Blockchain and Artificial Intelligence Non-Formal Education System (BANFES)

Abstract

The resurgence of the Taliban in Afghanistan has significantly exacerbated educational challenges for marginalized women and girls, deepening gender disparities and impeding socio-economic development. Addressing these issues, this article introduces the Blockchain and Artificial Intelligence Non-Formal Education System (BANFES), an innovative educational solution specifically designed for Afghan girls deprived of formal schooling. BANFES leverages advanced artificial intelligence technologies, including personalized data analysis, to provide customized learning experiences. Additionally, blockchain technology ensures secure record management and data integrity, facilitating a decentralized educational ecosystem where various nodes offer hybrid learning methodologies without intermediaries. This system not only adapts to individual learning speeds and styles to enhance engagement and outcomes but also employs an independent assessment mechanism to evaluate learners. Such evaluations promote transparency and maintain the quality and reputation of educational contributions within the network. The BANFES initiative also addresses implementation challenges, including local distrust and integration with existing educational structures, providing a robust model to overcome barriers to education. Furthermore, the paper explores the scalability of BANFES, proposing its application as a global strategy for non-formal education systems facing similar geopolitical and infrastructural challenges. By creating a secure, flexible, and learner-focused environment, BANFES aims to empower Afghan women and girls with essential skills for personal and professional growth, thus fostering socioeconomic advancement within their communities and setting a new standard for informal education worldwide.

1. Introduction

In 2023, the global educational landscape for marginalized women still faces significant challenges, with projections indicating that around 110 million girls and young women are at risk of being excluded from schooling by 2030. This is mainly due to the restricted access to education and healthcare, which are significant barriers to advancing gender parity. The educational gender divide is particularly pronounced in impoverished and conflict-ridden areas, where girls’ school attendance is significantly lower than global norms, persisting in higher education and vocational training. Despite advances in education, these improvements have not translated into greater participation of women in the workforce, which remains critically low, especially in regions such as South Asia, the Middle East and Africa. Addressing these educational gaps is crucial to fostering social and economic progress, alleviating poverty, and achieving gender equality, underscoring the importance of increased support for young women’s education and their transition to the labor market in underprivileged communities [1,2,3].

Figure 1 presents findings from the Local Burden of Disease Educational Attainment Collaborators, showing substantial disparities in educational attainment across low- and middle-income nations from 2000 to 2017, highlighting the ongoing gender gap in education, with men often receiving higher levels of education than women in regions like sub-Saharan Africa and South Asia [4].

The situation in Afghanistan has significantly worsened since the Taliban regained power in August 2021, reversing advancements in education by imposing severe restrictions on schooling for women over 10 years old and halting higher education. This has resulted in considerable economic losses and psychological suffering for the affected individuals [5,6].

To sustain educational pursuits, secret classes, online platforms, and community-led study groups have been initiated, supported by the Afghan diaspora and international aid, to address connectivity challenges. These efforts focus on improving academic and vocational skills, providing essential learning opportunities where formal education is not available [7,8].

The Taliban’s restrictive policies have exacerbated the social and economic exclusion of this demographic, deeply affecting their lives and rights. International NGOs face significant challenges, especially due to a Taliban decree that prohibits female employment in these organizations, which complicates their mission to support the Afghan population during an ongoing humanitarian crisis [9,10].

In Afghanistan, GDP per capita witnessed a sharp decline, falling from 516 in 2020 to 368 in 2021, with a potential further decrease in subsequent years. Coupled with this economic downturn, an alarming 97% of the Afghan population faces the risk of falling into poverty. In this context, the cost of Internet data packages, currently priced at USD 7, becomes prohibitively expensive. This situation severely limits access to online resources and educational opportunities for most Afghans, further compounded by the Taliban’s severe restrictions on women’s education. These constraints have forced educational initiatives to significantly scale back their engagement with students and adopt more clandestine operations [11]. Additionally, the persistent obstacles related to technology, connectivity, and gender issues significantly impede the advancement and reach of literacy initiatives. It is increasingly vital to maintain advocacy, secure global support, and develop creative solutions to provide educational and digital literacy access to Afghan women. This raises an important question: In a regime that denies basic educational rights to women, how can Afghanistan nurture and preserve the motivation and perseverance of its female learners over time?

Afghanistan’s education system, controlled by the government, faces challenges, particularly with the Taliban’s influence displacing many in the educational sector [12,13,14].

In response to these critical challenges, the introduction of the Blockchain and Artificial Intelligence Non-Formal Education System (BANFES) presents a transformative solution. BANFES aims to address these multifaceted issues by offering a flexible learning framework, shared educational responsibilities, and digital certification. This system proposes a non-formal, decentralized approach to education, which can provide substantial learning opportunities despite governmental restrictions, particularly in regions like Afghanistan.

By leveraging blockchain technology, BANFES seeks to create a secure, flexible, and learner-focused environment. The system will authenticate and accredit non-formal education, aiming to foster a more inclusive and empowering educational atmosphere. Furthermore, it outlines a comprehensive strategy to protect human rights, reduce gender inequities, and harness technological progress for continuous learning. The proposed framework for policy, regulation, operation, and evaluation will enhance adaptability and interoperability within Afghanistan’s infrastructural limits and promote collaboration among educational entities.

Ultimately, BANFES intends to standardize curricula, materials, and assessments, facilitate direct interactions among students, educators, and institutions, involve students in quality assurance, and ensure precise enrollment, credit registration, and credential verification processes through blockchain technology. It also plans to introduce methods for equitable resource distribution and periodic curriculum updates to meet global standards, incorporating AI for a dynamic learning experience while maintaining secure educational records [12,13,14].

The structure of this paper is methodically outlined as follows: Starting with an analytical and diagnostic background, the Section 2 describes the educational and digital challenges facing Afghanistan, such as a young population, economic limitations, and restricted digital access. It advocates for the adoption of blockchain technology to improve education and connectivity, highlighting the importance of technological investments and global cooperation to address these challenges. Next, Section 3 discusses BANFES, which aims to improve non-formal education for girls in Afghanistan. It utilizes blockchain technology to protect educational records and AI to customize learning, providing continuous, personalized education in a secure, decentralized environment. Following that, Section 4 discusses the role of artificial intelligence (AI) in education, emphasizing its use to personalize learning and improve administrative efficiency. It also discusses the challenges and ethical considerations involved in integrating AI into educational settings. Finally, the Section 5 summarizes the key points and outcomes of the discussion and underscores the transformative potential of blockchain and AI technologies in education, especially in contexts with significant challenges like Afghanistan.

2. Background

Implementing blockchain and artificial intelligence technologies in non-formal education systems in conflict-ridden and economically disadvantaged regions presents significant challenges. These include organizational and environmental constraints, as well as a pronounced knowledge gap among stakeholders. For instance, organizational resistance to new technologies and high deployment costs necessitate innovative approaches and collaborations to facilitate implementation [15,16].

Including case studies from similar settings could provide insights into overcoming barriers related to funding, technological literacy, and local resistance. Moreover, lessons from sectors like healthcare, where blockchain has helped manage data securely in resource-limited settings, might be adaptable to the educational sector [17].

2.1. Connectivity and Accessibility Challenges and Solutions

To develop a platform that offers a uniform curriculum and extensive and flexible educational materials, alongside assessment and evaluation procedures aligned with globally recognized universities while also safeguarding students’ physical and mental well-being throughout their learning process, it is crucial first to examine Afghanistan’s economic, social, and infrastructural landscape. This involves a thorough analysis of current challenges and the identification of potential opportunities for growth and improvement.

2.1.1. Afghanistan Youthful Demographics and Socioeconomic Challenges

In 2023, Afghanistan’s population is on the rise, showcasing a youthful trend with a median age of 17 years and a significant majority under 35, reflecting both potential and upcoming challenges in education, employment, and healthcare. The nation’s economy has contracted by 25% in the past two years, heavily impacted by dependence on external support and restrictive policies affecting women’s participation in education and the workforce. Despite the cessation of conflict, poverty is pervasive, and while there is an uptick in labor supply, including a significant increase in female labor force participation, it surpasses demand, leading to higher unemployment. Educational challenges are profound, especially for women in secondary education. Financially, Afghanistan faces a trade deficit despite stable tax revenue and a peak in exports, compounded by dwindling international aid and a constrained financial system, presenting a complex scenario of vulnerable stability and operational challenges for businesses [18].

2.1.2. Obstacles and Opportunities within Afghanistan’s Digital Landscape

Afghanistan’s digital landscape in 2023 showcases progress alongside persistent challenges. With 7.67 million Internet users and a 65% mobile connectivity rate, the nation sees gradual digital integration (

Table 1. Afghanistan’s population and internet usage statistics.

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

). However, over 80% of the population remains offline, underscoring a significant digital divide influenced by infrastructural and socio-economic barriers [19]. The International Telecommunication Union (ITU) underscores this global issue, particularly in low-income nations like Afghanistan, and through initiatives like ITU’s Giga project with UNICEF, strives to enhance connectivity, especially for educational purposes [20,21].

Despite these challenges, there are signs of digital resilience and adaptability. For instance, the growth in social media usage indicates a potential for broader digital engagement among Afghans when given access. The microfinance sector, analyzed by Microfinanza, shows that Afghans are embracing digital and financial platforms, which aids in overcoming economic hurdles and promotes micro-business growth [22]. This suggests a readiness among Afghans to leverage digital solutions tailored to their development needs, pointing towards future progress in digital integration.

