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Impact of Adult Education on Adults’ Knowledge of Algorithmic Bias

Abstract

Adult Education provides adults with relevant knowledge and skills for understanding technological developments that increasingly influence information, services, and decision-making. Algorithmic bias refers to systematic or unfair patterns in the outputs of algorithms or automated systems that can arise from biased data, design choices, assumptions, or other features of the systems through which decisions are produced. The purpose of this study is to examine the impact of Adult Education on adults’ knowledge of algorithmic bias. The study will focus on how educational experiences can improve adults’ understanding of the meaning, causes, manifestations, and implications of bias in algorithmic systems. Adult Education can introduce learners to basic concepts relating to algorithms, artificial intelligence, data, automated systems, and digital decision-making. Through discussions, demonstrations, examples, and practical learning activities, adults can develop a clearer understanding of how algorithms process information and produce outputs. Such learning experiences may also introduce learners to the possibility that algorithmic systems can reflect limitations or biases contained in their data, design, or application. This knowledge can provide adults with a foundation for recognizing algorithmic bias in different digital environments. Adults’ knowledge of algorithmic bias involves their understanding of what algorithmic bias means, how it can occur, and how biased outputs may affect individuals or groups. Adequate knowledge can help adults approach automated systems more critically and recognize that technological outputs should not automatically be regarded as neutral or error-free. Adult Education may strengthen this knowledge by providing accessible explanations, practical examples, and opportunities for learners to examine how data and algorithmic processes can influence automated outcomes. 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 knowledge assessment designed to measure adults’ knowledge 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 Adult Education on adults’ knowledge of algorithmic bias. The study is expected to establish that Adult Education has a positive impact on adults’ knowledge of algorithmic bias. Adults who participate in relevant educational activities may demonstrate improved understanding of the meaning, possible causes, and manifestations of algorithmic bias. The study may further establish that practical examples and discussions of automated decision-making can improve adults’ ability to recognize that algorithmic systems may produce outputs that require careful examination and interpretation. The findings are expected to have implications for Adult Education practitioners, digital literacy facilitators, community learning centres, and organisations involved in technology education. Incorporating basic lessons on algorithms and algorithmic bias into adult learning programmes may help learners develop knowledge needed to engage more critically with technology-driven information and services. Practical learning activities that examine simplified examples of algorithmic decisions may also make the concept more accessible and relevant to adults. The study will be significant to adult learners, Adult Education practitioners, digital literacy educators, community organisations, and other stakeholders interested in responsible engagement with emerging technologies. It is recommended that Adult Education programmes should incorporate appropriate learning activities that introduce adults to algorithmic bias, its possible sources, and its implications for automated decision-making. The study concludes that Adult Education can serve as an important avenue for improving adults’ knowledge of algorithmic bias and strengthening their capacity to engage thoughtfully with algorithm-based technologies.

Keywords: Adult Education, algorithmic bias, artificial intelligence, algorithms, digital literacy, technology education, algorithm awareness, automated systems, automated decision-making, data bias, digital awareness, technology knowledge, information evaluation, responsible technology use, emerging technologies