Tracing the Topic Evolution of AI Literacy Literature: From Knowledge Enlightenment to Knowledge Symbiosis

Artificial intelligence (AI) is profoundly reshaping production modes, lifestyles, and organizational paradigms. Enhancing individual adaptability and creativity in this intelligent era has emerged as an imperative research agenda within public literacy studies. This study investigates the evolution of themes in AI literacy research, with a focus on the evolutionary mechanisms through which the domain’s knowledge architecture transitions from techno-cognitive accumulation to synergistic innovation ecosystems. Employing text-mining methodology, we analyze academic literature spanning 2015–2024. Following standard processing steps—tokenization, stopword removal, and lemmatization—we apply Latent Dirichlet Allocation (LDA) topic modeling to extract and analyze latent topics, tracing paradigm shifts in knowledge production during socio-technical processes of innovation. Evolutionary pathways constructed through topic similarity metrics reveal a progression in AI literacy research from knowledge enlightenment to symbiotic knowledge development. LDA2vec hybrid modeling facilitates deep semantic exploration of stage-specific topics, while subsequent topic intensity evolution and semantic network analyses highlight breakthrough points in AI literacy-driven knowledge innovation. This dual-axis analytical framework—integrating topic evolution with intensity dynamics—provides empirical evidence for predicting disciplinary trajectories while establishing novel theoretical paradigms and methodological approaches to AI literacy. Aligned with knowledge organization principles, these frameworks emphasize systematic structuring, cross-domain classification, and semantic interrelations of AI literacy concepts to enhance knowledge accessibility and interoperability.