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 chaired the Learning@Scale 2024 Conference

Dr. Min Kyu Kim chaired the Learning@Scale 2024 Conference

July 25, 2024

Our director, Dr. Min Kyu Kim, served as a program chair for the Learning@Scale 2024 Conference held at the Georgia Institute of Technology from July 17 to 19.

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Welcoming our visiting scholar: Sumin Hong!

Welcoming our visiting scholar: Sumin Hong!

June 24, 2024

We are excited to welcome Sumin Hong to our lab as a visiting scholar!

Sumin Hong has joined our lab as a visiting scholar during the summer, 2024. She is currently a PhD candidate at Seoul National University, South Korea. Her research interest is technology integrated instructional design including Artificial intelligence, Virtual reality, virtual world and collaborative learning tool for meaningful learning. During her visit, she is exploring AI integrated education and immersive learning for adult learning.

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Presentations at AI-ALOE's 2024 Annual Review Meeting

Presentations at AI-ALOE's 2024 Annual Review Meeting

June 21, 2024

Director Dr. Min Kyu Kim and our graduate associate Jinho Kim attended and presented at AI-ALOE's 2024 Annual Review Meeting on June 21, 2024. The AI-ALOE team, comprising scholars, researchers, scientists, and student researchers, shared talks about our progress with the NSF evaluation team.

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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. & 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). 

Bae, Y., Kim, J., Davis, A., & Kim, M. K. (2024). A Study on AI-Augmented Concept Learning: Impact on Learner Perceptions and Outcomes in STEM Education. In Lindgren, R., Asino, T. I., Kyza, E. A., Looi, C. K., Keifert, D. T., & Suárez, E. (Eds.), Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024 (pp. 1450-1453). International Society of the Learning Sciences.

Haddadian, G., Panzade, P., Takabi, D., & Kim, M. K. (2024). Evaluating Private Artificial Intelligence (AI) Curriculum in Computer Science (CS) Education: Insights for Advancing Student-Centered CS Learning. In Lindgren, R., Asino, T. I., Kyza, E. A., Looi, C. K., Keifert, D. T., & Suárez, E. (Eds.), Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024 (pp. 2271-2272). International Society of the Learning Sciences.

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