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

Presentations at the AI ALOE Year 3 Executive Advisory Board Meeting

Presentations at the AI ALOE Year 3 Executive Advisory Board Meeting

May 10, 2024

Dr. Min Kyu Kim, two of our AI2 graduate associates, and our visiting scholar attended the AI-ALOE Year 3 Executive Advisory Board (EAB) Meeting on May 8th. About 30 AI-ALOE colleagues attended and shared their research at AI-ALOE. During the EAB meeting, Jinho presented on the topic of Fostering Deeper Understanding through Text Summarization while Dr. Kim participated in a Panel on Personalization with a presentation titled Design Dimensions for AI-Augmented Personalized Learning.

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Congratulations! Dr. Ali Heidari

Congratulations! Dr. Ali Heidari

May 6, 2024

Congratulations to our graduate associate, Ali Heidari, who received his Ph.D. degree on May 8, 2024! Ali has worked tirelessly under the guidance of Dr. Kim over the past five years. In a symbolic moment, his Ph.D. candidate title was replaced with the prestigious title of Dr. Heidari as Dr. Kim hooded him during the ceremony. Let's all join in celebrating this significant achievement and Ali's well-deserved success!

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Lia Haddadian, our graduate associate, has achieved two significant milestones.

Lia Haddadian, our graduate associate, has achieved two significant milestones.

April 25, 2024

We are thrilled to announce that our graduate associate, Lia Haddadian, has achieved two significant milestones. During her three years of working at the lab, Lia has made outstanding contributions. Her paper was published in the reputable open-access journal, The Journal of Applied Instructional Design, and she was also awarded the prestigious "AACE Award" at the Society for Information Technology & Teacher Education (SITE) conference on March 25, 2024, in Las Vegas, Nevada. This award was selectively given to only five out of 411 papers presented, making it a remarkable achievement.

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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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