Evaluating the Impact of AI-Powered Audiovisual Personalization on Learner Emotion, Focus, and Learning Outcomes
Project Overview
The document explores the Whisper project, an innovative application of generative AI designed to improve focus and emotional regulation for independent learners in distracting environments. By leveraging large language models (LLMs), Whisper creates personalized multisensory study environments, enabling users to customize visual and auditory elements to suit their learning preferences. This approach specifically addresses the challenges faced by neurodivergent individuals in managing attention and emotions, highlighting the importance of integrated tools that facilitate emotional regulation through tailored sensory experiences. The evaluation of the system emphasizes key factors such as cognitive load, user engagement, and overall experience, demonstrating promising outcomes for enhancing learning efficacy. Through its focus on personalization and support for diverse learning needs, Whisper exemplifies the potential of generative AI to transform educational practices and foster a more inclusive learning environment.
Key Applications
Whisper - AI-powered multisensory study environment
Context: Self-directed learners and professionals in distracting or unstructured environments, including higher education students and freelance workers.
Implementation: The system allows users to select or generate customized visual themes and auditory elements to create personalized study environments.
Outcomes: Improved focus and emotional stability, enhanced learning efficiency, and reduced time spent on setting up study aids.
Challenges: Potential issues with user interface design and integration of functionalities.
Implementation Barriers
Technical Barrier
Integration of various AI functionalities into a cohesive user-friendly interface.
Proposed Solutions: Iterative prototyping and user feedback integration to refine design and functionality.
Ethical Barrier
Concerns regarding user privacy and data management in AI systems.
Proposed Solutions: Transparent and consent-based data handling policies, ensuring users maintain control over their data.
Project Team
George Xi Wang
Researcher
Jingying Deng
Researcher
Safinah Ali
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: George Xi Wang, Jingying Deng, Safinah Ali
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai