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

Dr. Min Kyu Kim Represents ALOE at Summit for AI Institutes Leadership (SAIL) in Pittsburgh.

Dr. Min Kyu Kim Represents ALOE at Summit for AI Institutes Leadership (SAIL) in Pittsburgh.

October 10, 2024

From October 7 to October 10, Dr. Min Kyu Kim, attended the Summit for AI Institutes Leadership in Pittsburgh, Pennsylvania (SAIL 2024). The event brought together representatives from 27 AI institutes funded by the National Science Foundation to discuss advancements in various fields, including food security, public safety, education, and weather forecasting.

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AI-ALOE Mini-Retreat: We presented SMART Research in Year 4

AI-ALOE Mini-Retreat: We presented SMART Research in Year 4

October 4, 2024

On October 2, 2024, our graduate associates, Jinho Kim, Seora Kim, Yoojin Bae, along with Dr. Min Kyu Kim attended  AI-ALOE Mini-Retreat. Yoojin Bae and Seora Kim presented “SMART Research for Year 4,” focusing on technology integration in adult online education. Discussions centered on four key areas: educational goals for adult learners, instructional design collaboration, grounding in learning theories, and research contributions to adult education.

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Welcoming Seora Kim as a graduate research associate

Welcoming Seora Kim as a graduate research associate

August 26, 2024

We are happy to welcome Seora Kim as a new graduate research associate! 

Seora Kim is a Graduate Research Associate and a Ph.D. student at the Department of Learning Sciences in the College of Education and Human Development, Georgia State University (GSU). She earned her Bachelor of Arts (B.A.) in Education and English language and literature, and Master of Arts (M.A.) in Education from Yonsei University, South Korea. Her research interests include educational technology, artificial intelligence-assisted learning to enhance critical thinking and creativity.

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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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