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

Five papers accepted to ISLS 2024 Annual Meeting

Five papers accepted to ISLS 2024 Annual Meeting

April 12, 2024

We're excited to announce that five of our papers have been accepted for presentation at the 2024 International Society of Learning Sciences (ISLS) Annual Meeting in Buffalo, New York, taking place from June 10th to 14th. These papers, stemming from our NSF AI ALOE and NSF SaTC projects, touch on our work related to AI-augmented concept learning, private AI curriculum in computer science education, AI-augmented summarization, and the evaluation of learner comprehension through AI techniques. We look forward to sharing our findings!

.. Read More

Dr. Min Kyu Kim presented about the theoretical underpinnings for the SMART project

Dr. Min Kyu Kim presented about the theoretical underpinnings for the SMART project

April 1, 2024

Dr. Kim Kyu Kim, our director, presented the theory-driven development and research for SMART at today's AI-ALOE Foundational and Use-Inspired AI Meeting.

Dr. Kim began by outlining the project's aim of aiding learners in understanding key concepts. He elaborated on how theories such as Personalization, Community of Inquiry (COI), and ICAP (Interactive, Constructive, Active, Passive) have influenced SMART's design....

.. Read More

Welcoming our visiting scholar: Sua Im!

Welcoming our visiting scholar: Sua Im!

March 31, 2024

We are pleased to welcome Sua Im to our lab as a visiting scholar! Sua Im has joined our lab as a visiting scholar through the Outstanding Graduate Students’ Overseas Training Program (BK21 FOUR Project) sponsored by The National Research Foundation of Korea. Her research focuses on various aspects of sports, serious leisure, and enhancing quality of life. Currently, she is exploring the intersection of AI-based programs and well-being among older adults within senior centers.

.. 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., Davis, A., & Kim, M. (accepted). A study on AI-augmented concept learning: Impact on learner perceptions and outcomes in STEM education. Proceedings of the 18th International Conference of the Learning Sciences/Computer-Supported Collaborative Learning (ICLS/CSCL-2024). Buffalo, NY: International Society of the Learning Sciences.  

Haddadian, G., Panzade, P., Takabi, D., & Kim, M. (accepted). Evaluating private artificial intelligence (AI) curriculum in computer science (CS) education: Insights for advancing student-centered CS learning. Proceedings of the 18th International Conference of the Learning Sciences/Computer-Supported Collaborative Learning (ICLS/CSCL-2024). Buffalo, NY: International Society of the Learning Sciences.  

Kim, J., Bae, Y., Stravelakis, J., & Kim, M. (accepted). Investigating the influence of AI-augmented summarization on concept learning, summarization skills, argumentative essays, and course outcomes in online adult education. Proceedings of the 18th International Conference of the Learning Sciences/Computer-Supported Collaborative Learning (ICLS/CSCL-2024). Buffalo, NY: International Society of the Learning Sciences.  

See more >