Jaward Sesay, Yue Yu, Siwei Dong, Yemin Shi, Guangyao Chen, Borje F. Karlsson (2026). arXiv cs.CL 📄 Full text (arXiv)
Key Findings
Hierarchical multi-agent framework (ProfessorAgent + subordinate agents) enables end-to-end adaptive embodied teaching. TASA algorithm aligns teaching actions with learner profiles. Outperforms baselines on lecture quality, embodiment, assessment, and personalization.
Relevance to AI in Education
This paper contributes directly to understanding how AI systems interact with learners in authentic educational settings. Introduces hierarchical multi-agent architectures for embodied, personalized teaching that adapts lecture content and actions to individual learners.
Related Pages
- personalized-learning — related page
- intelligent-tutoring-systems — related page
- ai-tutor-effectiveness-review — related page
- pedagogical-llm-training — related page
- agentic-education-coding — related page
- multi-agent-instructional-design — related page
Citation
APA: Jaward Sesay, Yue Yu, Siwei Dong, Yemin Shi, Guangyao Chen, Borje F. Karlsson (2026). LecturaAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching. arXiv:2606.16428. arXiv cs.CL.