"Our Mission is to Build on Theories of Learning and Instruction to Create Innovative Learning Environments that Maximize Learner Capacity to Achieve Learning Goals"

AI-ALOE Newsletter Feature

AI-ALOE Newsletter Feature

August 17, 2023

We are delighted to share the recent recognition of our exceptional lab members in the AI-ALOE Summer Newsletter! 

Our lab director, Dr. Min Kyu Kim, has been prominently featured in the Team Member Splotlight for his work with SMART. SMART has helped transform concept learning through AI, enabling learners to engage with learning materials and enhance their understanding. SMART's impact, evident through successful implementations in TCSG College classes, has brought about positive engagement, motivation, and performance outcomes for adult learners. Dr. Kim has also unveiled exciting furture directions for SMART, promising continued innovation and advancement. 

Furthermore, we celebrate the achivements of our graduate associate, Jinho, in the Student Highlights section. Her research journey within AI-ALOE has centered on personalization in concept learning and AI-augmented summary writing, harnessing the power of SMART. Jinho's dedication to enhaning the learning expereince is driven by a motivation to make a positive impact online education. 

Be sure to check out the full newsletter here: AI-ALOE Newsletter: Summer 2023

Congratulations! The director, Dr. Min Kyu Kim, has been awarded a new NSF IUSE grant.

Congratulations! The director, Dr. Min Kyu Kim, has been awarded a new NSF IUSE grant.

August 4, 2023

We have great news! The director, Dr. Min Ky Kim, has received the new NSF IUSE grant entitled 'Artificial Intelligence-Scaffolded Pre-Classroom Learning for Large, Introductory Undergraduate Physics Courses.' Dr. M. Shameer Abdeen from the Department of Physics & Astronomy at Georgia State University is the Co-PI of the grant project. See the information below.

NSF: Improving Undergraduate STEM Education (Grant/Award Number: 2315709)

  • Title: IUSE-Engaged Student Learning (Level 1): AI-Scaffolded Pre-Classroom Learning for Large/Introductory Undergraduate Physics Courses
  • Position: Principal Investigator 
  • Project dates: 08/01/2023 – 07/31/2026 
  • Budget: $ 298,375

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. Undergraduate students often need to develop a solid understanding of content or problem situations in self-paced online learning contexts to prepare for in-classroom active and collaborative learning. However, unsupervised pre-classroom learning can be an ongoing issue in a student-centered learning model. This problematic situation is particularly evident in large introductory-level STEM courses where traditional instructional techniques are less effective. 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.

This project will take three phases to develop and investigate the effectiveness of AI-augmented pre-classroom activities to promote engaging student experiences in undergraduate physics courses. The project's research will take a Participatory Research (PR) approach that emphasizes the direct engagement of faculty members who teach physics courses in designing and implementing new assignments. These faculty members will also co-construct research through a partnership with researchers to conduct a mixed-methods study of instructors and students in the courses. The primary research goal of the first phase is to identify topics and problem tasks that utilize AI-scaffolded pre-classroom learning and investigate learner engagement and progression in the pre-class assignments. The evaluation studies during the second phase will prove whether knowledge development during pre-classroom learning can help students solve cognitively demanding tasks in classrooms and develop positive self-efficacy in STEM. The findings will also determine whether AI in education improves students' well-being inside and outside of classrooms, with a focus on students traditionally underrepresented in STEM education. Extensive data collected in the final phase will uncover the relationships among pre-classroom activities, in-classroom performance, self-efficacy, interest in physics, and student backgrounds, including gender, race, ethnicity, first-generation status, and L2 learners. Our sequence mining and cluster analysis will reveal students' different hidden engagement states and group their engagement trajectories, explaining how cluster membership and trajectories vary across students' backgrounds. Consequently, this project will lay the groundwork for further research to develop an AI-scaffolded pre-classroom learning model that promotes most students' success in introductory physics courses. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.

Dr. Young Ju Jeong has joined our lab as a visiting scholar.

Dr. Young Ju Jeong has joined our lab as a visiting scholar.

August 4, 2023

It is a great pleasure to introduce another visiting scholar, Dr. Young Ju Jeong, who has joined our lab!  

Dr. Jeong received her Ph.D. in computer science and electrical engineering from the University of Southern California, Los Angeles, USA. She is currently an associate professor in the software department at Sookmyung Women's University, Seoul, Korea. Before joining Sookmyung, she worked as a Research Member at Samsung Advanced Institute of Technology, Suwon, South Korea. Her research interests include Augmented Reality and Virtual Reality content rendering and display design. During her visit, she will lead the research and development efforts to integrate AI techniques with AR/VR learning environments.

Welcome our visiting scholar: Mr. Taehee Lee!

Welcome our visiting scholar: Mr. Taehee Lee!

August 3, 2023

We are very excited to introduce Mr. Taehee Lee as our new visiting scholar.

During his visit, he will work with us to advance AI-augmented teaching and learning research. Mr. Lee earned a B.S. degree in electrical engineering from Korea University, Seoul, Korea, in 2004, and an M.S. degree in electrical engineering from the University of Southern California, Los Angeles, United States, in 2006. He is currently pursuing his Ph.D. degree in computer education from Sungkyunkwan University, Seoul, Korea. Additionally, he holds a senior engineer position at Korea Telecom in Seoul, South Korea. Prior to joining KT, he worked as a junior engineer at Samsung Advanced Institute of Technology. His areas of expertise include computer vision and specialized person attribute recognition in artificial intelligence, industrial/vertical deep learning applications, unstructured data analysis, and AI-based education platforms.