Addressing the problem of inadequate Internet connectivity in Afghanistan, Section 4.3 presents an all-encompassing approach. It details various techniques to help students with restricted Internet access, with the goal of enhancing their learning journey.

2.2. Blockchain Evolution and Fundamental Principles

Previous studies such as the examination of Smart Contracts by Szabo in 1997 [23] and the investigation of Hashcash by Back in 2002 [24] have laid the groundwork for the blockchain technology we see today. The launch of Bitcoin in 2008 by an anonymous figure or group known as Satoshi Nakamoto marked a significant milestone in the practical application of blockchain technology [12]. This technology is now at the core of a variety of cryptocurrencies and blockchain-based solutions. An illustration of the basic structure of a blockchain network is provided in Figure 2. For example, Ethereum goes beyond the original application of Bitcoin by supporting smart contract execution, allowing for transactions that not only transfer cryptocurrency but also run smart contract code [14]. These contracts are crucial for the creation of decentralized applications (DApps), which offer a wide array of functionalities such as decentralized finance (DeFi), non-fungible tokens (NFTs), and decentralized autonomous organizations (DAOs).

In the realm of healthcare, blockchain enhances data security, patient privacy, and interoperability of health records, thereby allowing secure exchange of patient data among healthcare providers [25]. Likewise, the supply chain industry benefits from the transparency, traceability, and efficiency that blockchain brings to the entire product lifecycle, from production to delivery [26].

Utilizing digital asymmetric key signatures, the blockchain network ensures trustless but secure transactions, enabling only the sender and receiver, who hold the corresponding asymmetric key pair, to carry out the exchange without the need for a trusted third party’s involvement. The financial services sector has witnessed blockchain facilitate faster, safer, and more cost-effective transactions outside of conventional banking systems, thus expanding financial inclusion. Moreover, the potential of blockchain to secure electoral processes is being explored, with Estonia pioneering the use of blockchain for secure online voting, thereby enhancing the integrity and security of electoral systems [27].

Blockchain is also laying a solid foundation for copyright management, empowering creators to effectively manage their intellectual property rights [28]. In the real estate domain, blockchain is simplifying transactions by reducing fraud and increasing transparency [29], while the energy sector investigates blockchain for decentralized energy trading platforms, facilitating the sale of surplus renewable energy directly between neighbors [30].

2.2.1. Types and Development of Blockchain Technology

As of the time of this research, there are three main types of blockchain technology (Table 2):

• 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].

Table 2. Various blockchain platforms.

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

Table 2. Various blockchain platforms.

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

Blockchain technology has evolved through four major phases, each marking significant advancements in its capabilities, uses, and influence across different sectors. The inception, Blockchain 1.0, corresponds to the emergence of digital currencies like Bitcoin, introducing decentralized currency management outside the traditional banking or governmental oversight, with a focus on cryptographic security, anonymity, and peer-to-peer transactions. Advancing to Blockchain 2.0, we see the innovation of self-executing contracts encoded into the blockchain, exemplified by Ethereum. This expanded the scope of blockchain from digital currencies to applications such as decentralized finance and supply chain management, enhancing operational efficiency across sectors. Blockchain 3.0 further extends the application of blockchain into various industries beyond finance, concentrating on scalability, interoperability, and sustainability and heralding decentralized applications as decentralized alternatives to conventional centralized systems. Currently, Blockchain 4.0 emphasizes the extensive integration of blockchain into the industrial and enterprise realms, merging it with advanced technologies like the Internet of Things, AI, and big data aimed at augmenting transparency, efficiency, and security across numerous sectors.

Bashir highlights ten essential attributes of blockchain technology—distributed consensus, transaction verification, smart contract platforms, peer-to-peer value transfer, cryptocurrency generation and incentives, smart property, security measures, immutability, uniqueness, and smart contracts—as focal points of interest for enhancing educational systems [37].

2.2.2. Core Features of Blockchain Security

Hash functions are crucial in cryptography, transforming any string of data into a fixed-size string that is unique, helping to keep data secure and unchanged. For any given data, its hash can be computed and checked later to verify that the data remains unchanged, thereby confirming its integrity. The Bitcoin and Ethereum blockchains, for example, utilize SHA-256 and Keccak-256 hash functions. This process of converting any input into a hash is known as hashing [38].

Digital signatures enhance data security by assuring its integrity, authenticity, and non-repudiation. This involves generating a unique pair of keys (private and public), creating a digital signature with the private key, and verifying the signature with the public key to ascertain the data’s legitimacy. Various Digital Signature Algorithms (DSAs) like ECDSA offer efficient digital signing and verification, ensuring data integrity and origin. Digital certificates play a crucial role in verifying the identity of the message signer through public keys, with Public Key Infrastructures (PKIs) facilitating the issuance and management of these digital certificates [39].

The concept of Smart Contracts, introduced by Nick Szabo, has been central to blockchain’s evolution, allowing for the automation of transactions and agreements without intermediaries. Ethereum’s introduction marked a significant moment, providing a sophisticated platform for executing these contracts and fostering the growth of decentralized applications and financial systems [14]. By distributing data across multiple nodes, it mitigates risks like Distributed Denial of Service (DDoS) attacks. Each node independently verifies transactions through mechanisms such as Proof of Work or Proof of Stake, ensuring robust defense against unauthorized control and maintaining network integrity [37].

3. Blockchain and AI Non-Formal Education System (BANFES)

3.1. Preserving Learning via Alternative Educational System

Related works showcase the application of blockchain in streamlining and securing the verification of student credentials in Alumnichain [40], automating scholarship payments in education through Smart Contracts [41], securely awarding digital badges [42], and ensuring the authenticity and security of educational materials in online courses [43].

BANFES is presented as a promising initiative designed to address the significant educational challenges faced by young women in Afghanistan. It utilizes blockchain technology to create a secure and reliable record-keeping system. This system ensures the integrity and portability of educational achievements, which is especially important in a context like Afghanistan, where socio-political instability can disrupt the continuity of education.

BANFES initiative aims to address educational barriers in Afghanistan by offering a flexible and accessible learning framework. It focuses on personalized education plans and the credible acknowledgment of educational accomplishments, ensuring that learners receive a customized education experience that recognizes their achievements. Utilizing blockchain technology, BANFES secures the integrity and portability of educational records, ensuring that learners can progress without interruption despite socio-political challenges.

AI within BANFES customizes learning experiences through adaptive technologies, tailoring content to each learner’s pace and goals, thereby improving engagement and outcomes. AI also aids in evaluating educational content and educator performance, ensuring materials are both challenging and accessible, crucial in non-formal educational settings.

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

BANFES’ ecosystem, depicted in Figure 3, presents a decentralized network where various educational nodes offer hybrid and vast learning methodologies. In this educational framework, all nodes collaboratively replace the conventional centralized educational system by achieving consensus across the entire system. This integrated system, which merges educational and economic roles, identifies certain nodes as validator nodes. These nodes are notable for managing more than 51% of the market’s resources and activities and are recognized by their high reputation scores. Furthermore, nodes with lesser market shares, but significant educational engagements and similarly high reputations are also included. These nodes are randomly selected from those with lower capitalization to form a governing group of validators known as the Regulatory Authority (RA).

These validators serve for a specified term, after which, based on a reevaluation of the total market share and node activities, they may continue in their roles or be replaced. They contribute to educational policy-making and curriculum regulations in coordination with the International Education Commission (IEC) (please refer to Section 3.2), ensuring consensus among selected representatives of teachers and elite graduate students. Moreover, they oversee the validation of graduates and the precise and transparent execution of coin mining operations.

In this educational blockchain ecosystem, non-validator nodes perform essential functions that include managing data storage, overseeing data transfer, and handling the recruitment and coordination of teaching personnel for course delivery. These nodes also play a critical role in securing the necessary financial resources for educational endeavors and engaging in the mining of coins. Such diverse activities contribute significantly to the establishment of a strong infrastructure that supports the tracking of educational progress and the preservation of assessment integrity. This framework mirrors the mechanics observed in other sectors, such as gamified learning environments where user engagement in educational tasks parallels the mining process, ensuring progress validation and reward allocation [44]. Similarly, contributory learning platforms leverage user contributions to maintain educational quality, akin to the supportive role of miners in blockchain networks [45] (Figure 4). Moreover, blockchain-based educational records utilize a decentralized approach to validate transactions, ensuring transparency and integrity analogous to blockchain operations [46]. Lastly, the equitable distribution of educational resources through algorithmic allocation reflects the fairness ensured by blockchain mining mechanisms [47].

Blockchain’s security features prevent data tampering, and its decentralized nature cuts out intermediaries, reducing costs and broadening access. Students register using a unique code, contributing to a secure digital ledger that tracks their academic path and transactions for course fees, facilitated by smart contracts. AI analyzes academic progress, allowing sponsors to invest in promising students. Educators contribute to and benefit from a shared curriculum and exam question database, with rewards based on question difficulty and effectiveness. Exams are competitive, with earnings tied to performance. The system also enables geographic distribution of educational resources, with smart contracts facilitating exchanges to optimize capacity. Regulatory nodes ensure compliance and quality, with a transparent system for addressing discrepancies. This ecosystem promotes accountability and inclusion, offering a new path for educational empowerment in Afghanistan.

