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

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. 

 

AI-ALOE's Annual Review Meeting attended by AI2 Lab Members

AI-ALOE's Annual Review Meeting attended by AI2 Lab Members

June 22, 2023

Director Dr. Min Kyu Kim and the graduate associates from the AI2 lab, Jinho Kim and Yoojin Bae, attended the AI-ALOE Annual Review Meeting on June 22, 2023. NSF's annual review team, as well as multiple AI-ALOE scholars, research scientists, postdocs, and graduate research assistants participated in this meeting either in person or virtually.

During the meeting, Yoojin presented about Concept Learning in the Research Overview & Overall Evaluations session while Jinho presented about Personalization in Concept Learning in the Personalized Learning session.

Our two graduate associates also took part in the poster session to present their recent research work:

  • An Investigation of Knowledge-Based AI vs. Human Evaluation in the Context of Academic Summary Evaluation: Similarities, Dissimilarities, and Being Toward Mutual Understandings 
  • Clustering Cognitive Engagement Changes in Longitudinally Traced Discussion Data from a Graduate-Level Online Course

For more information about the Annual Review Meeting, please visit the following links: 

Presentations at the 2023 ISLS Annual Meeting

Presentations at the 2023 ISLS Annual Meeting

June 15, 2023

Dr. Min Kyu Kim along with our graduate associates Jinho Kim and Yoojin Bae presented three short papers at the 2023 International Society of Learning Sciences (ISLS) Annual Meeting that took place from June 10-15 in Montreal, Canada. The sessions were very successful, with a large number of attendees.

A Test of Learning Progress Models Using an AI-Enabled Knowledge Representation System
Monday, June 12th, 3:00 to 4:30 PM EDT, S-LAAI2 - AI and Human Interaction in Learning Environments 

This study tested two competing learning progress models - the three-stage and two-stage models - based on an AI-enabled formative assessment tool. Additionally, human-rated scores were used to further validate the models. The data for this investigation consisted of expository essays about a complex problem scenario written by 116 students and 6 experts. The validation analyses, including descriptive statistics, C-LCDM, and group-mean difference tests, demonstrated that the two-stage model was a better framework given the technology. The findings showed the potential of the model to determine learners' conceptual change over time.

 

An Investigation of Knowledge-Based AI vs. Human Evaluation in the Context of Academic Summary Evaluation: Similarities, Dissimilarities, and Being Toward Mutual Understandings
Monday, June 12th, 3:00 to 4:30 PM EDT, S-LAAI2 - AI and Human Interaction in Learning Environments 

This study aims to explore the similarities and dissimilarities of knowledge-based AI evaluations vs. human evaluations and discuss how they can be utilized for formative feedback in academic summary writing. Data were collected from 62 students who utilized AI-based formative feedback to make revisions to their summaries. We compared indices on three dimensions (surface, structure, semantic) that were automatically generated through this software with human-rated evaluations. MANOVA results of learners’ initial draft and final revision showed learning gains in the semantic dimension and human-evaluated scores. Some significant correlations were observed between automatic and human-rated evaluations. Given that each measure can be interpreted to provide different insights, we suggest combining knowledge-based AI and human evaluations for rich and informative feedback.

 

Clustering Cognitive Engagement Changes in Longitudinally Traced Discussion Data from a Graduate-Level Online Course
Thursday, June 15th, 10:30 to 12:00 PM EDT, CSCL-S10 / Important considerations for STEM collaboration in Higher Education 

Given the pervasive use of online learning, especially asynchronous discussion forums, ensuring students’ ongoing engagement is critical. Examining students’ cognitive presence throughout a series of discussions can disclose different engagement patterns. However, little research has leveraged longitudinal discussion data. The current study aims to identify learner engagement states and their trajectories in discussion forums using two-cohort data from a graduate-level online course. We used HMM (Hidden Markov model) to discover the hidden longitudinal engagement clusters. Results showed two clusters in the first cohort and three clusters in the second cohort. Students tended to remain in a similar engagement state, and their participation patterns depended on the discussion dynamics likely influenced by class size.

 

AI2 lab members attended the AI ALOE's Year 2 Executive Advisory Board Meeting

AI2 lab members attended the AI ALOE's Year 2 Executive Advisory Board Meeting

May 19, 2023

Director Dr. Min Kyu Kim and the graduate associates from the AI2 lab, Jinho Kim, Lia Haddadian, and Yoojin Bae, attended the AI ALOE's Year 2 Executive Advisory Board Meeting on May 19, 2023. Approximately 40 members, including 7 EAB members, participated in the meeting either in person or online.

During the meeting, Dr. Kim presented his recent AI in education research titled "Personalized Learning Strategies for Concept Learning."

