AI in EU Education Hub
< All Topics
Print

The Future of Multimodal AI for Education

UpdatedApril 28, 2025
ByIuliia Gorshkova

Artificial intelligence has already transformed educational environments across Europe, but the emergence of multimodal AI—systems capable of processing and interpreting diverse data types such as text, audio, images, and video—marks a new chapter in digital learning. As these technologies mature, the landscape of teaching and learning is poised for profound and nuanced changes. With a focus on near-term capabilities, this article explores the evolving potential of multimodal AI, highlighting three forecasted use cases that are particularly relevant for educators, and referencing advances from leading EU Horizon projects.

Understanding Multimodal AI: Beyond Text

Traditional AI applications in education have largely revolved around the analysis of textual data. However, multimodal AI brings together multiple streams of information—audio, images, video, and even sensor data—to create a richer context for understanding and interaction. This integration allows AI systems to interpret not just what is written, but also what is said, shown, or even implied through non-verbal cues.

The shift from unimodal to multimodal AI is not simply an incremental improvement; it represents a fundamental reimagining of how machines perceive and participate in human learning processes.

Current advances in neural architectures, such as transformer-based models, have enabled AI systems to process and relate information across modalities with increasing sophistication. These capabilities are being actively explored within the framework of European research initiatives, including the EU Horizon Europe programme, which funds projects at the intersection of AI, education, and societal impact (CORDIS, 2024).

Near-Term Capabilities: Audio, Image, and Video Inputs

Within the next two to three years, educators can expect multimodal AI systems to demonstrate significant improvements in the following areas:

1. Real-Time Audio Analysis and Feedback

Multimodal AI will soon enable real-time processing of classroom audio, analyzing nuances in spoken language, intonation, and even emotional cues. This technology can provide immediate, personalized feedback to learners, supporting language acquisition, public speaking, and collaborative discussions. For example, the EU-funded EMBEDDIA project (EMBEDDIA, 2023) has developed models that understand and generate human-like responses across languages and modalities, paving the way for intelligent virtual tutors that interact naturally with students.

Use case forecast: In a language class, students practice pronunciation while an AI assistant listens, identifies subtle errors, and offers targeted corrections, all in real time. The system also tracks student engagement and sentiment, alerting the educator if a learner seems frustrated or disengaged.

2. Image and Video-Based Assessment

Automated assessment of visual work—drawings, diagrams, experiments—has long been a challenge. With advanced computer vision capabilities, multimodal AI can now interpret complex visual inputs submitted by students. The iRead project (iRead, 2022), for example, has explored the use of AI for adaptive learning in literacy, including interpreting handwritten and illustrated responses. This opens new possibilities for disciplines where visual expression is critical, such as art, engineering, and the sciences.

Use case forecast: In an online biology course, students upload images of their hand-drawn cell diagrams. The AI system evaluates accuracy, offers feedback on labeling and structure, and highlights aspects that may require revision—all without human intervention, but always under the teacher’s guidance.

3. Multimodal Content Adaptation for Accessibility

Personalized learning is only effective if content is accessible to all students, including those with diverse needs. Multimodal AI can automatically generate alternative representations of educational materials—transcribing spoken lectures into text, describing images for visually impaired learners, or converting text into interactive graphics. The EASIER project (EASIER, 2023) exemplifies this approach by developing AI-driven tools for converting complex texts into easy-to-read formats, utilizing both linguistic and visual analysis.

Use case forecast: A student with hearing impairment attends a history class where the teacher presents spoken commentary alongside historical images and video clips. The AI system provides synchronized real-time captions, descriptive audio, and visual summaries, ensuring complete access to the learning experience.

Ethical, Legal, and Pedagogical Considerations

As multimodal AI makes its way into classrooms and universities, it brings with it important questions of ethics, privacy, and compliance with European laws. The EU’s AI Act and the General Data Protection Regulation (GDPR) set clear standards for transparency, accountability, and the protection of learners’ data. Educators are called to be vigilant in selecting AI solutions that align with these regulations and to foster a culture of digital literacy among students.

Effective integration of multimodal AI is not only a matter of technical feasibility, but also of pedagogical intentionality and respect for the rights and dignity of every learner.

Professional development is essential. Teachers must be empowered to understand how AI systems make decisions and to interpret their outputs critically. EU Horizon projects such as AI4T (AI4T, 2024) are already developing training resources and policy recommendations to support educators in this transition.

Looking Ahead: Collaboration and Community

One remarkable aspect of the European approach to AI in education is the emphasis on collaboration—among researchers, teachers, policymakers, and learners themselves. Multimodal AI, with its capacity to bridge sensory and cognitive divides, invites a renewed sense of partnership in the educational process.

Teachers are not merely users of these technologies; they are co-creators, shaping how AI is developed and implemented in real-world classrooms. EU Horizon’s AI4Education initiative (AI4Education, 2024) highlights the importance of participatory design, ensuring that solutions are responsive to the unique needs and values of European educators and learners.

Practical Steps for Educators

For those seeking to enhance their knowledge and skills in multimodal AI, several practical actions can be taken:

  • Engage in professional development: Participate in workshops, online courses, and communities of practice focused on AI and digital pedagogy.
  • Stay informed about legal frameworks: Review guidance from the European Commission and national authorities on the ethical use of AI in education.
  • Experiment and reflect: Pilot multimodal AI tools in your own teaching, collect feedback from students, and share insights with colleagues.
  • Contribute to research: Join EU Horizon project pilots or open calls to help shape the next generation of educational AI systems.

Resources and Further Reading

To support your journey, here are selected resources and ongoing projects at the forefront of multimodal AI in education:

The future of multimodal AI in education is being written by those who use it, question it, and dream beyond its current capabilities. With curiosity, responsibility, and a shared commitment to inclusive learning, European educators are at the heart of this transformation.

Table of Contents
Go to Top