The system promotes a collaborative environment through a “component broker” (e.g., Apache Pulsar, Apache Kafka, RabbitMQ) that manages data flow and communication, ensuring effective distribution of resources and feedback across the network.

Smart contracts automate and streamline educational transactions and processes, facilitating seamless interactions within the ecosystem among institutions, educators, and learners.

3.2. Credential Verification in Non-Formal Higher Education

This section focuses on the utilization of activity-based metrics for evaluating the effectiveness and quality of BANFES, specifically within the context of higher education institutions (HEIs). The activity level of each node, reflecting its dynamics, the volume, and the quality of content produced, should correspond to the academic achievements of students and the intellectual advancement of graduates in accordance with established academic norms. In BANFES, this approach forms the foundation for a decentralized education system and plays a crucial role in the mining process, acting as a criterion for scoring and generating new blocks in the network. Each graduating student increases the activity score of a node. However, node activity is not solely based on investment share. Credits and activity points can also be earned through content creation and increasing the number of graduates, especially by supporting individuals with disabilities, extending geographical reach, promoting gender diversity, and through academic achievements such as publishing articles. This score, along with their investment stake in the system, boosts their chances of being selected for mining activities or as a creditor.

To facilitate the smooth functioning and integration of BANFES, there is an intermediary mechanism that connects activity-based metrics with the smart contract infrastructure. This mechanism quantifies the academic engagement and output of each node, converting them into measurable metrics that feed into the blockchain. It creates a dynamic where educational contributions and achievements are directly linked to blockchain operations, such as mining and new block creation.

The blockchain’s approach to academic assessment and record-keeping demands a comprehensive framework for recognizing and rewarding the contributions of each educational node. The Curriculum Smart Contract, established by the Regulatory Authority (RA), is crucial here. This contract sets the minimum course requirements for degree completion, connects to the “Authority” contract and the respective HEI, and operates on a modified ownership model, ensuring that only the owner (the HEI with a valid digital certificate) can execute its functions.

BANFES Course Registration outlines the essential process enabling HEIs to log and manage their curriculum’s courses. Initiated by the RA, this procedure involves the HEI’s engagement in registering courses, detailing each course, generating blockchain transactions for course registration, ensuring smart contract validation, recording transactions for auditability, ensuring interoperability for academic mobility, verifying course details on the blockchain, and allowing for course updates and maintenance. The process, illustrated in Figure 5 and Figure 6, emphasizes secure and efficient course management within the academic curriculum.

Students’ course selections, tied to their public key, create a permanent academic record that tracks educational progression, ensuring the verifiability of the academic pathway while maintaining student privacy.

BANFES Credit Registry system manages and authenticates students’ accumulated academic achievements. Educators, students, and evaluators collaborate within this system, where educational credits are logged, encapsulated in blockchain transactions, and verified for course and student enrollments, contributing to a robust framework for tracking educational progress. Figure 7 and Figure 8 visually represents the educational credits and certificate issuance process, highlighting the blockchain’s role in ensuring data integrity and enabling the transparent and updatable record-keeping of academic milestones. This algorithm elaborates on the process, emphasizing the blockchain’s capacity to automate the summing of total credits and assess degree completion requirements, culminating in the issuance of academic degrees.

The establishment of the Independent Education Commission (IEC) involves a collective of distinguished educational organizations tasked with advancing global education standards. This diverse assembly, including entities such as UNESCO, the World Bank’s Education Sector, and the International Baccalaureate, among others, is dedicated to improving educational quality and accessibility worldwide.

3.3. Exams and Incentives

The problem of question paper leaks (QPL) is a global challenge affecting both developed and developing countries, undermining the integrity of examinations. High-profile cases include the cancellation of exams by ACT Inc. in the United States, scrapped A-level physics exams at Brighton Hove and Sussex Sixth Form College in the UK, and leaked test papers in China, South Africa, Egypt, Vietnam, Nepal, Pakistan, India, Bangladesh, and Korea. These incidents reveal that the issue is not confined to a specific region and involves various stakeholders, including students, educators, and administrative staff. There is a pressing need for a robust and intelligent examination system to prevent QPL, as evidenced by repeated occurrences worldwide [48]. In Afghanistan, the general university entrance exam, known as Konkur, has been canceled in several provinces due to question leaks and cheating incidents [49,50].

BANFES proposes a multifaceted solution to enhance exam integrity and address question paper leaks through a blend of student self-assessment, educator evaluations, and the innovative application of Distributed Ledger Technology (DLT). By leveraging blockchain for secure record-keeping of assessments and educator verifications, BANFES enhances both the security and transparency of the exam process while making it more accessible, especially to remote and underserved communities. This system utilizes DLT to dynamically update and validate the question bank, ensuring exam content remains relevant and fair, thereby making the examination system more inclusive [48].

BANFES fosters a continuous learning and assessment cycle, where students engage in self-assessment and educators perform validations, contributing to a decentralized and secure examination system. The system’s adaptability is showcased by its support for diverse student backgrounds, aiming to extend educational opportunities and promote equity [48].

In tackling the secure distribution of exam questions, Islam et al. [48] propose a blockchain-based method that ensures exam questions remain confidential until the exam. This system, involving various roles like the Question Setter and Question Cloud, utilizes encryption and smart contracts to secure exam questions on the blockchain, offering a more robust solution against unauthorized access and tampering compared to traditional methods.

By incorporating Blockchain 3.0, BANFES utilizes decentralized applications (dApps) for backend operations across a decentralized network, emphasizing the system’s innovation in leveraging blockchain for educational integrity and inclusivity [51].

BANFES also introduces a novel approach to evaluating academic performance and credential verification, utilizing blockchain-stored, immutable, and verifiable badges. This aligns with Open Badges specifications and demonstrates the system’s effectiveness in enhancing the reliability of educational achievements, offering a transparent and secure method for credential verification and assessment, and highlighting its potential to transform educational accreditation and recognition globally [42].

AI in the BANFES system customizes learning experiences through adaptive technologies by tailoring content to each learner’s pace and goals, which improves engagement and educational outcomes. AI aids in evaluating educational content and educator performance to ensure that materials are both challenging and accessible, which is crucial in non-formal educational settings.

Moreover, AI technologies in BANFES include a range of applications from adaptive learning systems that provide personalized instruction and content recommendations to employing large language models for language learning and coding skills. These systems are designed to enhance educational outcomes by creating more engaging and interactive learning experiences, thereby making education more personalized and accessible.

This integration of AI helps in providing a nuanced and effective educational system through BANFES, addressing the specific educational needs and challenges in environments like Afghanistan.

The assessment mechanisms within BANFES, including question development and exam fee structures, reflect its commitment to equitable and relevant evaluation processes. By categorizing questions based on difficulty and involving both students and educators in the validation process, BANFES ensures that exam content remains relevant and challenging. This dynamic updating and validation of the question bank through consensus among participants underscore the collaborative nature of BANFES, fostering a community-driven approach to education [48].

The descriptive exam process within BANFES, including peer evaluation and the use of smart contracts for the final adjudication, introduces a level of rigor and transparency previously unattainable in traditional examination systems. This method not only streamlines the evaluation process but also minimizes the potential for bias, ensuring that student performance is assessed accurately and fairly. The evaluation diagram is shown Figure 9.

3.3.1. Question Difficulty Index (QDI)

Sujan Kumar Saha [52] introduces a groundbreaking system for assessing the quality of question papers, employing a range of techniques like keyword extraction, Latent Dirichlet Allocation, and machine learning algorithms, including Support Vector Machines, to calculate a quality score numerically. This system, after being rigorously tested across various question papers, demonstrated encouraging outcomes. Its assessments of questions’ relevance, complexity, and predicted time to answer closely matched those made by human evaluators. Integrating these findings with additional factors, the suggested formula to calculate the QDI is initially established by the question designer and modified over time:

$$q_{\mathrm{diff}}^{\left(n\right)}=\left(\sum _{k=1}^{n-1}{\displaystyle \frac{q_{\mathrm{diff}}^{\left(k\right)}\times \mathrm{Question}{\mathrm{Weightage}}^{\left(k\right)}}{\mathrm{Full}{\mathrm{Marks}}^{\left(k\right)}}}\right)\times \left({\alpha }^{(n-1)}+1-{\displaystyle \frac{1-r_{\mathrm{succ}}^{(n-1)}}{q_{\mathrm{diff}}^{(n-1)}}}\right),$$

(1)

where $r_{\mathrm{succ}}^{(n-1)}$ signifies the question’s success rate in the $(n-1)$th test, and ${\alpha }^{(n-1)}$ is an adaptable coefficient fine-tuned through automatic evaluation algorithms. This procedure involves the categorization of the question, assessment of its difficulty based on its category, and the application of a Support Vector Machine (SVM) classifier that has been trained on a wide range of questions. The analysis covers critical features such as patterns in the words, question-relevant terms, and the similarity of sentence structures, with the last being determined through a tree kernel technique. Subsequently, a pre-established schema allocates a numerical score for the difficulty of each question.

