs are likely to establishment as critical tools in the effort to increase ML systems in the digital sphere.
Open access
Online Learning and Analytics
Intelligent Tutoring Systems and Adaptive Learning
Wang Jun, Muhammad Shahid Iqbal, Rashid Abbasi, Marwan Omar · 5 authors
Machine learning is playing an increasingly important role in education. This article examines its potential to bring about transformative change in this field. By using machine learning algorithms, physical education teachers can gather and analyze data on student performance and behavior. This enables them to create personalized learning experiences that cater to the unique needs of each student. Machine learning can also track and assess student progress, providing educators with valuable insights into the effectiveness of their teaching strategies. Furthermore, it can optimize the design of physical education curricula and assessments, making them more efficient and effective. Additionally, machine learning offers a more objective and accurate approach to evaluating and grading students. This paper discusses the challenges and opportunities associated with integrating machine learning into physical education, including ethical considerations and potential limitations.