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.
Dr. Min Kyu Kim presented generative AI-based application development projects. 🔗
December 8, 2023
The director, Dr. Min Kyu Kim, presented generative AI-based application development projects at the NSF-ALOE Use-Inspired AI meeting (Dec. 4, 2023) and the first AI ALOE's Year 3 Executive Advisory Board Meeting (Dec. 8, 2023).
Dr. Kim introduced our two-track approach. We have continuously enhanced the current SMART (track 1), while concurrently developing a new application that fully harnesses generative AI LLMs (track 2). New features, empowered by generative AI, have been modularly developed, allowing some to be applied to SMART improvements. The W.R.I.T.E., Writing and Reasoning Intelligent Tutor for Education system, not only leverages SMART but also incorporates entirely different AI techniques, extending its applicability to various learning tasks. Our goal is to complete the initial WRITE development by the end of year 3.
AI2 Lab's 2023 Thanksgiving Party at Dr. Kim's House 🔗
November 27, 2023
As part of our tradition, we had a great time at Dr. Kim's house during the 2023 Thanksgiving party! We celebrated our achievements throughout the year. During the lab feast event each semester, Dr. Kim showcased his culinary talents. This time, he prepared Korean-flavored roast beef along with several other dishes. Unfortunately, we realized that we hadn't taken a picture of the table. We couldn't wait to enjoy the delicious beef!
AI2 members presented papers at 2023 AECT conference, October 15, 2023 - October 19, 2023 🔗
October 30, 2023
At the 2023 Association for Educational Communication and Technology (AECT) convention, the AI2 members—Lia, Jinho, Yoojin, and our director, Dr. Kim—presented research papers from the NSF AI ALOE and NSF SaTC projects. Our graduate associates achieved remarkable success in their presentations and subsequent Q&A sessions. Through our projects, we demonstrated AI's potential to transform education by designing and implementing AI-augmented teaching and learning experiences, such as personalized learning in higher education and adult education. See the details of the papers below.
Bae, Y., Kim, J., Haddadian, G., Davis, A., & Kim, M. (October 2023). The impact of an AI-based educational tool, with a focus on technology acceptance and metacognitive awareness of adult learners. The 2023 Association for Educational Communications and Technology (AECT) Conference, Orlando, FL.
Abstract: This study examines whether AI-powered scaffolding during pre-classroom activities provides an advantage to students in an undergraduate-level Biology course. The study deployed Student Mental Model Analyzer for Research and Teaching (SMART), an AI-based formative assessment and feedback platform, to help students summarize reading materials. We observed the use of SMART had a positive effect on learners' perceived usefulness of technology, and the number of key concepts and density of summaries predicted learners' perceived technology usefulness.
Haddadian, G., Kim, J., Bae, Y., & Kim, M. (October 2023). A Comprehensive Model of AI Literacy from a Developmental Perspective. The 2023 Association for Educational Communications and Technology (AECT) Conference, Orlando, FL.
Abstract: We proposed a comprehensive model of AI literacy from a developmental perspective and demonstrate its potential as an analytic framework. Built upon the literature, we proposed a model that consists of cognitive and non-cognitive domains and supplemented our discussion with an empirical study which has used the model to analyze the AI literacy development in two college-level online courses. The empirical study demonstrates the potential of the proposed model to help research AI literacy development.
Haddadian, G., Panzade, P., Takabi, D., & Kim, M. (October 2023). A Design Study of Problem-Centered Instruction (PCI) for Private Artificial Intelligence (AI) Curriculum Development. The 2023 Association for Educational Communications and Technology (AECT) Conference, Orlando, FL.
Abstract: This design study examines a pilot test that implemented PCI for private AI curriculum in Computer Science (CS) education to identify the strengths and weaknesses of the curricular activities. The results indicated the feedback received from both the instructor and the students was generally positive. However, the study identified several areas of concern that indicate the need for further improvement. The study concludes by presenting the lessons learned and recommendations for enhancing the curriculum.
Kim, J., Bae, Y., Haddadian, G., Morris, W., Crossley, S., Holmes, L., Stravelakis, J., & Kim, M. (October 2023). AI-augmented summarization: Impact on online adult learners’ concept learning, discussion quality, and achievement. The 2023 Association for Educational Communications and Technology (AECT) Conference, Orlando, FL.