3.3.2. Markov Chain Representation of BANFES

Markov chains provide a powerful framework for modeling the stochastic processes underlying blockchain technologies [53]. By representing the states of a blockchain system and the transitions between these states as a Markov chain, one can analyze and predict the behavior of the blockchain network over time. This approach is particularly useful in understanding the dynamics of transactions, block creation, and the spread of consensus within the network. The application of Markov chains to blockchain systems enables a deeper insight into the efficiency, security, and scalability of these decentralized technologies, offering a mathematical lens through which to optimize and innovate within the blockchain space.

The Markov chain diagram for BANFES (Figure 10) outlines an interlinked architecture of states and transitions foundational to its educational framework. This graphical portrayal distinctly highlights the dynamics of the system, focusing on the recurring and evolving processes vital for adaptive learning, validation of credentials, and perpetual refinement within a blockchain-facilitated context. Description of nodes (states) follows.

EAS (Educational Activities State): The starting point where students engage in educational activities, marking the beginning of their learning journey within the BANFES ecosystem.

BVS (Blockchain Verification State): The crucial phase where educational achievements, such as course completions, are verified and securely recorded on the blockchain, ensuring data integrity and transparency.

AALS (AI Adaptive Learning State): Highlights the adaptive learning process driven by AI technologies. In this state, personalized learning experiences are crafted based on the student’s progress, preferences, and performance data. Large Language Models (LLMs), such as GPT-4, play a crucial role in this process. These models analyze vast amounts of data, including student interactions and responses, to understand individual learning styles and needs. By leveraging natural language processing capabilities, LLMs can generate tailored educational content, provide real-time feedback, and adjust the difficulty and focus of learning materials. This adaptive approach ensures that each student receives a customized learning path, enhancing engagement and improving learning outcomes [54,55].

CI (Credential Issuance State): Denotes the issuance of digital credentials (certificates, badges, etc.) to students upon successful completion of their courses, with these credentials being immutable and verifiable on the blockchain.

PRAS (Peer Review and Assessment State): Focuses on the peer evaluation mechanisms for student work, leveraging smart contracts to maintain integrity and transparency throughout the assessment process.

OES (Ongoing Enhancement Satate): Reflects the ongoing efforts to update and enhance the educational content and methodologies based on insights gained from AI analytics and blockchain records.

Transitions Between States: The pathways connecting these states are designed to be bidirectional and fluid, indicating a versatile and iterative progression within the educational framework. These transitions are marked with specific actions or conditions leading to changes in states, such as completing activities, undergoing verification, adjusting learning methods, and issuing credentials.

The structure facilitates repetitive learning loops, evidenced by transitions leading from the AI Adaptive Learning State back to the Educational Activities State and onwards to the Continuous Improvement State. This design allows learners to partake in new educational activities enriched by their prior experiences and the system’s ongoing developments.

Overall System Dynamics: This representation captures the essence of BANFES as a vibrant, interconnected ecosystem where the convergence of blockchain and AI technologies forge a strong, adaptable, and secure educational setting. The cyclical design of the graph reinforces the lifelong learning concept, where students constantly navigate through phases of learning, assessment, and enhancement. The architecture not only nurtures individual educational paths but also contributes to the collective advancement of the educational framework, ensuring it stays pertinent, efficacious, and attuned to the evolving needs of its participants.

3.3.3. Teacher Quality Score (TQS)

Consider that $T^{(n,t-1)}$ is determined at the preceding phase, specifically at time $t-1$ following n examinations, illustrating the capacity of educators to enhance student learning achievements and g is the average probability of all previous questions appearing. The Beta coefficient $B\left(t\right)$ determines the likelihood of selecting a question in the next stage, which is:

$$B\left(t\right)=g·\left({\displaystyle \frac{T^{(n,t-1)}}{1+{\Theta }^{(n,t-1)}}}\right).$$

(2)

It suggests that B increases with higher T values, implying a higher likelihood of selection for questions deemed of higher quality or relevance. $\Theta$ represents the coefficient of an educator’s incompetence, which increases with the frequency of mistakes educators make in grading exams, detecting cheating, and in similar scenarios. It acts to lower the value of B for questions associated with higher levels of incompetence under the presumption that questions linked to greater difficulty might be selected less often. The multiplication by g ensures that the historical probability of question appearance is factored into the selection likelihood, promoting a balance between quality, difficulty, and historical frequency of question usage.

The examination questions may originate from the same or different educators, with the success of students in these exams boosting the g coefficient for the contributing educator. The system also evaluates the educator based on the content provided, competency, and difficulty levels of the questions.

Taking into account the significant role of the educator’s involvement in student education, the method for determining the $TQS$ at a specific moment t may encompass the cumulative impact of the educator not only on their own students but on the entire educational system as well. That is summarized in Figure 11 and quantified as

$$T^{(n,t)}=B\sum _{i=1}^n\left(g^{n-i}\Delta q_{\mathrm{diff}}^{(i,t)}\right)\times \Psi \left(\mathrm{Content}\mathrm{Contribution},\mathrm{Impact}\mathrm{Factor}\right),$$

(3)

where $\Delta q_{\mathrm{diff}}^{(i,t)}$ represents the change in difficulty for the i-th question at time t, indicating improvement in student performance relative to the question. $\Psi$ is a function that quantifies the educator’s content contribution and the educational impact of their questions, providing a more nuanced assessment of their performance and contributions to the BANFES ecosystem. The impact factor essentially measures the educator’s role in actively engaging all students taught by them, including those who have utilized their teaching materials, as well as guiding talented students towards achieving high levels of skill and excellence. It also considers the educator’s contributions to topic depth based on Bloom’s Taxonomy as well as academic publications and innovative techniques.

3.3.4. Institution Reputation and Activity

Let C be a set of competencies that coach $T_k$ intends to transfer to his students. Each competency can be represented by a vector in a multidimensional space of skills, for example, $C=(c_1,c_2,\dots ,c_n)$ where $c_j$ is the competency level of C in student $j=1,\dots ,n$.

Students: Acquiring the competency C by a student can be modeled by the function $S\left(C\right)$ which measures student j’s skill in competency C. Student S engages in self-assessment by solving questions related to C and enhances his skill by solving problems and performing tasks related to C. After ensuring that the student has reached the skill level, the educator registers him in a peer evaluation exam.

Verification: The verification process can be modeled with a validation function $V(S,C)$ that evaluates the alignment between the skills demonstrated by student S in competency C. This function returns a value that indicates the degree of match between self-assessment and peer assessment, as well as the final evaluation for accreditation.

Blockchain Record: The blockchain record can be represented by a three-dimensional matrix B where each row corresponds to a student, each column to a competency, and the third dimension to educators. Each entry $b_{ij}$ is updated by the validation function $V(T,S,C)$.

We define a competency matrix B where each row represents a competency and each column represents the level of skills required for that competency

$$B=\left[\begin{array}{cccc}{b}_{11}& b_{12}& \cdots & b_{1n}\\ b_{21}& b_{22}& \cdots & b_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ b_{m1}& b_{m2}& \cdots & b_{mn}\end{array}\right]$$

Each blockchain transaction is a record of the validation function $V(T,S,C)$. The record can be represented as:

$$b_{ijk}=\left\{\begin{array}{cc}{T}^{(i,t)}\times V(S,C)\hfill & \mathrm{if}V(S,C)\mathrm{is}\mathrm{positive}\mathrm{for}\mathrm{teacher}T,\hfill \\ 0\hfill & \mathrm{otherwise}.\hfill \end{array}\right.$$

Reputation and Activity Level of Educational Institutions: A comprehensive mathematical model for the reputation and activity level of educational institutions is developed by integrating various components such as the results of competencies, activities by students and educators, external evaluations, and additional parameters addressing coverage and inclusivity. This enhanced model considers both positive and negative outcomes, coverage of remote and offline students, inclusivity of minorities and disabled individuals, and how these factors affect an institution’s reputation and activity level in both academic and market ecosystems. The variables are defined as follows:

• Activity Set $A=\{a_1,a_2,\dots ,a_n\}$: This parameter represents a comprehensive set of activities related to specific competencies that either a student or a teacher engages in. Each activity, $a_i$, 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 $R=\{r_1,r_2,\dots ,r_n\}$: The results set, R, records the outcomes of the activities in A, with $r_i=1$ denoting a positive outcome (such as successful skill acquisition or successful teaching outcome) and $r_i=-1$ 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 $W=\{w_1,w_2,\dots ,w_n\}$: 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 $e_i$ is associated with a weight $v_i$, 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 $Cov$: The coverage factor, $Cov$, quantifies the institution’s reach in engaging remote and offline students, with values ranging from 0 to 1. A higher $Cov$ 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 $\alpha$ 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.
• $\beta$ 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.
• $\Lambda$ 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 $\Gamma$ 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 $\Delta$, in the activity level formula, adjusts the contribution of inclusivity and coverage. It can be inferred that $\Delta$ continues to play a role in emphasizing the institution’s commitment to diversity, equity, and accessibility in its educational outreach and engagement.

