Page 647 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

teaching methods often fail to accommodate their diverse cognitive styles, resulting in
disengagement, stress, higher dropout rates, and reduced academic performance.

This use case proposes a real-time adaptive AI-powered learning assistant that supports
neurodivergent learners by adjusting instructional content and delivery based on each student’s 4.7: Education
emotional and cognitive state.

The system uses multimodal AI combining eye tracking, facial expression recognition, behavioral
analysis, and sentiment detection to continuously assess engagement and cognitive load.
Based on this input, the AI dynamically adjusts the pacing, format, and complexity of learning
content using NLP-driven content generation and Retrieval-Augmented Generation (RAG). The
system also provides accessibility support through features like text-to-speech, dyslexia-friendly
fonts, and guided relaxation breaks when stress or fatigue is detected.

Unlike existing AI-based education tools that focus solely on academic performance, this system
prioritizes emotional awareness and mental well-being. It not only customizes the learning
experience but also helps students manage cognitive stress, thereby improving retention,
motivation, and overall educational equity.

Expert validation is a core part of the approach. Educators and psychologists will evaluate
AI-generated outputs during pilot phases, ensuring reliable detection of frustration,
disengagement, and overload. Furthermore, the system supports personalization over time
by learning each student’s behavioral and emotional baseline.

Privacy and ethical design are embedded into the system. Emotion and biometric data are
collected only with informed consent, processed locally where possible, and secured using
anonymization techniques and privacy-by-design standards.

Partners

At present, this project does not have formal institutional or organizational partnerships.
However, the future implementation roadmap includes outreach to:

• Special education schools and therapy centers for real-world pilot testing.
• Universities and AI research groups for model development and validation.
• Mental health professionals and psychologists to help co-design stress detection
mechanisms and ensure ethical oversight.

The project is designed with openness in mind, aiming for future collaboration with stakeholders
in both education and mental health sectors, as well as open-source AI communities.

2�2 Benefits of use case

This use case directly delivers inclusive, adaptive learning experiences tailored to the unique
needs of neurodivergent students. By leveraging AI to adjust content in real-time based on
emotional and cognitive feedback, the system ensures that each learner receives an education
aligned with their learning style and mental well-being.

It addresses a population often left behind by standardized education systems. Neurodivergent
students frequently face academic and social marginalization due to a lack of individualized

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