Abstract: This study aims to investigate how an AI-augmented summarization tool called the Student Mental Model Analyzer for Research and Teaching (SMART) impacts concept learning, discussion quality, and the achievement of adult learners. Findings from 21 participants in an undergraduate-level English course indicated that using SMART helped learners build a solid understanding of the readings and achieve higher end-of-year final scores. The results suggest the potential of using AI-augmented summarization tools to enhance learning outcomes.
Kim, J., Bae, Y., Haddadian, G., & Kim, M. (October 2023). Leveraging Machine Learning to Automatically Evaluate Cognitive Engagement in Asynchronous Online Discussions. The 2023 Association for Educational Communications and Technology (AECT) Conference, Orlando, FL.
Abstract: This study focuses on developing machine learning models to automatically evaluate cognitive engagement in asynchronous online discussions. To this end, the bidirectional encoder representations from transformers (BERT) was finetuned and trained, resulting in an accuracy of 72%. The developed model was utilized to evaluate a previously uncoded dataset, which was then analyzed in terms of learning clusters and trajectories. This research demonstrates the potential of using BERT for cognitive engagement assessment.
Six undergraduate interns from the Honor College's CS department have joined our lab 🔗
October 6, 2023
Please welcome our undergraduate interns: Akshat, Akshit, Jeshal, Jinash, Shanshan, and Solanlly. Our interns will work on developing a new version of SMART, fully enhanced by generative AI. They are exceptionally intelligent students from the Honors College. These smart interns will contribute to making SMART even smarter. Cheers!
Akshat Namdeo
Akshat is an honors college undergraduate student in the Department of Computer Sciences at Georgia State University. He is actively engaged in various lab projects, drawing upon his six-year immersion in the multifaceted domain of web development.
Akshit Calonia
Akshit is an honors college student pursuing a major in Computer Science at Georgia State University. He is currently undertaking an internship in the lab, focusing on harnessing the power of AI to develop smarter and more innovative applications.
Jeshal Patel
Jeshal is an honors college undergraduate student pursuing a major in Computer Science at GSU. He is interning at the lab, driven by his keen interest in integrating technology and AI into various aspects of educational problem-solving.
Jinash Rouniyar
Jinash is an honors college undergraduate student pursuing a major in Computer Science at GSU. He is actively engaged in various lab projects, leveraging his experience as a front-end developer at NSTEM and his proficiency in Java/Python programming languages.
Shanshan Wen
Shanshan is an undergraduate student in the Department of Computer Science at Georgia State University. She possesses a keen interest in Artificial Intelligence and full-stack development, with a strong desire to leverage these skills to enhance the online learning experience.
Solanlly Rijo Lake
Solanlly Rijo Lake is an Honors college student majoring in Computer Science with a concentration in Cybersecurity at Georgia State University. Her passion for learning and improving education by leveraging learning technologies in her home country, the Dominican Republic, led her to volunteer in the lab.
Dr. Min Kyu Kim named a program chair for the ACM's Learning @ Scale 2024 Conference. 🔗
September 25, 2023
The director, Dr. Min Kyu Kim, was named a program chair for the Association for Computing Machinery's (ACM) Learning @ Scale 2024 Conference. This annual event highlights high-quality research on how learning and teaching can be transformed at scale in diverse learning environments.
The Learning at Scale community investigates large-scale, technology-mediated learning environments that typically have many active learners and few experts on hand to guide their progress or respond to individual needs (personalized learning support). Modern learning at scale typically draws on data at scale collected from current learners and previous cohorts of learners over time. Large-scale learning environments are very diverse.
This festival of learning in 2024 will be co-located with the Educational Data Mining (EDM) 2024 conference at Georgia Tech from July 14, 2024 to July 20, 2024.
https://learningatscale.acm.org/
Invited Talk: Dr. Min Kyu Kim presented the COI model and the Issue-Hypothesis Tree. 🔗
September 18, 2023
The director, Dr. Min Kyu Kim, was invited to the NSF-ALOE Use-Inspired AI meeting to present the Community of Inquiry (COI) Model and the Issue-Hypothesis Tree.
Dr. Kim introduced the Revised-CoI framework, which includes learning presence, both as a design framework to guide the development of instructional activities integrated with AI technologies for adult online education and as an analytic framework to assess the learner experience in a course with respect to COI elements. Specifically, for ALOE research, he has detailed teaching presence with two distinct types: one led by AI and the other by human instructors who may incorporate AI information. Dr. Kim has demonstrated how the R-COI model could help construct the issue-hypothesis tree (which is an elaboration of issues, testable hypotheses, anticipated outcomes, and appropriate measures) for NSF ALOE research.
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. 🔗
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. 🔗
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.