Reputation and Activity Level Formulas Integrated with Coverage and Inclusivity:

The comprehensive formula for the reputation ($Rep$) of an educational institution, integrating coverage and inclusivity, is given by:

$$Rep={\displaystyle \frac{{\sum }_{i=1}^n{w}_i·r_i+\Lambda ·{\sum }_{j=1}^m{v}_j·e_j+\alpha ·Cov+\beta ·I}{n+\Lambda ·m+\alpha +\beta }}$$

(4)

The activity level ($Act$) formula, reflecting engaged participants and the impact of inclusivity and coverage, is:

$$Act={\displaystyle \frac{1}{T+S}}\left(\sum _{i=1}^n{w}_i·\left|r_i\right|\right)+\Gamma ·Cov+\Delta ·I$$

(5)

This structured approach enables a detailed examination of the dynamics, incentives, and outcomes in the complex ecosystem described, highlighting the significance of balancing competition with cooperation, the importance of honesty and verification, and the adaptive nature of strategic decision-making in a decentralized educational network.

3.4. Optimization Techniques in BANFES: Strategic Alliances and Trust Dynamics

The blockchain relies on consensus nodes to maintain network integrity. Rational nodes aim to maximize their utility, while malicious ones may attempt to disrupt the network. Traditional solutions like Byzantine Fault Tolerance (BFT) protocols [56] are limited by centralized control and small node groups, making them unsuitable for decentralized, large-scale blockchain networks. Optimization methods based on Markov Decision Processes (MDP) [53] address node behavior but overlook interactions. Game Theory [57] emerges as an alternative, modeling strategic interactions among rational decision-makers.

Game Theory offers mathematical tools to examine interactions among rational agents, where each agent, or player, selects strategies to optimize their outcomes, taking into account the strategies of others. These concepts include the player, who is an agent such as a miner or blockchain user; the utility, which is the expected benefit or outcome for the player; the strategy, being the actions a player takes influenced by their own and others’ actions to achieve their desired outcome; and rationality, the assumption that players aim to maximize their own utility.

Analyzing node strategies and interactions enables the prediction of mining behaviors and the development of incentive mechanisms to deter misbehavior and attacks. Thus, it is a natural fit for decision-making among blockchain consensus nodes. In this context, Game Theory analyzes how rational players make decisions to maximize their outcomes in a scenario, often using mathematical tools. Players (like investors, miners, validatators nodes, educators, and learners) choose strategies to optimize their utilities, such as rewards or fees. A non-cooperative game, where players act independently without forming alliances, is particularly relevant. Here, all actors compete for rewards without directly coordinating their actions. The concept of Nash equilibrium is essential, indicating that no participant can gain by altering their strategy while others maintain theirs unchanged. This equilibrium facilitates the analysis of diverse blockchain dynamics, encompassing financial resource allocation, accessibility, content management, and security concerns. It ensures that strategies remain viable despite the competitive landscape.

Definition 1. 

• Let $N=\{1,2,\dots ,n\}$ denote the set of educational units within the network.
• $S_i$: Strategy space for educational unit i.
• $u_i(s_i,s_{-i})$: Utility function for unit i, dependent on its own strategy $s_i$ and the strategies $s_{-i}$ of other units.
• $C\subseteq N$: A coalition of educational units.
• $v\left(C\right)$: Value function for coalition C, indicating the total benefits achieved through cooperation.

Cooperative game for service coverage:

$$v\left(C\right)=\sum _{i\in C}{\beta }_i-{\gamma }_{\left|C\right|}+\delta ·I\left(\mathit{full}\mathit{coverage}\right)$$

Mechanism design for honest reporting (Figure 4):

$$u_i(s_i,s_{-i})=\alpha ·v\left(C_i\right)-\kappa ·p_{\mathit{dishonest}}+\lambda ·p_{\mathit{honest}}$$

Evolutionary dynamics:

$${\dot{x}}_i=x_i\left(u_i(s_i,s_{-i})-\overline{u}\right)$$

3.4.1. Linear and Mixed-Integer Linear Programming

Linear programming (LP) and mixed-integer linear programming (MILP) are foundational techniques in operations research and computational optimization. LP is widely recognized for its ability to model and solve resource allocation problems within educational systems [58]. MILP extends the capabilities of LP by allowing for binary or integer variables, enabling more complex decision-making scenarios such as the scheduling of educational content delivery [59]. These techniques facilitate efficient resource management and scheduling within BANFES, ensuring that educational content is distributed in accordance with institutional priorities and constraints.

3.4.2. Genetic Algorithms and Particle Swarm Optimization

This section discusses how certain algorithms—like genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE)—help solve complex problems by finding good solutions efficiently. GA, inspired by the process of natural selection, has been effectively applied to personalization of learning paths, taking into account students’ learning preferences and performance [60]. PSO, which simulates social behavior patterns, further enhances this personalization by iteratively improving the quality of the solution through the collective learning experiences of a population of particles [61]. DE, utilizing mechanisms of mutation, crossover, and selection among candidate solutions, complements these methods by offering a straightforward, yet powerful approach to adapting educational content based on evolving student needs and preferences [62,63]. These algorithms are particularly valuable in BANFES, where they dynamically adapt learning paths to maximize educational outcomes, leveraging their unique strengths to address various aspects of the optimization process.

3.4.3. Model Variables and Functions of Quality Competition and Market Dynamics

This section introduces a simulation model that considers various factors such as network effects, changing consumer preferences, and costs related to improving quality.

$$\begin{array}{cc}\hfill & \mathrm{Let}{q}_i\left(t\right)\mathrm{denote}\mathrm{the}\mathrm{quality}\mathrm{level}\mathrm{of}\mathrm{the}\mathrm{education}Rep\times Act\times E(C,M,D)\mathrm{provided}\hfill \\ & \mathrm{by}\mathrm{node}i\mathrm{at}\mathrm{time}t.\hfill \\ \hfill & \mathrm{Let}c(q_i\left(t\right),t)\mathrm{denote}\mathrm{the}\cos \mathrm{t}\mathrm{of}\mathrm{providing}\mathrm{quality}\mathrm{level}{q}_i\left(t\right)\mathrm{for}\mathrm{node}i\mathrm{at}\mathrm{time}t.\hfill \\ \hfill & \mathrm{Let}{p}_i(q_i\left(t\right),t)\mathrm{denote}\mathrm{the}\mathrm{price}\mathrm{charged}\mathrm{by}\mathrm{node}i.\hfill \\ \hfill & \mathrm{Let}D(p_i(q_i\left(t\right),t),q_i\left(t\right),t)\mathrm{denote}\mathrm{the}\mathrm{demand}\mathrm{for}\mathrm{the}\mathrm{product}\mathrm{from}\mathrm{node}i.\hfill \\ \hfill & \mathrm{Let}{N}_i\left(t\right)\mathrm{denote}\mathrm{the}\mathrm{network}\mathrm{effect}\mathrm{for}\mathrm{node}i’\mathrm{s}\mathrm{product}\mathrm{at}\mathrm{time}\mathrm{t}.\hfill \\ \hfill & \mathrm{Let}{\pi }_i\left(t\right)\mathrm{denote}\mathrm{the}\mathrm{profit}\mathrm{for}\mathrm{node}i\mathrm{at}\mathrm{time}t.\hfill \end{array}$$

The profit function for node i is given by:

$${\pi }_i=p_i\left(q_i\right)·D(p_i\left(q_i\right),q_i)-c\left(q_i\right)$$

(6)

This function aims to capture the profit generated by considering both the revenue from demand and the cost of providing quality.

Objective:

$$\mathrm{Maximize}{\pi }_i\mathrm{with}\mathrm{respect}\mathrm{to}{q}_i\mathrm{and}{p}_i\left(q_i\right).$$

(7)

This maximization reflects the intention of node i to find the optimal quality level and the pricing strategy that will yield the highest profit.

The model includes constraints to ensure feasible solutions:

$$q_i\ge q_{min},$$

(8)

$$S_i={\displaystyle \frac{D(p_i\left(q_i\right),q_i)}{{\sum }_{j=1}^{n}D(p_j\left(q_j\right),q_j)}}\le S_{max},$$

(9)

where $S_i$ is the market share of node i, $S_{max}$ is the maximum allowable market share, and n is the total number of nodes.

Node’s Quality Dynamics: Quality dynamics is governed by the following differential equation, which models how quality evolves over time for node i:

$${\displaystyle \frac{d{q}_i\left(t\right)}{dt}}=f_i(q_i\left(t\right),N_i\left(t\right),{\overrightarrow{q}}_{-i}\left(t\right),t)-g_i\left(c(q_i\left(t\right),t)\right)$$

(10)

Network Effect Dynamics: The dynamics of the network effect for node i’s product are described by:

$${\displaystyle \frac{d{N}_i\left(t\right)}{dt}}=h_i\left(D(p_i(q_i\left(t\right),t),q_i\left(t\right),t)\right)-\delta N_i\left(t\right)$$

(11)

Node’s Profit Dynamics: The change in profit over time is captured by the profit dynamics equation:

$${\displaystyle \frac{d{\pi }_i\left(t\right)}{dt}}=p_i(q_i\left(t\right),t)·D(p_i(q_i\left(t\right),t),q_i\left(t\right),t)-c(q_i\left(t\right),t)-v_i\left(N_i\left(t\right)\right)$$

(12)

The functions and variables $f_i$, $g_i$, and ${\overrightarrow{q}}_{-i}$ play a critical role in capturing the complex interactions and strategies of the nodes over time. $f_i(q_i\left(t\right),N_i\left(t\right),{\overrightarrow{q}}_{-i}\left(t\right),t)$ represents the rate of quality improvement effort by node i, influenced by its own current quality level $q_i\left(t\right)$, the network effect $N_i\left(t\right)$, the quality levels of competing nodes denoted by ${\overrightarrow{q}}_{-i}\left(t\right)$, and time t, capturing how a node’s decision to improve its product quality is impacted by its market position, the value derived from a growing user base, the actions of its competitors, and the changing conditions over time. $g_i\left(c(q_i\left(t\right),t)\right)$ signifies the impact of the cost of providing a certain quality level on the rate of quality improvement, reflecting how costs influence a node’s investment in quality improvement. ${\overrightarrow{q}}_{-i}\left(t\right)$, the vector of quality levels for all nodes except node i, models the competitive landscape, indicating that strategic decisions regarding quality improvement are influenced by the quality levels and strategies of competitors, facilitating an analysis of strategic interactions among nodes in a competitive market. Also, the function, $v_i\left(N_i\left(t\right)\right)$, represents the additional costs or investments required by node i to maintain or enhance the network effect $N_i\left(t\right)$ of its product.

