Dr. Min Kyu Kim attended the AI-ALOE External Advisory Board Meeting π
April 26, 2022
Dr. Min Kyu Kim attended the AI-ALOE External Advisory Board Meeting at Georgia Tech on April 26, 2022. It was the first in-person meeting of the AI-ALOE Institute as a whole team! At the meeting, Dr. Kim also presented a recent study conducted with our co-director, Dr. Nam Ju Kim.
A Design Study of the Artificial Intelligence-Augmented Motivation Indicator (AIMI) System
In online learning, motivation encourages learners to initiate certain actions, use appropriate strategies to achieve their goals, and sustain actions even in difficult environments. Therefore, detecting students’ motivation accurately and on time is crucial as educators can easily identify what learning contents, activities, and instructional methods significantly undermine learners’ motivation in the online learning environment. To measure students’ real-time motivation level in online learning, we developed Artificial Intelligence-Augmented Motivation Indicator (AIMI) system. This study aims to validate the accuracy of AIMI by comparing motivation levels measured by both this tool and traditional paper-based surveys. As a result, the motivation values generated by the AIMI system demonstrated a high level of accuracy, with an error rate of only about 10%.
Presentations at 2022 AERA Annual Meeting π
April 25, 2022
Our co-directors, Drs. Min Kyu Kim and Nam Ju Kim, presented two posters at 2022 Annual Meeting for American Educational Research Association (AERA) in April 21-26, 2022, San Diego.
Revisiting a Three-Stage Learning Progression Model Through a Technology-Based Formative Assessment System (Poster 5)
Fri, April 22, 4:15 to 5:45pm PDT (7:15 to 8:45pm EDT), SIG Virtual Rooms, SIG-Instructional Technology Virtual Poster Session Room
Link to i-Presentation
In a previous study, we proposed a three-stage learning progression model that drew on theories of mental models and expertise development to test stages through which students develop an expert-like understanding of a problem situation. Even though the initial work showed promising results, the student data were manually processed and thus required further validation with computerized data analytics. This study revalidated the learning progression model, using data generated by a technology-based formative feedback system. We used textual explanations about a complex problem scenario written by 136 students and 6 experts. Descriptive statistics and Exploratory Factor Analysis (EFA) results demonstrated the existence of latent knowledge attributes. We are now in further investigation, and full results will be presented at the convention.
A Design Study of the Artificial Intelligence-Augmented Motivation Indicator System (Poster 1)
Mon, April 25, 2:30 to 4:00pm PDT (5:30 to 7:00pm EDT), San Diego Convention Center, Floor: Upper Level, Sails Pavillion
In online learning, motivation encourages learners to initiate certain actions, use appropriate strategies to achieve their goals, and sustain actions even in difficult environments. Therefore, detecting students’ motivation accurately and on time is crucial as educators can easily identify what learning contents, activities, and instructional methods significantly undermine learners’ motivation in the online learning environment. To measure students’ real-time motivation level in online learning, we developed Artificial Intelligence-Augmented Motivation Indicator (AIMI) system. This study aims to validate the accuracy of AIMI by comparing motivation levels measured by both this tool and traditional paper-based survey. As a result, the motivation values generated by the AIMI system demonstrated a high level of accuracy, with an error rate of only about 10%.
Crystal Bundrage presented at the 32nd International SITE Conference π
April 13, 2022
Our graduate research associate, Crystal Bundrage, presented at this year’s Society for Information Technology & Teacher Education Conference. Their presentation, “Design and Development of an Online Professional Development Course on Culturally Responsive Pedagogy Using the ADDIE Model”, described the process of creating an asynchronous institutional, professional development course centered on Culturally Responsive Pedagogy (CRP). CRP uses cultural knowledge, and the lived experiences of ethnically diverse students as a conduit for teaching more effectively. As a more diverse population grows, there is a need for educators to improve their intercultural fluency and to incorporate it into their pedagogy.
Bundrage, C. & Mapson, K. (2022). Design and Development of an Online Professional Development Course on Culturally Responsive Pedagogy Using the ADDIE Model. In E. Langran (Ed.), Proceedings of Society for Information Technology & Teacher Education International Conference (pp. 248-256). San Diego, CA, United States: Association for the Advancement of Computing in Education (AACE). Retrieved April 29, 2022 from https://www.learntechlib.org/primary/p/220742/.
AI-ALOE Introduction Video π
April 11th, 2022
Dr. Chris Dede, one of the co-PIs of the AI-ALOE institute, created a fantastic video introduction to AI-ALOE. This video was submitted to the 2022 STEM for ALL Video Showcase. This video gives you an overview of the National AI Research Institute for Adult Learning and Online Education (AI-ALOE). Funded by the National Science Foundation and headquartered at Georgia Tech, AI-ALOE aims to leverage AI technologies for transforming adult education in effectiveness, efficiency, scale, and personalization, thereby making it more available, achievable, and equitable.
Runner-Up Best Research Paper Award π
March 23, 2022
“A Design Study of the Artificial Intelligence Motivation Indicator System” coauthored by co-directors Dr. Nam Ju Kim and Dr. Min Kyu Kim, is the Runner-up for the Online Teaching and Learning SIG 2022 Best Research Paper Award.
