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

Hyunkyu Han joins as a graduate research associate.

Hyunkyu Han joins as a graduate research associate.

January 22, 2024

Hyunkyu joins our lab. She earned a Master's in Educational Technology from Yonsei University, South Korea and an undergrad in Elementary Education from Gongju National University. Currently, she is an elementary school teacher in South Korea. Her research interests involve leveraging AI to create personalized learning environments that cater to the needs of learners and implementing AI-augmented active learning strategies.

.. Read More

Dr. Min Kyu Kim presented generative AI-based application development projects.

Dr. Min Kyu Kim presented generative AI-based application development projects.

December 8, 2023

The director, Dr. Min Kyu Kim, presented generative AI-based application development projects at the NSF-ALOE Use-Inspired AI meeting (Dec. 4, 2023) and the first  AI ALOE's Year 3 Executive Advisory Board Meeting (Dec. 8, 2023).

.. Read More

AI2 Lab's 2023 Thanksgiving Party at Dr. Kim's House

AI2 Lab's 2023 Thanksgiving Party at Dr. Kim's House

November 27, 2023

As part of our tradition, we had a great time at Dr. Kim's house during the 2023 Thanksgiving party! We celebrated our achievements throughout the year.

.. Read More

See more >

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.

See more >

Publications

Bae, Y., Kim, J., & Kim, M. (2023). Clustering cognitive engagement changes in a longitudinal traced discussion data from an online course. In Damșa, C., Borge, M., Koh, E., & Worsley, M. (Eds.). Proceedings of the 16th International Conference on Computer-Supported Collaborative Learning - CSCL 2023 (pp. 297-300). Montreal, Canada: International Society of the Learning Sciences.

Kim, J., Haddadian, G., & Kim, M. (2023). An investigation of knowledge-based AI vs. human evaluation in academic summary evaluation: Similarities, dissimilarities, and being toward mutual understandings. In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Eds.). Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023 (pp. 994-997). Montreal, Canada: International Society of the Learning Sciences.

Kim, M., Kim, N., Haddadian, G., & Heidari, A. (2023). A test of learning progress models using an AI-enabled knowledge representation system. In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Eds.). Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023 (pp. 986-989). Montreal, Canada: International Society of the Learning Sciences.

See more >