More Than Coding: A Mixed-Methods Systematic Review of the Multidimensional Landscape of Programming and AI in Primary Education

Description

This dataset contains the structured data extracted for a systematic literature review examining the multidimensional impact of programming and artificial intelligence (AI) education in primary school (ages 6–12). The dataset includes detailed information on included studies, screening and eligibility criteria, and analytical coding across four dimensions: cognitive, social, socioemotional, and technical outcomes. Studies were identified through systematic searches in multiple academic databases and selected according to predefined inclusion and exclusion criteria. Data were extracted and synthesised using a mixed-methods approach to identify pedagogical practices, reported impacts, methodological patterns, and persistent research gaps. This dataset supports transparency and replicability and may be useful for researchers, educators, and policymakers interested in digital competence development, AI literacy, and curriculum integration in primary education.

Steps to reproduce

The review followed a mixed-methods systematic approach consistent with PRISMA 2020 reporting guidelines. Define the review scope: programming, artificial intelligence (AI), computational thinking, and educational robotics explicitly linked to programming or AI in primary education (ages approximately 6–12). Conduct database searches in Scopus, IEEE Xplore, ScienceDirect, ERIC, RCAAP, and BDTD using predefined keyword combinations related to programming, AI, computational thinking, and primary education. Limit results to publications between 2018 and 2023 in English or Portuguese. Remove duplicates and perform title/abstract screening. Conduct full-text eligibility assessment according to the inclusion and exclusion criteria provided in the dataset. Extract data into a structured spreadsheet including study characteristics, intervention type, and reported outcomes. Classify reported outcomes across the analytical dimensions (cognitive, technical, pedagogical, socio-emotional, and ethical/AI literacy). Conduct a mixed-methods synthesis combining descriptive quantitative mapping and narrative thematic analysis. All structured data supporting the synthesis are available in this dataset.

Institutions

• Iscte – Instituto Universitário de Lisboa

  Lisbon, Lisbon

Categories

Licence