Additionally, Jinho and Yoojin participated in the poster session, where they presented their recent research work:

  • An Investigation of Knowledge-Based AI vs. Human Evaluation in the Context of Academic Summary Evaluation: Similarities, Dissimilarities, and Being Toward Mutual Understandings 
  • Clustering Cognitive Engagement Changes in Longitudinally Traced Discussion Data from a Graduate-Level Online Course

For more information about the EAB meeting, please visit the following link:  https://aialoe.org/eab2023/
 

NSF AI ALOE Institute Research Videos Now Available

NSF AI ALOE Institute Research Videos Now Available

May 4, 2023

We have created two video presentations for public access. The videos introduce our AI research and development, centering on two topics: Concept Learning and Personalization. We define concept learning as the cognitive process of building a solid understanding of STEM content necessary for developing critical thinking and problem-solving skills. Personalization is one of the goals pursued in AI in education that enables learners to be better engaged in and regulate their learning and performance. Additionally, personalized support for instructors can empower them to support learners in more adaptive and appropriate ways. Our three graduate associates, Jinho, Lia, and Yoojin, have served as narrators. Enjoy!

Attending and Presenting at the 2023 AERA Annual Meeting

Attending and Presenting at the 2023 AERA Annual Meeting

April 13, 2023

Members of our AI2 Lab attended and presented at the 2023 American Educational Research Association (AERA) Annual Meeting, which took place from April 13-16 at Chicago, IL.

Dr. Min Kyu Kim and our graduate associate, Jinho Kim, presented the paper titled "Using Machine Learning for Cognitive Presence Detection in Asynchronous Online Learning" at the Instructional Technology SIG Roundtables on the 13th.

Title: 

Using Machine Learning for Cognitive Presence Detection in Asynchronous Online Learning

Abstract:

This study aims to explore and develop a machine learning model to automatically detect individual students’ levels of cognitive presence they exert while participating in online discussions. To this end, we used the Practical Inquiry Model to analyze 1,360 discussion posts gathered from the multiple sections of a graduate-level online course. We have used the training data to develop a machine learning model. Especially, we tested a deep learning algorithm, Bidirectional Encoder Representations from Transformers (BERT) model, to find the most suitable hyperparameters. Our final model showed a 94% accuracy in detecting cognitive presence levels reflected on given discussion posts. Findings demonstrate the potential of AI to detect students’ cognitive presence and provide real-time feedback.   

Yoojin presented her recent study at LS-GSA Spring Student Conference

Yoojin presented her recent study at LS-GSA Spring Student Conference

April 7, 2023

Our graduate associate, Yoojin Bae, presented her recent study entitled "Clustering Cognitive Engagement Changes in Longitudinally Traced Discussion Data from a Graduate-Level Online Course" at the 2023 Learning Science (LS)-Graduate Student Association (GSA) Spring Conference. All of our lab members attended and celebrated her first but wildly successful presentation. Yoojin is in her second semester.

Title: 

Clustering Cognitive Engagement Changes in Longitudinally Traced Discussion Data from a Graduate-Level Online Course

Abstract:

Given the pervasive use of online learning, especially asynchronous discussion forums, ensuring students’ ongoing engagement is critical. Examining students’ cognitive presence throughout a series of discussions can disclose different engagement patterns. However, little research has leveraged longitudinal discussion data. The current study aims to identify learner engagement states and their trajectories in discussion forums using two-cohort data from a graduate-level online course. We used HMM (Hidden Markov model) to discover the hidden longitudinal engagement clusters. Results showed two clusters in the first cohort and three clusters in the second cohort. Students tended to remain in a similar engagement state, and their participation patterns depended on the discussion dynamics likely influenced by class size.    

Awards received by Jinho Kim and Lia Haddadian

Awards received by Jinho Kim and Lia Haddadian

April 7, 2023

We have exciting news about two of our graduate associates! Jinho Kim won the Outstanding Ph.D. Student in Learning Technologies Award from the Department of Learning Sciences at the College of Education & Human Development at Georgia State University (GSU), and Lia Haddadian was awarded the Doctoral Student Fellowship Award by the Department of Learning Sciences at the College of Education & Human Development at GSU.

The Outstanding Ph.D. Student in Learning Technologies Award is given to a student who has demonstrated potential for excellence in research, teaching, and service in instructional technology. The Doctoral Student Fellowship Award is awarded to Ph.D. students who demonstrate exceptional scholarship and academic potential in the department each year.

Congratulations to both!

Dr. Min Kyu Kim will serve as a guest editor for a special issue of Frontiers in Psychology

Dr. Min Kyu Kim will serve as a guest editor for a special issue of Frontiers in Psychology

April 6, 2023

Director, Dr. Min Kyu Kim, working with Co-Director, Dr. Nam Ju Kim, will serve as a guest editor for a special issue that will be published in two journals, Frontiers in Psychology (SSCI listed) and Frontiers in Education.  Below is the introduction to the special issue.  

