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

AI2 members presented the 3rd year research work at the AI ALOE retreat on March 7 - 8.

AI2 members presented the 3rd year research work at the AI ALOE retreat on March 7 - 8.

March 18, 2024

Dr. Min Kyu Kim, accompanied by two AI2 graduate associates, Jinho Kim and Yoojin Bae, participated in and delivered three presentations during the AI ALOE retreat at the CODA building at Georgia Tech on March 7th and 8th. This retreat marked the third year of ALOE activities, where researchers from multiple institutions shared their Year 3 studies, with a focus on theory, experimentation, and data analysis.

.. Read More

AI2RL Spring Feast

AI2RL Spring Feast

March 12, 2024

We had our highly anticipated Spring Feast during the spring break, gathering members of our lab together for a time of fun and enjoyable gathering. On March 12th, the members of our lab, including our two visiting scholars and three graduate associates, gathered at Dr. Kim's place at lunchtime to enjoy a midday filled with great food and pleasant company.

.. Read More

Invited Talk: Dr. Min Kyu Kim presented our learning measures and approach to machine teaching

Invited Talk: Dr. Min Kyu Kim presented our learning measures and approach to machine teaching

February 26, 2024

Dr. Min Kyu Kim, our director, was invited to present at the NSF-ALOE Learning and Management Discussions on February 26th. His presentation was about the SMART project's learning measures and approach to machine teaching.

.. 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 >