Blockchain and Artificial Intelligence Non-Formal Education System (BANFES)
Abstract
:1. Introduction
2. Background
2.1. Connectivity and Accessibility Challenges and Solutions
2.1.1. Afghanistan Youthful Demographics and Socioeconomic Challenges
2.1.2. Obstacles and Opportunities within Afghanistan’s Digital Landscape
2.2. Blockchain Evolution and Fundamental Principles
2.2.1. Types and Development of Blockchain Technology
- Public Blockchains: These are open to everyone and operate transparently. Examples include Bitcoin and Ethereum, which use special methods (called consensus algorithms) to ensure all transactions are secure and agreed upon without a central authority [14].
- Private Blockchains: These are controlled by specific organizations and are not open to the public. They are designed for business use, prioritizing efficiency, privacy, and internal governance [31].
- Consortium Blockchains: These are a hybrid of public and private, run by a group of organizations rather than a single entity. They are less open than public blockchains but offer a balance of security and efficiency [32].
Platform Name | Ledger Type | Consensus Protocol |
---|---|---|
Bitcoin [12] | Public | Proof of Work (PoW) |
Ethereum [14] | Public | PoW and Proof of Stake (PoS) |
Hyperledger Fabric [31] | Consortium | Pluggable algorithm |
EOS [33] | Public and Private | Delegated Proof of Stake (DPoS) |
Stellar [34] | Private | Stellar consensus protocol |
Quorum [35] | Private | Majority voting |
Ripple [36] | Private | Probabilistic voting |
2.2.2. Core Features of Blockchain Security
3. Blockchain and AI Non-Formal Education System (BANFES)
3.1. Preserving Learning via Alternative Educational System
3.1.1. Core Components
- Blockchain: Serves as the secure backbone, recording and verifying educational transactions and student records, enabling continuity in education.
- Adaptive AI Technologies: Analyze learner data to provide personalized content and predict learning outcomes, enhancing the educational experience.
- Content and Question Repository: A centralized database for educational resources, accessible to all, supporting self-assessment and peer review.
- Independent Assessment: Learners are evaluated by entities separate from their educational nodes, with performance impacting the reputations of both students and institutions within the network.
3.1.2. BANFES Educational Ecosystem
3.2. Credential Verification in Non-Formal Higher Education
3.3. Exams and Incentives
3.3.1. Question Difficulty Index (QDI)
3.3.2. Markov Chain Representation of BANFES
3.3.3. Teacher Quality Score (TQS)
3.3.4. Institution Reputation and Activity
- Activity Set : This parameter represents a comprehensive set of activities related to specific competencies that either a student or a teacher engages in. Each activity, , is linked to learning or teaching a particular skill or knowledge area. This set encompasses a wide range of educational interactions, from classroom exercises to practical applications, emphasizing the diverse ways in which competencies can be developed and assessed within the educational framework.
- Results Set : The results set, R, records the outcomes of the activities in A, with denoting a positive outcome (such as successful skill acquisition or successful teaching outcome) and indicating a negative outcome (such as a failure to acquire a skill or an unsuccessful teaching attempt). This binary outcome measure provides a straightforward mechanism for evaluating the effectiveness of educational activities and interventions.
- Activity Weights : Weights, W, are assigned to each activity to reflect its relative importance or impact on the educational process. High-weight activities might include key competencies critical to a student’s academic and professional development, while lower-weight activities might involve supplementary skills. These weights help prioritize resources and focus on activities that offer the most significant benefits to students and educators.
- Total Participants T and S: These parameters represent the total number of teachers (T) and students (S) actively involved in the educational activities. They provide a scale of educational engagement, indicating the breadth of participation in the institution’s programs and initiatives.
- External Evaluations E: External evaluations or accreditations, represented by E, include formal recognitions, ratings, or certifications received from outside organizations. Each external evaluation is associated with a weight , reflecting its significance or prestige. This parameter underscores the institution’s standing in the broader educational and professional communities, influenced by external benchmarks of quality and achievement.
- Coverage Factor : The coverage factor, , quantifies the institution’s reach in engaging remote and offline students, with values ranging from 0 to 1. A higher indicates broader accessibility and outreach, reflecting the institution’s effectiveness in overcoming geographical and logistical barriers to education.
- Inclusivity Factor I: This factor measures the institution’s support for diversity, equity, and inclusion within its educational offerings and community engagement, with values also ranging from 0 to 1. A higher I value demonstrates a commitment to creating an inclusive environment that accommodates a wide range of backgrounds, abilities, and perspectives, enhancing the educational experience for all participants.
- A scaling factor adjusts the reputation formula based on internal assessments conducted by teachers and students. This factor emphasizes the significance of internal evaluations in reflecting the institution’s educational environment and operational effectiveness.
- adjusts the reputation formula to account for the quantity and quality of educational content produced by the institution. This factor considers various educational attributes such as relevance, depth, and innovation, highlighting the institution’s commitment to high-quality teaching and learning resources.
- modifies the reputation formula based on external evaluations linked to the success of the institution’s graduates, whether in the workforce or in further academic pursuits. This factor represents the value that the institution adds to its graduates as recognized by external entities, serving as a measure of the institution’s effectiveness in preparing students for real-world success.
- A scaling factor is used in the activity level formula. It accounts for the institution’s impact on education in areas initially identified as weak. This factor is adjusted based on independent assessments of improvement, underscoring the institution’s efforts to address and ameliorate educational challenges.
