Mapping Artificial Intelligence in Electric Two-Wheelers: Paradigm Evolution, Technology–Application Specialization, and Emerging Frontiers (2015–2025)

Artificial intelligence (AI) is increasingly embedded in electric mobility, but evidence on electric two-wheelers (E2Ws) remains dispersed across engineering, safety, shared mobility, and user-oriented research. This bibliometric science-mapping study examines how AI paradigms have evolved and specialized across E2W problem domains from 2015 to 2025. A reproducible Scopus search retrieved 759 records; focality screening retained 568 Direct publications, 118 Adjacent records for sensitivity analysis, and 73 Exclusions. The analysis combined performance mapping, co-word/co-citation/coupling networks, non-exclusive AI-family and application-domain coding, specialization lift with enrichment testing, and robustness checks. Annual output rose from 8 papers in 2015 to 166 in 2025 (CAGR = 35.4%), with 52.5% of the Direct corpus published in 2024–2025. Computational-intelligence prevalence fell from 48.2% to 20.7%, while classical machine learning and deep learning each reached about 41% in 2023–2025. The strongest specialization signals were generative AI–design/HMI (lift = 4.70), reinforcement learning–routing/logistics (2.63), and computer vision–safety/perception (2.24). AI-family coding was highly reproducible, although several application-domain boundaries were less stable. Overall, AI–E2W research follows problem-dependent technological trajectories rather than a single linear maturity path.