Abstract
Artificial intelligence as a component of education technology and financial technology is burgeoning and its capacity for disruption is being demonstrated in some of the most dominant use cases in these sectors. As this emergence takes place, there is a need to ensure that appropriate governance and stakeholder engagement are in place to facilitate data privacy policies that minimise risk, bias, discrimination, and other unintended consequences that could occur from the use of these technologies. This chapter delves into the state of artificial intelligence in the EdTech and FinTech sectors, the current stakeholder, governance, and data privacy challenges and recommends how the interplay of stakeholder engagement, regulation, and governance at the regional, national, organisational and project levels can contribute to robust data privacy policies in these sectors.
TopBackground
Though established several decades ago, the use of AI has shown a marked rise in recent years due to advancements in machine learning, computing power, capacity to store data and networks (Biallas and O'Neill, 2020). In fact, in the 2020s, AI has increasingly become a dominant technology (Hilpisch, 2020). The past several years has seen the proliferation of AI in every aspect of industry, becoming a fundamental and disruptive force in all sectors including in the Education Technology (EdTech) and Financial Technology (FinTech) sectors.
Research has shown that the integration of AI into the EdTech and FinTech sectors is increasingly progressive. EdTech, as recorded in higher education, has witnessed a sharp increase in the integration of AI into the sector since 2020, with a 150% rise from the two years before that and including main use cases such as Assessment/Evaluation, Predicting, AI Assistant, Intelligent Tutoring system (ITS) and Managing Student (Crompton, 2023). An economic perspective also highlights the growing prevalence and importance of AI in the sector as the global market for AI in education was at US$4 billion in 2022 and is set to grow to US$32 billion by 2032 (Global Market Insights, 2023).
Likewise, the FinTech sector is being transformed by innovation in use cases such as enhanced customer experience, personalisation, targeted offers and communication, behaviour-based decisions using predictive and prescriptive analytics, credit risk assessment, fraud detection, financial reporting, robo advisors, compliance automation and end-to-end digital banking in the form of neobanks. AI is changing and reshaping this sector in the areas of financial intermediation, compliance, risk management and oversight with the advent of Generative AI set to accelerate this adoption further (Shabsigh & Boukherouaa 2023). Impressively, the global market for AI in the financial technology sector was valued at US$12.11 billion in 2022 and is expected to grow to US$41.15 billion in 2030 (Grand View Research, 2020).
Key Terms in this Chapter
Artificial Intelligence Models: A set of computer programmes that have been trained to take decisions by themselves e.g. through pattern recognition.
EdTech: Education Technology entails the application of technology to processes, procedures and practices within the education sector in order to improve outcomes.
Stakeholder Engagement: A process of communicating and collaborating with those who will be affected by the outcome of an event or plan.
Artificial Intelligence: Technology capable of achieving tasks that would traditionally need human intelligence.
Web Scraping: A technique used to harvest data from the internet in order to train artificial intelligence models or for other purposes.
Generative Artificial Intelligence (AI): The type of AI capable of producing videos, images, text etc in response to natural language used as prompts.
Data Privacy: The endeavours to ensure that data is collected, used and stored in an ethical manner.
Fintech: Financial Technology entails the application of technology to processes, procedures and practices within the financial sector in order to improve outcomes.