- An additional scaling factor , in the activity level formula, adjusts the contribution of inclusivity and coverage. It can be inferred that continues to play a role in emphasizing the institution’s commitment to diversity, equity, and accessibility in its educational outreach and engagement.
3.4. Optimization Techniques in BANFES: Strategic Alliances and Trust Dynamics
- Let denote the set of educational units within the network.
- : Strategy space for educational unit i.
- : Utility function for unit i, dependent on its own strategy and the strategies of other units.
- : A coalition of educational units.
- : Value function for coalition C, indicating the total benefits achieved through cooperation.
3.4.1. Linear and Mixed-Integer Linear Programming
3.4.2. Genetic Algorithms and Particle Swarm Optimization
3.4.3. Model Variables and Functions of Quality Competition and Market Dynamics
3.4.4. Theoretical Justification
3.4.5. Higher Education Program Transaction Costs
3.4.6. Lifelong Learning
4. Future Prospects
4.1. The Potential of AI in Education
4.1.1. AI Integration and Functionality in Education
4.1.2. Integrating AI and Blockchain for Personalized Education
4.1.3. Machine Learning for Personalization
- Encoding student interactions as input to the RNN.
- Utilizing the network to predict future performance based on past interactions and self-assessments.
- Analyzing the accuracy of predictions to refine the learning path dynamically.
4.1.4. Leveraging AI for Enhanced Educational Outcomes
4.1.5. Navigating Ethical and Policy Dimensions of AI in Education
4.1.6. Prospective AI Contributions and Global Educational Shifts
4.2. AI and Women’s Education
AI-Powered Education Platform for Women in Afghanistan
4.3. Advancing toward Inclusive Education in Afghanistan: A Multifaceted Approach
4.3.1. Overcoming Connectivity Obstacles in African Rural Education through Cloud-Enabled E-Learning
4.3.2. Strategies for Low-Bandwidth Online Learning
4.3.3. Bridging the Educational Digital Divide through Micro-Cloud Innovations
4.3.4. Harnessing Renewable Energy in Rural Educational Environments
4.3.5. Adopting Cost-Efficient Hardware and Ad Hoc Digital Networks for Educational Fairness
4.3.6. Enriching Education via Space: The Influence of CubeSats and NASA’s Endorsement
4.3.7. Satellite Solutions for Overcoming the Educational Divide
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AI | artificial intelligence |
BANFES | blockchain and AI non-formal education system |
GDP | gross domestic product |
HEI | higher education institution |
ITU | International Telecommunication Union |
NGO | non-governmental organizations |
UNICEF | United Nations international children’s emergency fund |
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Category | Total | Percentage of Population | Notes |
---|---|---|---|
Population | 41.68 million | N/A | 49.5% female, 50.5% male, median age: 17 years |
Internet Users | 7.67 million | 18.4% | Internet penetration increased by 2.7% from 2022 |
Social Media Users | 3.15 million | 7.6% | |
Mobile Connections | 26.95 million | 64.7% | Increased by 921 thousand (+3.5%) from 2022 |
Internet Connection Speeds | Mobile: 5.27 Mbps, Fixed: 2.25 Mbps |
Category | Details |
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Prospective Advantages | 1. Personalized learning enhancements through AI-driven tools, offering tailored educational experiences. 2. Administrative efficiency via AI automation, optimizing tasks like enrollment and grading. 3. Insightful analytics for informed educational decisions, aiding in curriculum and pedagogical refinement. 4. Bridging educational access gaps, especially for marginalized communities, through AI-enabled platforms. |
Implementation Challenges | 1. Necessity for robust infrastructure and technological resources, often scarce in disadvantaged regions. 2. Concerns surrounding data privacy and ethical handling of student information. 3. Risk of exacerbating the digital divide, potentially disadvantaging socio-economically challenged students. 4. The imperative for comprehensive educator training on AI integration in teaching practices. |
Strategic Implementation Considerations | 1. Stakeholder engagement, ensuring collaborative efforts in AI-driven educational initiatives. 2. Ethical guidelines adherence, with a focus on fairness, transparency, and accountability in AI applications. 3. Continuous AI initiative assessment to refine and optimize educational outcomes. 4. Emphasis on contextual educational materials to demystify AI complexities and maximize its potential. |
Categories | Details |
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Core Objectives: |
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Key Features: |
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Nazari, Z.; Vahidi, A.R.; Musilek, P. Blockchain and Artificial Intelligence Non-Formal Education System (BANFES). Educ. Sci. 2024, 14, 881. https://doi.org/10.3390/educsci14080881
Nazari Z, Vahidi AR, Musilek P. Blockchain and Artificial Intelligence Non-Formal Education System (BANFES). Education Sciences. 2024; 14(8):881. https://doi.org/10.3390/educsci14080881
Chicago/Turabian StyleNazari, Zahra, Abdul Razaq Vahidi, and Petr Musilek. 2024. "Blockchain and Artificial Intelligence Non-Formal Education System (BANFES)" Education Sciences 14, no. 8: 881. https://doi.org/10.3390/educsci14080881
APA StyleNazari, Z., Vahidi, A. R., & Musilek, P. (2024). Blockchain and Artificial Intelligence Non-Formal Education System (BANFES). Education Sciences, 14(8), 881. https://doi.org/10.3390/educsci14080881