Abstract

The convergence of artificial intelligence (AI) and lifelong learning has attracted growing educational, computational, and policy attention, yet its intellectual structure has not been mapped systematically. This study reports a scientometric analysis of the AI-lifelong learning literature indexed in Scopus and the Web of Science Core Collection between 2014 and 2024. Records retrieved from both databases (N = 347) were merged and duplicated in ScientoPy, yielding 259 unique documents. Research momentum was quantified with three time-normalized indicators, average growth rate (AGR), average documents per year (ADY), and percentage of documents published in the last years (PDLY), across six analytical dimensions. Output grew from one document in 2014 to 116 in 2024; across the five principal document types (n = 220), 70.5% of records appeared in 2023-2024. Conference papers (41.3%) and journal articles (38.2%) predominated, with articles expanding fastest (AGR = 21.5; PDLY = 71.7%). Education & Educational Research recorded the highest normalized momentum among subject categories (AGR = 7.0; PDLY = 89.5%). Lifelong learning (n = 57) and artificial intelligence (n = 53) anchored the keyword field, while ChatGPT and Higher Education emerged entirely within the final two years. The United States led in volume (n = 57), whereas India (n = 29; PDLY = 89.7%) showed the strongest recent acceleration. Triangulating these indicators suggests, as an interpretive inference rather than a network-based measurement, that the corpus comprises three largely separate epistemic communities sharing a label but not a research program, framing an agenda for integrative, ethically grounded scholarship.

This work is licensed under a Creative Commons Attribution 4.0 License.