Hopes and dilemmas: artificial Intelligence, AI literacy and data literacy
Katalin Varga, Tibor Koltay
This paper provides a general background and some characteristics of AI and data literacy, which is a life skill for everyday problem solving. It enables successful and sustainable action that is based on evidence and that adequately considers uncertainty and change in our living environment (Schüller, 2022).
A data literacy competence frameworks may contain the need for data and its usefulness. They encompass knowledge, skills, and values for the conscious and ethically sound handling of data, and systematically covering the entire process of knowledge and decision-making with data. It also should be accessible to everyone and taught lifelong in all areas of education by relying on transdisciplinary and interdisciplinary skills (Ridsdale et al. 2015). Obviously, it involves more than computational skills, but requires personal data management and data-driven decision-making, as well. All also requires cultivating more critical view of data as we know that data alone cannot provide a complete picture, data literacy is a set of foundational competencies, so we present a generalised conceptual model of core data literacy competencies. AI literacy depends on effectively addressing the complexities of data equity mandates, by appreciating the different viewpoints that different stakeholders have about data (Stonier et al., 2023). Artificial intelligence (AI) is likely to impact significantly data-related developments (Cox et al., 2018). However, AI literacy is easier to define as it is a set of competencies for evaluating, communicating, and collaborating with AI technologies. This means that digital literacy, information literacy and data literacy are prerequisites of AI literacy. In such environments, data needs to be interpreted from different perspectives, but by identifying and describing the ethical issues. There is also a need to understand the characteristics that characterise human, animal, and machine intelligence. To this should be added a self-evident understanding of how to use computers and how programmes can inform and help make sense of AI (Long & Magerko, 2020).
This paper provides a general background and some characteristics of AI and data literacy, which is a life skill for everyday problem solving. It enables successful and sustainable action that is based on evidence and that adequately considers uncertainty and change in our living environment (Schüller, 2022).
A data literacy competence frameworks may contain the need for data and its usefulness. They encompass knowledge, skills, and values for the conscious and ethically sound handling of data, and systematically covering the entire process of knowledge and decision-making with data. It also should be accessible to everyone and taught lifelong in all areas of education by relying on transdisciplinary and interdisciplinary skills (Ridsdale et al. 2015). Obviously, it involves more than computational skills, but requires personal data management and data-driven decision-making, as well. All also requires cultivating more critical view of data as we know that data alone cannot provide a complete picture, data literacy is a set of foundational competencies, so we present a generalised conceptual model of core data literacy competencies. AI literacy depends on effectively addressing the complexities of data equity mandates, by appreciating the different viewpoints that different stakeholders have about data (Stonier et al., 2023). Artificial intelligence (AI) is likely to impact significantly data-related developments (Cox et al., 2018). However, AI literacy is easier to define as it is a set of competencies for evaluating, communicating, and collaborating with AI technologies. This means that digital literacy, information literacy and data literacy are prerequisites of AI literacy. In such environments, data needs to be interpreted from different perspectives, but by identifying and describing the ethical issues. There is also a need to understand the characteristics that characterise human, animal, and machine intelligence. To this should be added a self-evident understanding of how to use computers and how programmes can inform and help make sense of AI (Long & Magerko, 2020).
