AIM HIGH: AI-Powered Multimodal Hybrid Instruction for Growth

AIM HIGH (AI-Powered Multimodal Hybrid Instruction for Growth) is a scalable, multi-agent platform designed to advance hybrid learning through AI agent-supported, one-on-one conversational coaching. The platform hosts a suite of learning agents that support knowledge exploration, knowledge construction, and knowledge demonstration—capabilities essential for upskilling and reskilling workforce competencies. Currently, AIM HIGH includes agents designed for summarization, causal/process analysis, and argumentation activities, with its companion system.

AI-Powered Multimodal Analytics of Learner Dynamics in Learner-Centered Activities

Learner-centered learning environments encourage autonomy, collaboration, and creative problem-solving, but they also present challenges in understanding how students engage and progress individually. This project leverages AI-driven multimodal analytics to examine learners’ dynamics using video data collected from a learner-centered classroom, focusing on how learners interact with peers, tools, and tasks. By using computer vision, machine learning techniques, and multimodal large language models (MLLMs), the project aims to detect key events such as collaboration, focus shifts, tool use, and moments of guidance-seeking. The ultimate goal is to develop an AI model that supports timely and personalized feedback, offering instructors and learners deeper insights into the learning process and helping to shape more responsive educational practices.

AI-Powered Multimodal Hybrid Instruction for Growth in Nursing Simulation (AIM HIGH-SIM)

Simulation-based learning plays an important role in nursing education but often faces challenges due to being resource-intensive, which limits accessibility and scalability. The AIM HIGH-SIM project, in collaboration with the School of Nursing at Georgia State University, aims to establish a flexible framework for facilitating simulation-based learning through a conversational AI-augmented simulator. This framework will provide instructors the flexibility to easily add scenario cases along with related assessment criteria. These scenarios will support learners in practicing simulations within physical simulation rooms as part of their nursing education, with assessments drawn from both dialogue interactions and visual data within the simulation space. The system will provide personalized feedback, supporting both simulation centers and individual practice. By leveraging the capabilities of AI and structured framework design, the project enables learners to continuously practice and refine critical thinking and communication skills in a realistic, flexible, and authentic environment.