Vocabulary Without Infrastructure: A Bibliometric Analysis of Explainability and Equity in AI-Based Personalised Learning

Artificial intelligence has become a central paradigm in personalised learning. However, it remains unclear whether the two principles that responsible AI treats as foundational, explainability and equity, are integrated into the field’s intellectual architecture or are only loosely associated with it. This question is addressed through a bibliometric analysis of 1371 publications indexed in Scopus and Web of Science between 2015 and 2025. Performance analysis and science-mapping techniques were applied using bibliometrix and VOSviewer, with cluster structure quantified through modularity and inter-cluster density metrics, and uncertainty assessed through 1000-iteration edge-resampling bootstrap. Keyword co-occurrence analysis showed that ethics-equity vocabulary connects to the applied-AI cluster at densities comparable to those of technical vocabulary (0.20 vs 0.21), but the ethics-equity and technical streams interact directly only at half that rate (0.11; bootstrap 95% CIs non-overlapping), engaging primarily through the applied-AI mediator cluster rather than with each other. Co-citation analysis showed that the foundational responsible-AI references in education do not register as cluster-forming nodes. No specialist responsible-AI venue appears in the field’s Bradford core, and Sub-Saharan Africa and Latin America remain almost absent from the country-level collaboration network. To account for this pattern of vocabulary diffusion without infrastructural consolidation, we propose the Structural Integration Model (SIM). The SIM is a three-proposition framework that specifies the conditions under which explainability and equity can become structurally embedded in the field’s foundation rather than only thematically present within it.

@article{Echchoayeby2026,
  title     = {Vocabulary Without Infrastructure: A Bibliometric Analysis of Explainability and Equity in AI-Based Personalised Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {8},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Ahmed Echchoayeby and Wafae Abbaoui and Reida Ilal and Nassim Kharmoum and Soumia Ziti},
  doi       = {10.14569/IJACSA.2026.0170881},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170881}
}