Objective and Solution Method: Each node aims to maximize its integrated profit over a planning horizon T

$$\underset{q_i\left(t\right),p_i(q_i\left(t\right),t)}{max}{\int }_0^T{e}^{-rt}{\pi }_i\left(t\right)dt$$

(13)

This model underscores the strategic interplay and dynamic competition within blockchain networks for education, highlighting pathways for enhancing educational access and quality through sophisticated AI and optimization techniques.

3.4.4. Theoretical Justification

Strategic Interactions Among Nodes: The model’s assumption about strategic interactions among nodes is grounded in the theory of oligopolistic competition, aligning with established models such as Cournot, Bertrand, and Stackelberg. These models illustrate how firms in markets with few competitors make decisions based on the actions of others [64,65].

Rate of Quality Improvement Effort ($f_i$): The function that captures the rate of quality improvement by a node is supported by Schumpeter’s theory of economic development, which posits that firms invest in Research and Development (R&D) and quality improvements for competitive advantage [66]. The inclusion of network effects in this function is based on network economics principles [67].

Impact of Cost on Quality Improvement ($g_i$): The diminishing returns principle, a fundamental concept in microeconomics, supports the model’s depiction of how costs impact quality improvement efforts [68].

Competitive Landscape (${\overrightarrow{q}}_{-i}$): The influence of the competitive landscape on the strategic decisions of a node is based on the concept of competitive positioning, highlighted by Porter’s Five Forces Framework [69].

3.4.5. Higher Education Program Transaction Costs

The framework suggests adopting the Cardano platform for its reduced transaction fees and improved energy efficiency, due to its proof-of-stake protocol. For an educational system, transaction costs can be estimated based on activity level, encompassing content amount and quality, student graduation rates, and graduate quality. The Cardano system facilitates transaction cost measurement in ADA, affected by the computational complexity, storage requirements, and network bandwidth utilized by smart contracts and transactions.

In a prototype, the cost implications of deploying three essential smart contracts—Authority, Curriculum, and Diploma—would be considered. The transaction costs within the Cardano system would be analyzed for each contract’s functions. For example, contract deployment costs, which are generally one-time, would be higher due to the initial data input size and the complexity of configuring internal variables. Transaction costs for regular operations like registering new students, logging course completions, and issuing diplomas would also be calculated. Compared to Ethereum, where cost fluctuations can be significant, Cardano’s costs are expected to be more predictable and stable over time. The Cardano blockchain would also take into account the number of transactions a student is expected to engage in during their academic journey, from enrollment to graduation. The goal is to maintain manageable costs, ensuring accessibility for all students, particularly in financially constrained regions.

3.4.6. Lifelong Learning

In “Creating a Learning Society: A New Approach to Growth, Development, and Social Progress”, Joseph Stiglitz and Bruce C. Greenwald discuss how a society’s ability to learn and accumulate knowledge is essential for economic growth [70]. They highlight the decreasing lifespan of skills, now around five years, and note the trend of shorter job tenures, currently averaging 4.2 years. This reality emphasizes the need for continuous learning to maintain professional relevance and advancement.

A key challenge in lifelong learning is the seamless transfer of educational records, complicated by the fragmentation of Learning Record Stores (LRSs) into isolated data silos. The Blockchain of Learning Logs (BOLL) system addresses this by using blockchain technology to securely and immutably record and share learning data across institutions. This ensures data integrity and supports personalized learning through smart contracts, making it easier to track and recognize lifelong educational achievements [13].

4. Future Prospects

To implement this project, a semi-centralized system will initially be designed and launched with ten schools (from grades 6 to 12) and three universities. These educational units will actively participate in a consensus for one year. To achieve greater transparency and reproducibility of the obtained data, which includes educational databases, surveys, or interviews with stakeholders in the Afghan education sector, more precise methodological execution will be implemented. The analytical methods used, both qualitative and quantitative, will be detailed, and the software and tools used for data analysis, as well as the criteria for including or excluding data, will be reported. Methods used to evaluate the effectiveness of the BANFES system, including pilot tests, user feedback loops, and metrics for measuring learning outcomes and system efficiency, will also be documented.

Current empirical studies demonstrate the effectiveness of similar technological interventions in education. For example, a study by Kumar et al. [71], which investigated the impact of blockchain technology in educational settings in South Asia, provides evidence of increased engagement and improved educational outcomes. Additionally, research by Li and Daniels [72] on AI-driven personalized learning systems in resource-poor areas serves as evidence of the practical applications and benefits of these technologies in challenging environments like Afghanistan.

4.1. The Potential of AI in Education

Artificial intelligence (AI), encompassing machine learning, decision-making, and problem-solving capabilities traditionally requiring human intelligence, has widespread adoption across various sectors, including education. This expansion has been particularly notable in the educational sector, where AI’s journey from its nascent stages in the mid-20th century to the present day has been marked by significant milestones. Initially centered on content delivery, the advent of personal computing and the Internet later paved the way for more sophisticated educational software, culminating in the current era where AI facilitates personalized learning, administrative efficiency, and tutoring services [73].

AI’s transformative impact on education is evident in several key areas, notably in personalized learning, where it offers tailored educational experiences to meet individual learners’ needs, interests, and abilities. These personalized learning experiences are made possible through AI-driven adaptive learning systems, custom content recommendations, personalized instruction, and the early identification of learning gaps [73]. Additionally, AI plays a crucial role in streamlining administrative processes and providing tailored tutoring and mentorship. For example, Luckin et al. (2016) demonstrated how AI can automate routine administrative tasks, such as grading and scheduling, allowing educators to focus more on student interaction and personalized instruction [74]. Furthermore, AI-driven platforms like Coursera and Khan Academy use intelligent algorithms to offer personalized tutoring, adapting to each learner’s pace and style, significantly improving learning outcomes [75,76].

As AI continues to evolve, it promises to further revolutionize education through the development of AI-powered educational games, enhanced tutoring systems, and greater administrative efficiency. The successful integration of AI into education hinges on addressing ethical considerations and ensuring its inclusive and effective use [77].

4.1.1. AI Integration and Functionality in Education

The integration of AI into education necessitates adaptive systems capable of leveraging advanced technologies such as speech recognition, semantic web, intelligent agents, educational data analysis, and computer vision. The comprehensive toolkit provided by AI, including machine learning, deep learning, reinforcement learning, natural language processing, and large language models, is instrumental in customizing educational experiences to each learner’s unique needs [78,79,80].

This technological evolution offers opportunities to enhance educational outcomes in numerous ways, from employing LLMs for personalized language learning to utilizing AI in coding and digital literacy, thereby boosting employability. Further, it advocates for the support of technological entrepreneurship and lifelong learning through AI, aiming to create an adaptive educational ecosystem that emphasizes personalized, accessible learning [81].

AI’s application as intelligent tutees and learning tools exemplifies a learner-centered approach, enhancing engagement and understanding by facilitating interactive, personalized learning experiences [77]. Research by Woolf et al. (2013) highlighted the efficacy of AI-powered intelligent tutoring systems in providing real-time feedback and personalized learning paths, which lead to better student performance and engagement [82]. Furthermore, AI systems like IBM’s Watson Education and Squirrel AI have been successfully implemented in various educational settings, demonstrating their ability to act as intelligent learning companions that support and challenge students based on their individual learning needs [83,84].

Recent analyses and empirical studies have provided insights into AI’s application in education, offering guidelines for educators and AI specialists. These studies underscore the importance of interdisciplinary collaboration, the exploration of underutilized AI technologies, and the need for a balanced approach to AI integration in education, addressing efficiency, security, and ethical considerations [85,86,87,88].

4.1.2. Integrating AI and Blockchain for Personalized Education

The traditional education system often follows a one-size-fits-all approach, limiting personalized learning and self-assessment opportunities. Thanks to the emergence of blockchain technology and the progress in machine learning, it is feasible to establish a decentralized educational framework that prioritizes the unique learning preferences and goals of each student. Ouyang et al. [89]. examined AI’s evolving role in education through three paradigms: AI-directed, emphasizing programmed learning; AI-supported, fostering learner-AI collaboration; and AI-empowered, where learners utilize AI to lead their education.

