.svg){kind=small}
What are you searching for?
Imagine you’re a student learning about photosynthesis. Instead of just reading text in a book, you watch a video explaining the process, interact with a 3D animation of a plant, and ask questions using voice commands — all in one cohesive experience. This is the power of multimodal AI: a technology that combines text, images, audio, video, and more to create richer and more interactive learning experiences.
The numbers tell the story: the global multimodal AI market was valued at USD 1.74 billion in 2024 and is expected to grow at an impressive CAGR of 36.8% from 2025 to 2030. This rapid growth highlights the technology's potential — not just in improving how we learn but also in making data smarter and more actionable.
In this article, we’ll explore Multimodal AI, how it works in EdTech, and why it’s shaping the future of education. Ready to dive in? Let’s go!
What is multimodal AI, and how does it work?
Multimodal AI is a type of artificial intelligence that can understand and process different types of data simultaneously. Unlike traditional AI, which may focus on just one form of input — like text or voice — multimodal AI can combine multiple inputs such as text, images, audio, and video to create a deeper, more accurate understanding of a situation or task.
For example, in an educational setting, multimodal AI can take a student's spoken question, analyze the text from their written responses, and even observe their facial expressions or body language through a camera.
This allows the AI to understand not just what the student is asking but also how they’re engaging with the material. It can then use this combined information to offer more personalized feedback or suggest resources to help the student better understand the topic.
Here’s how it works:
Data input: The system collects various types of data — text from books, images from diagrams, voice from spoken questions, and video from virtual lessons.
Data integration: It then combines all these inputs to create a more comprehensive view of the situation. For instance, if a student says something verbally and then looks confused while reading a text, the AI can combine both voice input and visual cues to assess their level of understanding.
Actionable insights: After analyzing this information, the AI can make decisions, such as recommending additional resources, adjusting the difficulty of tasks, or even helping the teacher track student progress.
Thank you for Subscription!
Personalized learning with Multimodal AI
Personalized learning has always been a goal in education aiming to ensure every student gets the tailored support and resources needed to succeed. With the rise of multimodal AI, this vision is becoming a reality. AI systems can adapt to individual learning styles and needs in real time, creating tailored educational experiences for every student.
How Multimodal AI makes learning personal
This technology combines multiple data sources to create a comprehensive understanding of each student's learning process. Analyzing how a student interacts with content (through text, video, voice, etc.) can adapt the learning experience to suit their preferences and needs.
Adapting to learning styles
Different students have different learning preferences. Some may be visual learners, others learn better by listening, and some may need a more interactive or kinesthetic approach.
Multimodal AI helps by analyzing how students engage with content and offering personalized learning resources based on their learning style. For example, if a student engages more effectively with video content and understands concepts better through visual explanations, the system might suggest more video-based lessons.
On the other hand, if a student responds well to text-based content, the system could prioritize written materials like e-books or articles.
Example: A student struggling with a math concept might get more interactive visual content, like animations or simulations, while a student who learns best by reading could receive in-depth articles or practice problems to reinforce the lesson.
Real-time feedback and adjustments
One of the powerful features of Multimodal AI is its ability to offer real-time feedback. As students engage with lessons, the AI constantly tracks their responses and behaviors, adjusting the material to suit their progress.
If a student struggles with a particular concept, the AI can identify the difficulty and offer additional explanations, practice exercises, or examples tailored to the student's level of understanding. Conversely, if a student demonstrates mastery of the material, the AI can adjust to present more challenging tasks.
Example: Let’s say a student uses a language learning app that includes listening, speaking, and reading. If the system notices that the student excels in listening but struggles with speaking, it might focus on voice recognition exercises, giving extra attention to pronunciation and fluency.
Identifying learning gaps
This technology can also help identify gaps in a student’s knowledge byy analyzing all available data, including quiz results, voice responses, video interactions, and even facial expressions. By integrating these inputs, the AI system can determine areas where a student may have difficulty.
