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We argue in this paper that there is currently no adequate theoretical framework or model that spans the twelve odd year trajectory from non‐reader to proficient reader, nor addresses fine‐grain skill acquisition, mastery and integration. The target construct itself, reading proficiency, as often operationalized as an endpoint of formal secondary schooling, is defined and measured in imprecise, fragmented terms. Consequently, schools (and empirical research) fall back on heuristics like the Simple View of Reading, or a few stages (learn to read, read to learn, read to do) to describe reading development. Those models, however, are too general to guide instructional decisions for adaptive learning systems. Progress in engineering an adequate learning system has been inhibited by a mismatch with curriculum standards and school organization that impose good‐faith but not fully optimized developmental targets on the educational system. We propose the development of a learning and assessment framework to scaffold reading proficiency development while accounting for the diverse learning trajectories of groups or individuals across development. We then identify some key problems, challenges and opportunities that AI technologies are poised to help us address in conceiving individualized, adaptive learning systems for reading proficiency across the developmental spectrum. We close with a selective review of examples of AI‐enhanced research or products. Practitioner notesWhat is already known about this topic Reading proficiency is a vital component of education systems design.Theoretical and empirical studies across multiple disciplines have been published, but much of this research is framed in fragmented theories that do not seamlessly span the trajectory of life‐long reading development.Current assessments are time‐consuming, coarse‐grained and fail to provide a roadmap for educators and designers of adaptive learning environments.There is a significant lack of knowledge of potentially non‐linear growth in reading skills within or between years and how to design adaptive instruction for diverse subpopulations.What this paper adds We describe the initial steps towards a literacy learning and assessment framework that spans the trajectory from non‐reader to proficient reader.We provide a landscape of exemplars of artificial intelligence and computational linguistics that reflect the possibilities of a more comprehensive, cohesive literacy development system.We reflect upon key problems, challenges and opportunities that AI technologies can help address in conceiving individualized, adaptive learning systems for reading proficiency across the developmental spectrum.Implications for practice and/or policy AI and computational linguistics can help fill in the gaps in understanding and enacting a longitudinal vision of reading development.Educators would ideally know what to expect of their students at particular points of development, identify deviations and have additional tools to intervene effectively to maximize progress.There is a need to develop adaptive instruction that spans the development of proficiency from preschool to college/career levels and adapts to address common barriers among diverse subpopulations. [ABSTRACT FROM AUTHOR]

Data literacy has been suggested as an important competence that individuals need to succeed in a data‐intensive society. However, there is no common understanding as to what data literacy entails and how it could be developed. Instructional emphasis on developing competence of individuals fails to capture learners' relationship to data in everyday life and limits what they can possibly achieve in data‐rich environments. This paper critically reviews conceptualizations of data literacy in the literature with a focus in K‐12 education. The analysis determined four orientations of data literacy: development of competence, inquiry with data, awareness of personal data and civic engagement. I proposed a broader conceptualization of data literacy that integrates conceptions, competencies and contexts. The study offers holistic and context‐oriented framing of data literacy for researchers and educators. Practitioner notesWhat is known about the topic Data literacy is a potential buzzword in the recent literature. There are increasing calls for developing data literacy skills of students and the general public.Data literacy is framed and implemented as a technical competence. Accordingly, curricular interventions and pedagogical practices focus on making use of data and benefiting from available datasets.What this paper adds The above framing of data literacy is too narrow to be useful in everyday life and rarely considers individuals interaction with data outside of schools.This study develops four focus areas in the conceptualization of data literacy and suggests broader framing of the concept as it relates to everyday life.It also suggests context‐oriented approaches to data literacy education that can go beyond classrooms and academic activities.Implications for practice and policy This paper has implication for educators, researchers and policy makers. It allows boarder conceptualizations of data literacy that can be used in curricular interventions. It also provides ways of designing learning environments for the data literacy education and research. [ABSTRACT FROM AUTHOR]

In this study, support for teaching data literacy in social studies is provided through the design of a pedagogical support system informed by participatory design sessions with both pre‐service and in‐service social studies teachers. It provides instruction on teaching and learning data literacy in social studies, examples of standards‐based lesson plans, made‐to‐purpose data visualization tools and minimal manuals that put existing online tools in a social studies context. Based on case studies of eleven practicing teachers, this study provides insight into features of technology resources that social studies teachers find usable and useful for using data visualizations as part of standards‐ and inquiry‐based social studies instruction, teaching critical analysis of data visualizations and helping students create data visualizations with online computing tools. The final result, though, is that few of our participating teachers have yet adopted the provided resources into their own classrooms, which highlights weaknesses of the technology acceptance model for describing teacher adoption. Practitioner notesWhat is already known about this topic Data literacy is an important part of social studies education in the United States.Most teachers do not teach data literacy as a part of social studies.Teachers may adopt technology to help them teach data literacy if they think it is useful and usable.What this paper adds Educational technology can help teachers learn about data literacy in social studies.Social studies teachers want simple tools that fit with their existing curricula, give them new project ideas and help students learn difficult concepts.Making tools useful and usable does not predict adoption; context plays a large role in a social studies teachers' adoption.Implications for practice and/or policy Designing purpose‐built tools for social studies teachers will encourage them to teach data literacy in their classes.Professional learning opportunities for teachers around data literacy should include opportunities for experimentation with tools.Teachers are not likely to use tools if they are not accompanied by lesson and project ideas. [ABSTRACT FROM AUTHOR]