This study highlights AI-directed learning, demonstrating how the platform, discussed in Section 3, allows learners to self-assess and peer review, using blockchain to securely record progress and achievements for a personalized, AI-enhanced educational experience. This environment securely logs educational progress for adaptive learning experiences, and applies deep knowledge tracing (DKT) for outcome predictions [90]. This approach empowers learners to navigate their educational paths, predict their learning outcomes, and tailor their study plans to meet their individual needs.

4.1.3. Machine Learning for Personalization

Utilizing recurrent neural networks (RNNs), specifically the methodology outlined in the DKT approach, the system models each student’s knowledge state over time. This modeling allows for predicting a student’s performance on future assessments based on their historical data and self-assessments.

Simulation of Learning Process and Performance Prediction: To validate BANFES, the learning process is simulated using a dataset that mirrors the interactions of students with educational materials. An RNN architecture based on the Deep Knowledge Tracing model is used to monitor and forecast student performance over time. The simulation includes:

• 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.

Expected Results and Discussion: The impact of self-assessment on student learning is significant. Self-assessment practices are associated with improved academic performance because they enable students to reflect on their learning, identify areas of strength and weakness, and take control of their educational journey. This reflective practice cultivates self-regulated learning behaviors, fostering an environment where students can adaptively enhance their study strategies and learning materials, leading to a more personalized and efficient learning experience [91]. By enabling students to evaluate their own understanding and adjusting the learning material accordingly, BANFES promotes a more personalized and efficient learning experience. Additionally, the blockchain component ensures that all student data and achievements are securely managed and verifiable.

4.1.4. Leveraging AI for Enhanced Educational Outcomes

The integration of artificial intelligence (AI) in educational settings heralds a transformative era, promising to redefine pedagogical approaches, advance educational sustainability, and tackle prevailing challenges in the sector, as highlighted by the United Nations Educational, Scientific and Cultural Organization (UNESCO) [92]. AI’s revolutionary impact is evident across educational systems, enhancing institutional competitiveness, and empowering both educators and learners alike [93]. The evolving learning paradigms underscore a shift towards personalized and autonomous learning experiences, as detailed in various studies [89]. Hwang et al. underscored the value of customized guidance, integrating emotional and cognitive dimensions into an adaptive learning system tested within a fifth-grade mathematics context, showcasing its efficacy in improving student performance and reducing anxiety [94]. Similarly, Bajaj et al. introduced an AI-driven framework aimed at identifying students’ learning preferences, employing various AI methodologies for adaptive learning enhancements [95]. The AI discourse of the Organization for Economic Co-operation and Development’s (OECD) emphasizes its role in fostering critical thinking and creativity among students, alongside addressing dropout rates through predictive analysis, albeit with a cautious note on data privacy and ethical use [96]. The potential advantages, alongside the challenges and essential considerations for AI’s successful educational implementation, are succinctly outlined in the

Table 3. Exploring AI’s impact on education: prospects, hurdles, and implementation insights [97].

Category	Details
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.

.

4.1.5. Navigating Ethical and Policy Dimensions of AI in Education

The integration of AI and blockchain technologies in educational settings introduces significant ethical concerns, particularly in the domains of data privacy and security. Blockchain’s decentralized structure and encryption can potentially enhance the security and integrity of educational records, making them resistant to tampering while promoting transparency. However, the immutable nature of blockchain conflicts with privacy rights such as data rectification and the right to be forgotten, crucial under privacy regulations like the General Data Protection Regulation (GDPR).

Moreover, AI technologies, employed to personalize learning experiences, involve the processing of extensive personal data, raising issues related to data misuse, algorithmic bias, and the risk of surveillance. Ensuring ethical application of AI involves adhering to transparent data handling practices, implementing effective bias mitigation strategies, and conforming strictly to privacy legislation [17,98,99].

Additionally, the ethical, social, and policy landscapes of AI in education demand careful consideration, as underscored by Vincent-Lancrin et al., who discuss the imperative of balancing AI benefits against privacy and human rights, suggesting a shift towards GDPR-inspired regulations [96,100]. The nuanced relationship between AI advancements and traditional educational methodologies, alongside the ethical implications on educator-student dynamics, has been explored, advocating for an AI-specific ethical framework and inclusive policy formulations to mitigate risks and foster beneficial AI integration in educational ecosystems [101,102,103,104,105].

4.1.6. Prospective AI Contributions and Global Educational Shifts

Investigations into AI’s potential within education reveal its capacity to redefine traditional learning paradigms, introduce efficiencies, and personalize student learning experiences. The discourse ranges from exploring AI’s role in enhancing cognitive skills through machine learning and deep learning technologies to its implications for future employment and ethical considerations [106,107,108,109,110,111,112,113,114,115,116,117,118]. These insights converge on the notion that AI’s integration into educational frameworks promises to significantly enhance learning outcomes, streamline administrative processes, and foster inclusive, personalized learning pathways, albeit with an emphasis on ethical usage, data transparency, and collaborative research for its effective and responsible application. The graphical representation in Figure 12 visualizes AI’s application in education, delineating both its benefits and challenges.

4.2. AI and Women’s Education

AI’s interpretation of behavioral data to tailor educational pathways is a trend gaining traction worldwide, with projects from the UK to Japan utilizing predictive analytics and AR technologies to tackle educational challenges. This highlights AI’s crucial role in learning. China’s aim to lead in AI innovation by 2030, with extensive educational initiatives and improved vocational training, reflects a global push to align education with AI advancements [119,120].

The UNESCO-Ericsson partnership showcases strategic efforts to use AI for youth skills development, emphasizing the need for educational content that meets current labor market needs and advocating for ongoing curriculum innovation in light of rapid AI developments [120].

AI’s success in education, shown through adaptive learning platforms and automated grading, underlines its potential to personalize learning and streamline processes. These achievements signal a shift towards more adaptable and inclusive educational models, ready for the future’s challenges [121,122,123].

Artificial intelligence (AI) is based on the notion that human cognitive processes can be replicated in computers for task execution. Alan Turing introduced the idea of considering humans in mechanical terms to progress AI. Rosalind Picard’s research in affective computing indicates that for computers to be truly intelligent, they must understand emotions. Developments in AI have facilitated the impartation of human characteristics to robots, leading to concerns about AI inheriting societal biases, such as gender and racial biases. Donna Haraway’s work critiques traditional feminism and promotes a cyborg viewpoint, diminishing the distinctions between humans and machines. However, concerns about AI reinforcing societal biases, particularly gender bias in AI and human–robot interactions, persist [124,125]. This segment examines AI’s impact on women, focusing on education.

AI’s role in enhancing accessibility for women in remote areas is significant. AI-driven telemedicine provides essential healthcare services, especially in gynecology, overcoming cultural barriers. AI-enabled educational platforms deliver personalized learning experiences, making education more accessible. AI also assists in providing tailored financial services, improving agricultural decision-making for female farmers, and enhancing safety through emergency aid applications [126].

AI technologies remove language barriers with speech recognition and translation, promote entrepreneurship by connecting women to broader marketplaces, and foster communities through virtual spaces for collaboration and support. The use of AI across various sectors significantly empowers women, promoting equality and improving access to essential services [127].

AI-Powered Education Platform for Women in Afghanistan

In Afghanistan, AI offers a unique opportunity to overcome educational restrictions faced by women due to political challenges. AI platforms can provide secure and culturally sensitive access to educational content, allowing women to pursue education safely. These platforms are customizable, supporting personalized learning and accommodating cultural sensitivities and language preferences. They can facilitate a range of educational content from basic literacy to vocational training and academic courses, incorporating community and mentorship features for a comprehensive learning experience.

Table 4. AI-powered education platform.

Categories	Details
Core Objectives:	Empower Afghan women with education. Align content with cultural values. Ensure user safety and privacy. Accommodate different tech literacy levels.
Key Features:	Personalized learning with AI: adaptive paths, language support. Educational content: literacy, vocational training, academic courses, life skills. Community and mentorship: peer learning, global mentorship. Access: offline modules, low-bandwidth optimization, mobile app. Relevance and safety: culturally tailored content, encrypted access, anonymous profiles.

outlines the platform’s core objectives and key features, emphasizing empowerment, safety, accessibility, and the relevance of educational content.

Implementing the AI-powered education platform requires collaboration with local and international partners, community engagement, and continuous refinement based on feedback. Addressing challenges like the digital divide and cultural acceptance is crucial, with strategies for security and privacy enhancing user trust and safety.

4.3. Advancing toward Inclusive Education in Afghanistan: A Multifaceted Approach

In the quest to overcome educational barriers within connectivity-challenged rural landscapes, a comprehensive strategy embracing cloud-powered e-learning, strategies for low bandwidth, micro-cloud technologies, renewable energy solutions, cost-effective technological tools, and initiatives in space education is deployed. This holistic approach assures the expansion of educational resource access, even within isolated locales. It entails the development and distribution of downloadable, compressed educational materials, the implementation of local cloud solutions for offline access, the utilization of solar power for information and communication technology (ICT) infrastructure, the integration of affordable digital learning devices, and the incorporation of space technology to bolster interest in science and technology. Collectively, these initiatives seek to close the digital gap, rendering quality education reachable and equitable across varied environments.

