Artificial intelligence (AI) literacy and AI-related digital competencies are becoming increasingly important in U.S. K–12 education as AI tools expand across instructional, administrative, and professional contexts. In response, teacher professional development (PD) has emerged as a critical area for preparing educators to engage with AI in pedagogically meaningful and ethically responsible ways. This study adopted a hybrid design combining a PRISMA 2020-guided systematic literature review with bibliometric mapping in VOSviewer to examine research on AI literacy and AI-related digital competencies in U.S. K–12 teacher professional development published between 2019 and 2026. Records were retrieved from Google Scholar through Publish or Perish.

The search yielded 451 records, of which 73 duplicates were removed in the review workflow. After title and abstract screening of 378 records, 261 were excluded. Full-text assessment of the remaining 117 records resulted in 107 exclusions and a final corpus of 10 included studies. Complementary bibliometric mapping of the filtered topic-relevant dataset revealed a relatively coherent field organized around three interrelated dimensions: AI literacy and teacher-oriented implementation; AI competence and digital competence as the conceptual core; and curriculum design and competency frameworks as the formalization layer. The most visible thematic patterns emphasized classroom practice, teacher preparation, programmatic initiatives, and framework development.

Methodologically, the literature was dominated by exploratory, descriptive, and self-report-based approaches, including surveys, case studies, mixed-methods designs, and validation-oriented studies. A major finding was the absence of strong evidence for objective biometric or physiological assessment. Terms related to eye-tracking, electroencephalography (EEG), heart rate variability, and wearable sensing were not prominent in the bibliometric structure, suggesting that this dimension remains underdeveloped. Overall, the field is expanding but remains conceptually evolving and methodologically constrained, highlighting the need for clearer definitions and more rigorous multidimensional approaches to assessing teacher learning and AI-related competency development.