Artificial Intelligence and Blockchain as Disruptive Technologies in Adolescent Lives

Introduction

Artificial Intelligence (AI) and Blockchain technologies are applied in adolescent’s lives to create new possibilities or frontiers. Blockchain technology is viewed as a myriad integrated platform of distributed databases that supports data collection, aggregation, and analysis. The highly secure platform supports audit trails that support the accountability and responsibility of users. Blockchain supports the learning process of adolescents in educational platforms by enforcing accountability. Researchers mainly focused on the integration of the disruptive technologies (Artificial Intelligence and Blockchain technologies), their pros and cons, and their use but not specifically for education in adolescents and how it is implemented (from development, deployment, use, and maintenance) (Anderson et al., 2018; Fedorova & Skobleva, 2020; Sgantzos & Grigg, 2019; Zovko & Gudlin, 2020). The book focuses specifically on the application of disruptive technologies in primary education. The application of AI and Blockchain concepts in education is essential to endow skills that are essential to create or add value in pupils. This can be in form of unearthing new teaching methodologies and synchronization of recent data sources. The technologies have the potential of unearthing new insights and innovations (get more use out of the data). AI algorithms crawl on the web which consists of unstructured, semi-structured, and structured data. Unstructured data such as videos and images on Digital Learning Platforms (DLPs) are indexed to contextualize information on these objects in form of text, thus the creation of value. The unstructured data can be labeled automatically and certified (Anderson et al., 2018).

AI and Blockchain technology makes sharing of data seamless through a matchmaking of contents and this is done in a trusted manner (Ahmad et al., 2021). It is essential to cluster subjects offered, schools, districts, and regions the adolescents are studying. The aggregation of related content results in informing new knowledge domains which are student-centric. The interactive digital learning platform can be blended with various technologies such as supporting drill and practice, tutorials, and simulation activities. Students can view and interact with virtual features or items and receive customized instruction or feedback. Learning using entertaining educational games results in practicing skills and learner productivity through engagement. The use of Artificial Intelligence coupled with data science algorithms on big data supports analytics of student performance, and adaptive learning (student-centered) and offers customized learning activities and resources to address the unique needs of the student (Guan et al., 2020). The collection and analysis of data (data discovery) is enhanced by the cloud model, globalized-networked information communication devices.

Data discovery in DLPs is attained through the partnership of stakeholders such as schools’ authorities and other educators, students, electronic learning platform developers, researchers, policymakers, community members, and students’ guardians with the Ministry of Primary and Secondary Education (MoPSE). The MoPSE has special units such as the Curriculum Development Unit and the Technical Services (CDTS) that focus on quality delivery of education. It focuses on the development, validation, rolling-out and continuous improvement of the curriculum through stakeholder involvement. Stakeholders involved include educationists, industry and commerce experts and captains, local and international educational institutions (primary to tertiary level) (MoPSEC, 2021). CDTS is responsible for validating and giving a ‘proxy’ to publish curriculum material to the public domain. Digital Learning Applications in Zimbabwe include eNhava, Ruzivo, The Learning Passport, and MoPSE e-Library. The rolling out of these applications is certified by the CDTS (Watt, 2018; ZARNet, 2021). The CDTS also supports training activities and awareness campaigns on products on offer. The involvement of stakeholders and teamwork is an essential component of requirements engineering or analysis in any system developed. The DLPs (systems) supported are mainly web-based. Android and iOS applications are still few. The Unstructured Supplementary Service Data (USSD) applications are not supported.