Digital and AI-based Learning Environments for Data Literacy Development (2017–2026): A Bibliometric–Systematic Review
Keywords:
AI in education; digital learning environment; data literacy; bibliometric analysis; systematic reviewAbstract
Artificial intelligence has transformed the world of educational technology, especially the way data-driven digital learning environments are designed. Data literacy is now considered both a purely statistical ability and a critical-thinking ability in interacting with algorithmic systems. The purpose of this study is to map the development, intellectual structure, and thematic trends of research regarding digital and AI-based learning environments for data literacy development. This study uses a bibliometric and systematic review approach to publications from 2017 to 2026. Data were collected from Scopus using the search terms “AI in education”, “digital learning environment”, and “data literacy”. The analysis was conducted using VOSviewer and Biblioshiny, with support from the PRISMA framework for systematic selection. The analysis includes annual scientific production, author productivity, and geographic distribution as well as mapping of keyword networks and thematic structures. The results of the analysis show a huge increase in recent years. The structure of author productivity and the relatively dispersed geographical distribution suggest that this sector is still in the stage of establishing an epistemic identity. Based on the thematic analysis, artificial intelligence serves as the central theme connecting technology, literacy and education. This leads to an artificial intelligence-driven data literacy paradigm. This research helps to integrate data literacy and artificial intelligence into education, developing a critical pedagogy reflective of data-driven systems.
https://doi.org/10.26803/ijlter.25.5.30
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