From Prompting to Multi-Agent AI: Controlled Generation of CEFR-Specific Language Learning Content

Generative artificial intelligence is increasingly being used to support content creation in digital language-learning environments; however, generating educational materials that consistently reflect different proficiency levels remains a challenge. This study investigates an orchestrated multi-agent AI architecture for generating language-learning content targeting different CEFR proficiency levels. The proposed architecture employs an orchestrator that routes content-generation requests to independently configured, level-specialized agents operating under predefined lexical, structural, and instructional requirements, without inter-agent collaboration or deliberation. To distinguish the effects of explicit level-specific constraints from those associated with agent-based orchestration, a controlled experiment compared three generation conditions using the same underlying large language model: conventional prompt-based (PB) generation, constrained prompting (CP), and multi-agent (MA) generation. CP incorporated the same core level-specific requirements and vocabulary resources as MA without employing agent-based orchestration. The three conditions were evaluated using 100 matched tasks across five intended CEFR levels (A1–C1), resulting in 300 generated stories. The outputs were assessed in terms of text length, lexical diversity, syntactic characteristics, and adherence to predefined generation constraints. Within MA, all six continuous linguistic measures showed significant differences across intended proficiency levels, although adjacent-level differentiation varied across linguistic dimensions. Across the three conditions, PB differed from CP and MA on several linguistic measures, whereas CP and MA exhibited greater similarity, with statistically significant differences in 12 of 30 planned comparisons. Both CP and MA achieved 100% adherence to the evaluated story-length, target-vocabulary-count, and comprehension-question-count requirements in the analyzed outputs. Overall, the findings suggest that explicit constraints and vocabulary resources contribute substantially to controlled proficiency-targeted generation, while the orchestrated multi-agent architecture provides modular organization and is associated with selected level-dependent linguistic differences rather than consistent superiority over CP. The study therefore focuses on controlled linguistic differentiation and operational constraint adherence, rather than independent validation of CEFR proficiency levels.