Motivation is a critical component of effective learning and directly affects academic achievement. In this study, we have developed the Artificial Intelligence-Augmented Motivation Indicator (AIMI) system that is tuned to detect learners’ real-time motivation levels to help students sustain their motivation for learning activities. The AIMI system’s unique approach leverages artificial intelligence techniques to analyze learners’ facial expressions and determine their emotions and associated motivation levels in real-time. A pilot version of AIMI demonstrated high accuracy of motivation detection, which supports the potential use of Artificial intelligence in the field of psychology and education.
Publication on teachersβ perceptions of AISS π
March 29, 2022
Kim, N., & Kim, M. (2022). Teacher's perceptions of using an artificial intelligence-based educational tool. Frontiers in Education, 7(755914), 1-13. https://doi.org/10.3389/feduc.2022.755914
Abstract
Efforts have constantly been made to incorporate AI into teaching and learning; however, the successful implementation of new instructional technologies is closely related to the attitudes of the teachers who lead the lesson. Teachers’ perceptions of AI utilization have only been investigated by only few scholars due to an overall lack of experience of teachers regarding how AI can be utilized in the classroom as well as no specific idea of what AI-adopted tools would be like. This study investigated how teachers perceived an AI-enhanced scaffolding system developed to support students’ scientific writing for STEM education. Results revealed that most STEM teachers positively experienced AI as a source for superior scaffolding. On the other hand, they also raised the possibility of several issues caused by using AI such as the change in the role played by the teachers in the classroom and the transparency of the decisions made by the AI system. These results can be used as a foundation for which to create guidelines for the future integration of AI with STEM education in schools, since it reports teachers’ experiences utilizing the system and various considerations regarding its implementation.
Publication on AISS π
February 14, 2022
Kim, M., Kim, N. & Heidari, A. (2022). Learner experience in Artificial Intelligence scaffolded argumentation. Assessment and Evaluation in Higher Education. Advance online publication. http://doi.org/10.1080/02602938.2022.2042792
Abstract
Writing academic arguments is a complex and demanding task, even for proficient tertiary students. At the same time, providing prompt support for individual students working on discipline-specific arguments is often challenging for instructors. Artificial Intelligence (AI) techniques enable automated and adaptive educational scaffolding. In this study, we leveraged Natural Language Processing (NLP) AI techniques in an AI-Supported Scaffolding (AISS) system to evaluate written arguments and present alternative writing examples that human experts might write. To evaluate a pilot version of AISS, we gathered mixed-method data from 14 students enrolled in two sections of the same graduate-level online course (6 students in Cohort 1 and 8 students in Cohort 2). We used the Tool for the Automatic Analysis of Cohesion (TAACO) to track revisions in written arguments. TAACO indices demonstrated that the students used AI-generated scaffolding to build stronger claims with more elaborate and cohesive ideas. In written reflections, students revealed their perceived value of AISS, and visual inspection of their written arguments indicates that students used AISS feedback to improve their arguments. The findings demonstrate the potential of AI to provide personalized scaffolding for academic argument composition.
Keywords: Artificial intelligence, scaffolding, expert model, academic argument, personalized learning
Yoojin Bae joins as a graduate research associate π
January 1, 2022
Welcome Yoojin to the AI2 research laboratory!
Yoojin is a Graduate Research Associate and a Ph.D. student at the Department of Learning Sciences in the College of Education and Human Development, Georgia State University (GSU). She earned her master’s degree in Education from Seoul National University, South Korea. Her research interests lie in the intersection of Education and Computer Science. Her work’s focus is on examining ways to leverage artificial intelligence in order to offer adaptive learning environment. Yoojin is exploring natural language processing to analyze text data in educational circumstances.
Jinho Kim joins as a graduate research associate π
January 1, 2022
Welcome Jinho to the AI2 research laboratory!
Jinho Kim is a Graduate Research Associate and a Ph.D. student at the Department of Learning Sciences in the College of Education and Human Development, Georgia State University (GSU). She earned her Bachelor of Arts (B.A.) in Education, Bachelor of Science in Engineering (B.S.E.) in Computer Science and Engineering, and Master of Arts (M.A.) in Education from Yonsei University, South Korea. Her research interests include educational technology, online learning environments, computer and software education, and artificial intelligence-assisted learning.
Welcome to the AI2 Research Laboratory π
January 1, 2022
I am Min Kyu Kim, Associate Professor of Learning Sciences at Georgia State University. I am the co-founder and co-director of the lab. On behalf of the co-director, Dr. Nam Ju Kim, and other lab members, I welcome you to the AI2 research laboratory!
I am excited to open our lab and begin our endeavor to create innovative learning environments that maximize learner capacity to achieve learning goals. Beyond such effectiveness, our work pursues more diverse, equitable, and inclusive learning experiences augmented by learning technologies.
Our lab builds an interdisciplinary and cross-institutional effort that unites experts in learning sciences, computer sciences, STEM educators, and literacy researchers from multiple institutions to achieve our mission. To strengthen our collaboration and impact on educational practices, we seek your unique expertise and enthusiasm for technology-augmented adaptive, equitable, engaged, and effective teaching and learning. Please join our lab. We hope that this lab continues to grow through our collective efforts.
Warm regards,
Min Kyu Kim, Ph.D.
Associate Professor
Department of Learning Sciences
Georgia State University
mkim120@gsu.edu