AI2 Research Laboratory

AI2 stands for Artificial intelligence (A), Interactive (I), Augmented (A), and Immersive (I) learning environments. AI2 represents the innovative learning environments we pursue to advance more adaptable, engaged, equitable, and effective teaching and learning in various educational contexts. We build on the legacy of our understanding of how people learn to answer the question, how we can scaffold people to learn better. Our endeavor to promote AI2 learning is driven by our belief that most learners can achieve learning goals if provided with appropriate instructional support.

News@AI2 RL

External Advisory Board Meeting Presentations

External Advisory Board Meeting Presentations

December 4, 2024

During AI-ALOE's External Advisory Board meeting on December 4th, our director, Dr. Min Kyu Kim, presented on SMART Deployment and Research in Fall 2024 within the AI Technologies and Adult Learning section.

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AI2RL Lab Thanksgiving Feast

AI2RL Lab Thanksgiving Feast

November 27, 2024

On November 27th, our lab came together at Dr. Kim's home to celebrate Thanksgiving with a joyful potluck gathering. The table was overflowing with a wide variety of delicious dishes, including meat skewers, beef, sushi, bulgogi, potato pancakes, and homemade cookies!

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AI and Instructional Design Outreach Workshop

AI and Instructional Design Outreach Workshop

November 16, 2024

Dr. Min Kyu Kim and our graduate associates, Jinho Kim and Yoojin Bae, participated in AI-ALOE's AI and Instructional Design Outreach Workshop on November 15, 2024. This event served as a valuable opportunity to connect researchers and educators, emphasizing the intersection of AI innovation and instructional design practices. It also provided a platform to showcase the capabilities of our SMART technology.

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Research Projects

NSF IUSE-Engaged Student Learning (Level 1): AI-Scaffolded Pre-Classroom Learning for Large/Introductory Undergraduate Physics Courses

NSF IUSE-Engaged Student Learning (Level 1): AI-Scaffolded Pre-Classroom Learning for Large/Introductory Undergraduate Physics Courses

This Engaged Student Learning Level 1 project aims to serve the national interest by designing and implementing an Artificial Intelligence (AI)-augmented formative assessment and feedback system. This system will help students develop source-based STEM arguments, such as STEM text summarization, or problem spaces, which are mental representations of a problem and of multiple paths to solving it. This will be implemented in large, undergraduate introductory physics courses at an urban university that serves diverse and historically underrepresented student groups. Persistent learner engagement in pre-classroom learning activities is critical to learner success in introductory STEM courses. The innovation of the project will include AI-generated adaptive scaffolding information and learning progress feedback with data visualization techniques to help students with concept learning and self-regulatory behaviors. The unique learning opportunities supported by an AI-scaffolded feedback system will significantly increase students' engagement levels in self-paced online pre-classroom learning. This, in turn, will help students acquire content knowledge and build a proper understanding of problems to prepare themselves for success in in-classroom interactive problem-solving activities.

NSF AI Institute: AI Institute for Adult Learning and Online Education (ALOE) (Grant/Award Number: 2112532), National Science Foundation.

NSF AI Institute: AI Institute for Adult Learning and Online Education (ALOE) (Grant/Award Number: 2112532), National Science Foundation.

The ALOE institute is led by the Georgia Research Alliance (GRA), headquartered at Georgia Tech. The interdisciplinary and cross-institutional effort unites experts in computer science, artificial intelligence (AI), cognitive science, learning science and education from two Non-Profit Organizations (GRA and IMI Global), three industry partners (IBM, Boeing and Wiley) and seven universities (Georgia Tech, Georgia State, Harvard, Arizona State, Drexel, University of North Carolina, and multiple colleges within the Technical College System of Georgia [TCSG]). The multinational company Accenture joins NSF as a funding partner of ALOE.

The 5-year NSF grant is to establish the NSF AI Institute for Adult Learning and Online Education (ALOE) that will develop new AI theories and techniques as well as new models of lifelong learning, and evaluate their effectiveness at Georgia Tech, Georgia State, multiple colleges within the Technical College System of Georgia (TCSG), as well as with corporate partners IBM, Boeing and Wiley. ALOE aims to integrate AI theories, models, and techniques into online adult learning to create more available, affordable, adaptable, and scalable learning experiences, which creates more effective and efficient teaching and learning.

Artificial Intelligence-Augmented Motivation Indicator (AIMI) System

Artificial Intelligence-Augmented Motivation Indicator (AIMI) System

AIMI is an AI-augmented system that detects learners’ real-time motivation levels. AIMI utilizes neural network algorithms that interpret student facial expressions to indicate students’ current emotions (i.e., anger, disgust, fear, happiness, sadness, surprise, and neutral) and motivation levels in real-time.

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Publications

Haddadian, G., Radmanesh, S., & Haddadian, N. (2024). Construction and validation of a Computerized Formative Assessment Literacy (CFAL) questionnaire for language teachers: An exploratory sequential mixed-methods investigation. Language Testing in Asia, 14(33). https://doi.org/10.1186/s40468-024-00303-2

Kim, M. K., Kim, J., & Heidari, A. (2024). Exploring the multi-dimensional human mind: Model-based and text-based approaches. Assessing Writing, 61, 100878. https://doi.org/10.1016/j.asw.2024.100878

Haddadian, G. & Haddadian, N. (2024). Innovative use of grammarly feedback for improving EFL learners’ speaking: Learners’ perceptions and transformative engagement experiences in focus. The Journal of Applied Instructional Design, 13(2). 

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