 

Title: Advancements and Challenges in AI Applications for Education

Keywords: AI in Education, Intelligent Tutoring Systems, Automated Essay Scoring, Feedback Generation, Chatbot, Predictive Analytics, Ethical Concerns, Fairness, Transparency, Privacy, Teacher Job Displacement

Background: 

Artificial intelligence (AI) is increasingly being used in education, offering opportunities to improve teaching and learning outcomes. Intelligent tutoring systems, automated essay scoring, and feedback generation using chatbots are a few examples of AI technologies being developed and implemented in classrooms. Chatbots like ChatGPT can provide personalized feedback and support to students, analyze performance data to suggest tailored learning resources and assist with grading processes. AI can also enhance accessibility for students with disabilities. While AI has the potential to transform education, there are also ethical and practical considerations that need to be addressed to ensure that the benefits of AI in education are realized while minimizing any potential drawbacks.

Goal: 

The goal of this special issue is to advance research in the areas of intelligent tutoring systems, automated essay scoring and feedback generation using chatbots, and predictive analytics for student performance. We aim to explore recent advances in these areas and identify opportunities for further research to improve the effectiveness and accessibility of AI-powered educational tools. Additionally, we aim to address issues related to the use of AI in education, such as ethical considerations, bias and discrimination, transparency, and privacy concerns. By addressing these issues, we hope to promote the responsible and effective use of AI in education.

Scope:

We invite original research articles, review papers, and case studies that contribute to advancing the state of the art in intelligent tutoring systems, automated essay scoring and feedback generation using chatbots, and predictive analytics for student performance. We also welcome papers that explore the ethical, fairness, and equality concerns associated with the use of AI in education, and propose solutions to mitigate these concerns. Specific themes that we would like contributors to address include, but are not limited to: 

-Design and development of intelligent tutoring systems, automated essay scoring and feedback generation using chatbots, and predictive analytics for student performance
-Evaluation and validation of AI-powered educational tools
-Ethical considerations in the use of AI in education, including bias and discrimination, transparency, and privacy concerns
-Strategies for addressing teacher job displacement and ensuring that AI complements rather than replaces human teachers
-Case studies of AI implementation in educational settings

Presentations at Spring 2023 Department Lunch & Learn Meetings

Presentations at Spring 2023 Department Lunch & Learn Meetings

March 24, 2023

Two graduate research associates, Jinho Kim and Lia Haddaian, and Dr. Min Kyu Kim presented recent studies on AI in education at the knowledge-sharing events called "Lunch & Learn" in the Department of Learning Sciences at Georgia State University. The meetings were held virtually and had approximately 20 attendees per each, including faculty members and graduate students. The first presentation, led by Jinho Kim and Dr. Kim, was held on March 10th, and the second presentation on March 24th, 2023 was presented by Lia and Dr. Kim.

 

AI-Enabled Automatic Evaluation of Learning Cognitive Engagement during Asynchronous Online Discussions

Presented by Jinho Kim and Min Kyu Kim on March 10, 2023

While online discussions are frequently used to foster interaction, collaboration, and learner achievement, it is challenging for instructors to evaluate and monitor individual students' cognitive engagement during the discussions. This study aims to explore and develop machine learning models to automatically evaluate levels of cognitive engagement from online discussion posts. Using discussion data coded by the Practical Inquiry Model, we search through different hyperparameter combinations of the Bidirectional Encoder Representations from the Transformers (BERT) model. Our results show a finetuned model with 72% accuracy. We then apply the developed model to a new dataset and use the results to explore student clusters and trajectories within a semester. The findings of the study show clearly distinctive cognitive engagement patterns and transitions to the upper-level clusters. This study illustrates the potential of BERT to automatically evaluate learner cognitive engagement levels and demonstrates how to monitor learner progress in terms of cognitive engagement.

 

A Comprehensive Model of AI Literacy from a Developmental Perspective 

Presented by Lia Haddadian and Min Kyu Kim on March 24, 2023

This study aims to propose a comprehensive model of AI literacy from a developmental perspective and demonstrate its potential as an analytic framework to conduct empirical research. We build upon the literature on AI literacy to propose an AI literacy model that consists of cognitive and non-cognitive domains. The cognitive domain is clustered into AI literacy knowledge and AI ethics, while self-efficacy and emotions in using AI constitute the non-cognitive domain. Each AI literacy domain is detailed with associated levels on which an individual’s progress can be explained. In the second part, we introduce an empirical study in which we used the AI literacy model to analyze the AI literacy development of two instructors who deployed an AI tool in their college-level online courses. The empirical case study demonstrates the potential of the proposed model to help research AI literacy development.