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Impact of Artificial Intelligence Education on Adults’ Understanding of Algorithmic Bias

Abstract

Artificial Intelligence Education is increasingly important as AI systems are being applied in areas such as employment, education, communication, information services, finance, and decision-making. Algorithmic bias occurs when an algorithm or AI system produces systematically unfair, inaccurate, or unequal outcomes, which may arise from limitations in data, system design, assumptions, or the way technologies are developed and applied. Adults who lack sufficient understanding of algorithmic bias may find it difficult to recognize how AI systems can produce or reinforce biased outcomes. This study therefore seeks to examine the impact of Artificial Intelligence Education on adults’ understanding of algorithmic bias and determine whether educational exposure can improve their awareness of bias in AI-supported systems. Artificial Intelligence Education can introduce adults to the basic principles of AI systems and the processes through which algorithms use data and programmed rules to generate outputs or support decisions. Through relevant learning activities, adults can examine concepts such as training data, automated decision-making, pattern recognition, model outputs, and algorithmic bias. Education can also expose learners to examples showing how incomplete, unbalanced, or inappropriate data and system design choices may influence AI outcomes. These learning experiences can provide adults with the conceptual foundation required to examine AI systems more critically. Adults’ understanding of algorithmic bias refers to their ability to explain the concept, recognize circumstances in which AI systems may produce biased outcomes, and understand factors that can contribute to such bias. This understanding enables adults to avoid assuming that automated outputs are automatically neutral, objective, or accurate. Artificial Intelligence Education can strengthen this understanding by providing opportunities to examine AI-generated decisions, compare outcomes, identify potential sources of bias, and discuss the importance of human oversight and critical evaluation when interacting with AI technologies. The study will adopt a suitable descriptive survey research design. The population will comprise adults participating in selected Adult Education programmes, from which an appropriate sample will be selected using a suitable sampling procedure. Data will be collected through a structured questionnaire and an appropriate assessment designed to measure participants’ understanding of algorithmic bias. The research instruments will be subjected to appropriate validation procedures, while their reliability will be established before administration. Data collected will be analysed using relevant descriptive and inferential statistical techniques to determine the impact of Artificial Intelligence Education on adults’ understanding of algorithmic bias. The study is expected to establish that Artificial Intelligence Education has a positive impact on adults’ understanding of algorithmic bias. It is expected that adults exposed to relevant educational activities will demonstrate improved ability to explain algorithmic bias, recognize potential sources of biased AI outcomes, and understand why automated systems may not always produce neutral results. The study may further establish that practical examination of AI-related scenarios helps adults develop greater awareness of the need to question, evaluate, and contextualize AI-supported decisions. The findings are expected to have implications for Adult Education practitioners, programme developers, and facilitators. Adult Education programmes can incorporate practical AI literacy activities that enable adults to explore algorithmic decision-making and examine possible sources and consequences of bias. Facilitators can use case studies, scenario-based exercises, comparative activities, and guided discussions to help learners recognize the relationship between data, algorithms, system design, and AI outcomes. Such approaches can strengthen adults’ critical understanding of AI and encourage more responsible engagement with automated technologies. The study will be significant to adult learners, Adult Education practitioners, employers, technology-training organisations, and programme developers by providing insight into the contribution of Artificial Intelligence Education to adults’ understanding of algorithmic bias. It is recommended that relevant Adult Education programmes incorporate structured learning opportunities covering algorithmic bias, data quality, automated decision-making, fairness, critical evaluation, and human oversight. The study concludes that Artificial Intelligence Education can improve adults’ understanding of algorithmic bias and support more informed and critical engagement with AI-supported technologies.

Keywords: Artificial Intelligence Education, algorithmic bias, Adult Education, adult learners, AI literacy, artificial intelligence, automated decision-making, algorithmic fairness, data quality, AI systems, machine learning, biased outcomes, human oversight, critical evaluation, digital competence