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

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.

1. Theoretical Framework for the SMART Project

During the morning session of the first day, on March 7th, Dr. Kim proposed a hierarchical model of theories from a design research perspective to explain the theoretical framework for the SMART project. Specifically, he leveraged theories of mental models and self-determination theory as foundational principles supporting cognitive and motivational analysis in SMART design experiments. Additionally, he demonstrated how design guidelines derived from the general principles of the Community of Inquiry (COI) model and the Interactive-Constructive-Active-Passive (ICAP) framework were applied to SMART development.

2. SMART Experiments 

In the afternoon session on March 7th, Yoojin presented the SMART experiments conducted during Fall 23 and Spring 24 at English and Biology classes within the Technological College System of Georgia (TCSG) colleges. To assess different user experiences and levels of adaptation to the AI technology SMART, we conducted A/B experiments in the format of a quasi-experiment and randomized controlled trial, respectively. Yoojin demonstrated how AI2 researchers collaborated with instructors to design and implement SMART-integrated teaching and learning. Additionally, she shared her reflections on successful outcomes and failures for future consideration in AI-related experiment designs, including automated data collection at scale.

3. Summative Evaluation of the Three-Year of the SMART Deployment    

On March 8th, Jinho presented our data analysis findings from the three-year deployment of SMART technology in two TCSG courses, English and Biology. As the AI ALOE team is currently in the midst of the project's third year, evaluating the effectiveness of SMART deployment for learner performance is crucial. To achieve this, we defined learning at both the micro and meso levels. Micro-level learning pertains to specific learning assignments within a unit, with summarization tasks on SMART considered as examples in this project. Meso-level learning, on the other hand, occurs across assignments throughout a course over a semester. Our assumptions were twofold: (a) learners' efforts in revising with SMART improve their conceptual understanding of the course materials (micro-level learning), and (b) this improved conceptual understanding influences higher performance in subsequent learning activities. To test these assumptions, linear mixed effects models were deployed, revealing a positive and significant impact across the courses.

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. We had a wonderful time with the delicious food Dr. Kim prepared for us, as well as tasty desserts and a choice of tea or coffee. The Spring Feast provided us with a much-needed opportunity to relax and unwind, setting the stage for a memorable and enjoyable break.

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.

Dr. Kim discussed the various learning metrics utilized within the SMART project, starting from outlining the theory of change guiding our work, highlighting our research questions, and explaining the tools we use for data collection. Additionally, he addressed the successful strategies we've implemented, the challenges we face, and our efforts to improve data collection scalability. Dr. Kim also touched upon our team's approach to machine teaching, focusing on the use of generative AI for expert modeling. He briefly mentioned our upcoming pilot test plans and W.R.I.T.E. development as well.

Hyunkyu Han joins as a graduate research associate.

Hyunkyu Han joins as a graduate research associate.

January 22, 2024

Let's welcom Hyunkyu, our new graduate research associate.  

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 of Education. 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.

Hyunkyu particularly focuses on the NSF-IUSE project in which she will lead the design of AI-augmented pre-classroom learning activities and strategies. The goal is to facilitate learners' smooth transition from pre-classroom to in-classroom learning and from self-paced to collaborative and hands-on learning in large-sized introductory physics courses.