This allows for immediate intervention, ensuring that no student falls behind. The AI can flag areas of weakness and suggest targeted resources, such as tutorials, videos, or even personalized practice exercises, to fill those gaps.
Example: In an online science course, if a student consistently struggles to answer questions about a specific topic, like the process of photosynthesis, the AI might suggest a video explaining the concept in simpler terms, followed by a quiz to test the student’s understanding.
Emotion recognition and engagement
Multimodal AI goes beyond just academic performance; it can also track student engagement and motivation. AI can use facial recognition and sentiment analysis to assess emotions during learning activities.
If it detects signs of frustration or boredom (e.g., frowning or lack of interaction), it can adjust the task's difficulty or offer a break. This emotional insight helps create a more supportive and encouraging learning environment, improving student well-being and retention.
Example: Imagine a virtual tutor system that notices a student’s frustration during a math lesson. The AI might offer a more interactive method of explaining the concept or even ask if the student wants to switch to a different type of activity to reset their focus.
Supporting diverse learners
Multimodal AI is also instrumental in supporting students with diverse learning needs, including those with disabilities. For example, students with hearing impairments can benefit from AI that provides real-time captions for video content or converts spoken words into text.
Likewise, students with visual impairments can benefit from voice-driven AI systems that read text aloud or guide them through content in a way that’s accessible to them. This makes learning more inclusive and equitable for everyone.
Example: In a history class, a student who struggles with reading can use an AI-powered app that reads text aloud while also showing relevant images or videos, which helps them understand the context without relying on reading alone.
Gamification and interactive learning
Do you want to engage students as much as you can? Multimodal AI boosts gamification by combining audio, visual, and interactive elements to make learning more engaging. It turns lessons into game-like challenges where students earn rewards, and the AI adjusts difficulty based on their progress. This keeps the learning process fun, motivating, and tailored to each student.
Example: A coding platform that uses Multimodal AI could provide immediate feedback on students’ coding attempts while offering interactive tutorials and challenges. The AI could track their progress, adjust the difficulty of tasks, and even reward them with virtual badges or points for completing certain challenges, making learning feel more like a game.
More from our blog
AI due diligence: Genuisee best practices and recommendations
Learn how to evaluate AI solutions for accuracy, compliance, scalability, and potential risks.
Read moreHow EdTech companies use multimodal AI
Several EdTech platforms have already started to implement Multimodal AI to enhance personalized learning. Here are a few examples for you:
Duolingo: This language-learning app uses Multimodal AI to combine voice recognition, text, and gamified elements to tailor lessons to individual learners. It adjusts difficulty based on performance and provides instant feedback, helping users learn at their own pace.
Knewton: Knewton uses AI to personalize learning in real time for students in subjects like math and science. It gathers data on how each student interacts with the material and adjusts content accordingly, offering different learning paths to ensure each student gets the support they need.
DreamBox: An online math program for K-8 students, DreamBox uses Multimodal AI to analyze students’ responses and learning behavior. Based on this data, it personalizes lessons, adapts to the student’s skill level, and provides instant feedback, making sure each student is challenged appropriately.
Geniusee: improve your EdTech product with AI
At Geniusee, we specialize in transforming EdTech products by integrating AI to create more personalized, engaging, and effective learning experiences. One of our standout projects, SciQuiry, is a great example of how we use AI-powered solutions to enhance education for middle and high school students.
The AI solutions we integrated focused on adapting content to each student's needs, providing a personalized learning path that evolved as the student progressed. By analyzing user behavior and adjusting the system based on this data, we ensured the platform's functionality and flow were optimized for maximum educational effectiveness.
Conclusion
Education is entering a new era where technology does more than just support learning. It fundamentally transforms it. Multimodal AI brings together multiple input forms, from videos and animations to real-time feedback, creating a richer and more tailored experience for every student.
This isn’t just about making lessons more interactive; it’s about understanding each student’s unique needs and adapting to them. Multimodal AI offers tools that make learning more inclusive, accessible, and effective for everyone, whether it’s through gamification, personalized resources, or support for diverse learners.
