IEEE P7015

New IEEE Standard - Active - Draft.
This standard is established as a comprehensive operational framework for data and artificial intelligence literacy, skills, and readiness. Essential competencies are defined across multiple proficiency levels to help ensure individuals and organizations are empowered in an increasingly data- and AI-driven society. A common set of definitions, language, and understanding of data and AI literacy is provided, allowing for systematic inclusion in educational curricula and training programs. By the framework, policy interventions are enabled, progress is tracked, and outcomes are evaluated through measurable indicators. Through the standardization of these competencies, data and AI literacy is intended to be made a core component of modern education and professional development, facilitating ethical and responsible engagement with these technologies.

To coordinate global data and AI literacy building efforts, this standard establishes an operational framework and associated capabilities for designing policy interventions, tracking their progress, and empirically evaluating their outcomes. The standard includes a common set of definitions, language, and understanding of data and AI literacy, skills, and readiness.
The purpose of the standard is to enable that the respective set of skills and competencies are widely taught as a transdisciplinary competence across all subjects from three perspectives (application-oriented, technical-methodological, and socio-cultural) and can be acquired by all people so that every individual, and our society as a whole, will be enabled to deal with data and AI in a conscious and ethically sound manner. The standard creates a common understanding of data and AI literacy so that it can be systematically included into curricula and educational standards of schools, teacher training, higher education, and further education. Furthermore, the standard serves as a reference for data and AI literacy programs for extracurricular and vocational training, to enable lifelong learning of data and AI literacy. Given that, such a standard can serve as a basis for eventually developing a convergent, coherent data literacy assessment, measurement and impact evaluation framework for the national, regional and global levels that can help to fill the empirical evidence gap on outcomes from data literacy interventions and programs.