This comprehensive survey explores how chain-of-thought reasoning is being extended into multimodal contexts, enabling AI systems to process and reason across different types of data.

• Introduces innovative reasoning paradigms for diverse modalities (images, video, speech, 3D data)
• Examines integration with multimodal large language models (MLLMs)
• Addresses unique challenges in cross-modal reasoning
• Maps the landscape of methodologies achieving human-like reasoning

Educational Impact: This research provides crucial frameworks for developing AI-powered educational tools that can process multiple input types (text, images, video) while explaining their reasoning—potentially revolutionizing personalized learning and assessment tools.

Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey