"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 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 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 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 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 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 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.
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.