4.3.1. Overcoming Connectivity Obstacles in African Rural Education through Cloud-Enabled E-Learning

The African Union [128] reports that a significant segment of Africa’s populace resides in rural sectors, where limited Internet connectivity significantly hampers educational progress. The application of cloud-based e-learning frameworks offers a hopeful solution to this digital divide, as evidenced by pioneering methods in Kenya’s educational system.

Cloud technology stands as a pivotal innovation in e-learning, enabling scalable, flexible educational resource access. Li and Lalani [129] propose that cloud computing has the capacity to democratize education, equipping remote and underprivileged communities with educational content quality comparable to urban centers. Specifically, in rural African contexts with unpredictable Internet availability, cloud-based e-learning solutions permit the local storage of educational content, allowing for digital resource access sans constant Internet connection [130].

Kenya’s implementation of cloud-based e-learning platforms, like the eLimu project, demonstrates the efficacy of such technology in improving educational accessibility and quality. The eLimu initiative employs local servers to provide interactive learning content, enabling students in rural schools to utilize digital learning resources despite inadequate Internet infrastructure [131]. This exemplar validates the capacity of cloud-enabled e-learning to navigate connectivity barriers, fostering a more inclusive, equitable educational environment.

Moreover, this adoption of cloud-based educational technologies is in line with UNESCO recommendations, which stress the role of ICT in elevating educational standards and supporting the attainment of Sustainable Development Goal 4 (Quality Education) through equitable access to quality education and the promotion of lifelong learning for all [132].

4.3.2. Strategies for Low-Bandwidth Online Learning

The transition towards online education necessitates the formulation of accessible teaching methodologies for students facing Internet constraints. Tenkorang and Welch [133] underscore the necessity of adapting e-learning materials to ensure equitable access, particularly in regions suffering from inadequate Internet infrastructure. Essential tactics include the provision of brief, downloadable video content to minimize data consumption, the compression of video files to facilitate easy access without quality compromise, the availability of text transcripts to cater to various learning preferences and to enhance accessibility, and the design of mobile-first interfaces for learners dependent on smartphones for Internet access. These strategies are aimed at diminishing the obstacles posed by limited Internet availability, thus advocating for a more inclusive online educational ecosystem.

4.3.3. Bridging the Educational Digital Divide through Micro-Cloud Innovations

Micro-cloud strategies offer a promising avenue for addressing the educational digital divide, especially in underserved rural communities. The introduction of micro-clouds, such as the C3 micro-cloud, plays a crucial role in overcoming connectivity challenges by creating a localized content repository accessible through Wi-Fi. This approach enables the use of cloud-based learning materials without the need for continuous Internet connectivity. The C3 micro-cloud system, for instance, has been recognized for its capability to provide an accessible, on-demand learning atmosphere, supporting the dissemination of educational content and resources in locations with scarce or non-existent Internet infrastructure [134]. Such a model is particularly relevant for countries like Afghanistan, where Internet access disparity aggravates educational disparities in rural and remote areas. Evidence suggests that employing technology in this manner not only boosts learning opportunities but also advances digital literacy among marginalized groups [135,136]. Additionally, the application of micro-clouds in educational contexts has shown significant promise in enhancing student engagement and outcomes by ensuring consistent access to educational materials and enabling collaboration between educators and students under challenging circumstances [137].

4.3.4. Harnessing Renewable Energy in Rural Educational Environments

In locales marked by infrastructural limitations, especially within rural settings, the incorporation of renewable energy systems stands out as a critical solution for powering ICT infrastructure in the education sector. Solar energy, in particular, presents a sustainable and viable power source essential for the uninterrupted operation of electronics and services crucial for education [138]. Chaurey and Kandpal [139] highlight the importance of photovoltaic systems in closing the energy gap for off-grid rural communities, thereby facilitating access to educational resources and digital platforms. Additionally, Majumder [140] supports the value of renewable energy sources, specifically solar power, in bolstering ICT infrastructure in schools located in areas with limited conventional power grid access. The implementation of renewable energy within education is aligned with sustainable development goals, endorsing environmental sustainability [141]. The use of renewable energy sources, like solar panels, to power ICT infrastructure represents a strategic approach to overcoming infrastructural challenges in rural educational settings.

4.3.5. Adopting Cost-Efficient Hardware and Ad Hoc Digital Networks for Educational Fairness

The emergence of affordable hardware platforms, such as Raspberry Pi, and the innovation of ad hoc digital network technologies like DakNet, herald a new phase in the democratization of digital learning materials and online educational platforms’ access. The Raspberry Pi, developed by the UK-based Raspberry Pi Foundation, exemplifies how economical computing devices can facilitate educational endeavors in resource-poor regions [142]. DakNet technology, originating from the MIT Media Lab, offers an innovative method for setting up ad hoc digital networks that bypass traditional Internet infrastructure limitations, thus providing vital access to educational resources in isolated areas [143]. Research by Smith et al. emphasizes the transformative impact of these technologies in fostering interactive and participatory learning experiences, especially in settings where access to standard Internet services is limited [144]. These initiatives align with the Sustainable Development Goals’ emphasis on ensuring inclusive and equitable quality education for all, underscoring the essential role of technology in narrowing the digital divide [145].

4.3.6. Enriching Education via Space: The Influence of CubeSats and NASA’s Endorsement

CubeSats, small-scale satellites for space research comprised of multiples of 10 × 10 × 10 cm cubic units and having a mass no greater than 1.33 kg per unit, utilize commercial off-the-shelf (COTS) components for their electronics and structure. This renders them an affordable option for conducting space research and technological demonstrations, enabling universities, high schools, and private entities to engage in space exploration [146].

The maintenance cost of a CubeSat over a 5-year period can vary significantly, influenced by the mission’s complexity, design, and requirements for ground support, data management, and operations. Factors impacting maintenance expenses encompass tracking and communication, data analysis, and updates to hardware and software, alongside personnel costs. Maintenance expenditures can range from minimal for brief, autonomous missions to several hundred thousand dollars for more intricate missions necessitating comprehensive ground support and data analysis over extended durations [147].

NASA promotes the CubeSat concept through various initiatives aimed at educational and Higher Education Institutions (HEIs). The CubeSat Launch Initiative (CSLI), for instance, offers small satellite payload launch opportunities, targeting educational institutions, non-profit organizations, and NASA centers to foster hands-on space mission experiences [148]. By providing launch opportunities, NASA mitigates one of the significant barriers to space access for educational payloads.

Additionally, NASA supports educational endeavors through CubeSats by offering grants and partnerships to universities and colleges for CubeSat technology development and space research [149]. These efforts seek to inspire future scientists and engineers, offering practical spacecraft design, construction, and operation experience.

4.3.7. Satellite Solutions for Overcoming the Educational Divide

Satellite technology, particularly when leveraging higher orbits such as geostationary orbit, presents a viable solution for bridging the educational divide in remote areas. These satellites, positioned approximately 35,786 km above the Earth, provide steadfast and continuous coverage to designated terrestrial zones. The fixed position of geostationary satellites makes them ideal for broadcasting educational content directly to isolated locations. The implementation of educational initiatives via high-orbit satellites allows for the direct transmission of both television and Internet signals, facilitating access to educational resources in distant regions. Students and schools in remote areas can easily receive these signals using standard satellite dishes and receivers or modems. The content distribution process is managed by ground stations, which prepare and upload the educational material to the satellite. This material can then be broadcast to the targeted regions either continuously or as per a predetermined schedule, ensuring consistent and reliable educational outreach [150,151].

5. Conclusions

This paper has introduced a hybrid system designed to surmount the socio-political and infrastructural challenges in Afghanistan’s education sector. By integrating blockchain and artificial intelligence, BANFES not only preserves educational continuity in times of adversity but also pioneers a model for adaptive, secure, and scalable educational ecosystems globally.

At its core, BANFES utilizes blockchain technology to create an immutable record of academic achievements, thus establishing a reliable framework for educational integrity. Artificial intelligence, particularly through machine learning, customizes the learning experience to each student’s needs, enhancing engagement and effectiveness in challenging educational environments.

The system’s application of advanced analytics, Game Theory, and computational modeling supports sophisticated decision-making processes. These methods ensure that educational strategies are not only aligned with individual learners’ goals but also resonate with broader societal needs. This alignment fosters a cooperative educational environment ripe for continuous growth and innovation.

The insights garnered from the deployment of BANFES are valuable to the global educational narrative, especially as the world gravitates towards more decentralized and student-centered learning models. By demonstrating how technology can bridge the gap between potential and access, BANFES offers a blueprint for leveraging tech-based solutions to enhance educational access and equity worldwide.

Looking ahead, the successful scaling of BANFES requires active collaboration with educational practitioners and stakeholders. Pilot programs, grounded in this paper’s theoretical explorations, will test the practical applications of blockchain and AI in diverse educational settings. Additionally, securing partnerships and funding from educational bodies, technology firms, and international organizations will be crucial in refining and expanding the system’s capabilities.

Continuous evaluation and feedback will be instrumental in iterating BANFES to meet changing educational demands and technological advancements. Ultimately, BANFES is more than a technological innovation; it is a beacon of hope for marginalized populations and a foundational step towards an inclusive, enlightened global educational landscape.