<![CDATA[The integration of artificial intelligence (AI) into foreign language teaching has revealed significant disparities in global innovation and accessibility, necessitating systematic analysis. This systematic literature review analyzed 88 studies (2019–2024) from Scopus, Web of Science, and ProQuest. Geographically, AI development is concentrated in Asia (particularly China and West Asia), shifting from the previous U.S. dominance. Text- and audio-based tools dominate pedagogical practice, focusing overwhelmingly on productive skills (speaking and writing) and English-language instruction, marginalizing linguistic diversity. Stakeholders reflect dual perceptions: teachers acknowledge administrative efficiency but cite digital literacy gaps and content accuracy concerns; students report reduced anxiety yet criticize AI’s inability to grasp socio-cultural nuances and highlight dependency risks. Pedagogically, AI aligns with social constructivism (adaptive scaffolding) and Self-Determination Theory (motivation gains), although limitations in human interaction depth persist. Three multidimensional challenges emerge: (1) inter-country research-policy disparities, (2) pedagogical risks (dehumanization and over-reliance), and (3) infrastructure access asymmetry. This study contributes to the global landscape mapping of AI trends, validates pedagogical synergies, and offers evidence-based frameworks for policymakers (equitable research), educators (blended learning), and developers (context-responsive multilingual tools). Strategic implications urge developing regions to strengthen inclusive frameworks through international collaboration to prevent epistemic inequalities.]]>
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