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

To achieve our mission, the AI2 Research Laboratory builds an interdisciplinary and cross-institutional effort that unites experts in learning sciences, computer sciences, STEM educators, and literacy researchers from multiple institutions. We pursue answers to two critical questions in education: (a) how can we personalize and advance learning experiences supported by emerging technologies such as AI and augmented reality? and (b) how can we design highly accessible learner experiences using learning technologies that deepen learner engagement?

Specifically, we have focused on four aspects of learning technologies

AI-Supported Learning

We have deployed advanced AI techniques–for example, affective computing and Natural Language Processing (NLP) AI– to develop automated formative assessment and feedback technologies for learner cognition, motivation, and emotions.

Interactive Learning

Human-Computer Interaction is not just about learner-to-computer interaction. Technology can enable learners to be more interactive with peers and instructors in technology-based learning environments.

Augmented Learning

Teaching and learning effectiveness can be augmented by learning technologies appropriately integrated with the optimal pedagogy in a context. We focus on the affordances of new technologies to track and diagnose the patterns of learner performance and provide multi-dimensional scaffolds catered to individuals’ needs.

Immersive Learning

Problem-centered learning requires effective problem-posing strategies that engage learners in a real-world problem situation. Given a problem context, learners play a pivotal role in solving the problem. We utilize mixed-reality and augmented-reality techniques to build immersive learning in real-world scenarios.

IMPACTS & TRANSFORMATIONS

Our effort centering on AI2 aims to improve education practices in regard to adaptability, engagement, equitability, and effectiveness.

Adaptability

AI2 learning environments help provide personalized learning that reacts to learners’ individual differences in beliefs and knowledge as well behaviors and affects. A smarter technology can diagnose and provides individualized, formative guidance as learners practice more open-ended and complex skills such as solving complex STEM problems or writing discipline-specific academic essays.

Engagement

Sustaining or improving learner engagement in ill-defined and complex tasks is challenging. The demanding problems or collaborative leaning tasks can create emotional turmoil and decrease morale, resulting in the early loss of interest. We create technologies that track and support multidimensional learner engagement (i.e., cognitive, behavioral, and emotional engagement) in interactive learning environments.

Equitability

AI2 learning environments create more diverse, equitable, and inclusive learning experiences by providing technology-enhanced scaffolding adaptive to individual students’ cognition, metacognition, and motivation. Technology can work as a learning agent that helps learners connect their backgrounds, current knowledge, skill, and beliefs to the new problem situation.

Effectiveness

AI2 learning environments make teaching effective by informing instructors of the patterns of individuals or groups of learners and suggesting appropriate instructional remedies. Also, AI2 learning environments enable learners to engage in interactive and immersive learning activities and to regulate their cognition, behaviors, emotions, and motivation as they progress toward learning goals. Technology plays an essential role in helping learners to experience meaningful learning.

We are an interdisciplinary collaborative team from multiple institutions.
Please join us through the Contact Us

Researchers at the AI2 RL
Min Kyu Kim, Ph.D.

Min Kyu Kim, Ph.D. 

Min Kyu Kim is an Associate Professor of Learning Sciences at Georgia State University. Kim is a co-founder and co-director of the AI2 Research Laboratory. His research pursues innovative research that advances our understanding of how people learn and how to assess and foster transformative learning, especially in technology-rich learning environments. Specifically, he has focused on three major areas of research: (a) adaptive learning informed by learning progression models, (b) descriptive and predictive models of how people learn individually or in a group in CSCL environments, and (c) creative and innovative design solutions to advance technology-enhanced learning experience. For example, he is committed to ing new models of learning progression and computational models to collect, externally represent, and diagnose learner characteristics such as cognition, learning emotion, and social interaction in emerging learning technologies. Also, he has developed adaptive learning technologies called Student Mental Model Analyzer for Research and Teaching (SMART) and the Artificial Intelligence-Enabled Scaffolding System (AISS) to support students' academic writing.

Nam Ju Kim, Ph.D.

Nam Ju Kim, Ph.D. 

Nam Ju Kim is a director of Learning Sciences program and an assistant professor in the department of Teaching and Learning at University of Miami. He has been active in a wide range of technology-related learning initiatives including educational games, robots, artificial intelligence, spatial tools, and adaptive online learner-centered instructional models where cutting-edge technology can be embedded. His research has been supported by internal grants, foundations, industries, and federal agencies. His recent research focuses on a) investigating the effectiveness of the online learning platform for English learning through big data and machine learning algorisms and b) demonstrating the concept of adopting the Artificial Intelligence-based learning support systems he has developed to the Learning Management System.

Research Partners
Daniel Takabi, Ph.D.

Daniel Takabi, Ph.D. 

Daniel Takabi is an Associate Professor and Next Generation Scholar in the Computer Science Department at Georgia State University (GSU). He is Founding Director of the Information Security and Privacy: Interdisciplinary Research and Education (INSPIRE) Center, a designated National Center for Academic Excellence in Cyber Defense Research (CAE-R) and National Center of Academic Excellence in Cyber Defense Education (CAE-CD) by the National Security Agency and the Department of Homeland Security. His research interests include cybersecurity, privacy, trustworthy AI, and STEM education.

Hongli Li, Ph.D.

Hongli Li, Ph.D. 

Hongli Li, Ph.D., is an associate professor of Research, Measurement & Statistics in the Department of Educational Policy Studies at Georgia State University. She graduated from the Pennsylvania State University with a Ph.D. in educational measurement in 2011. Her major areas of research are quantitative methods and applied measurement in education. At Georgia State University, she teaches a number of courses, such as structural equation modeling, item response theory, meta-analysis, educational measurement, classroom assessment, research methods in education. Her research has been supported by the Spencer Foundation, Educational Testing Service, among other sources. She has published in many refereed journals, and her publications can be viewed here: https://scholar.google.com/citations?user=jfQii-oAAAAJ&hl=en

Kathryn Soo McCarthy, Ph.D.

Kathryn Soo McCarthy, Ph.D. 

Dr. McCarthy is an Assistant Professor of Educational Psychology in the Department of Learning Sciences at Georgia State University and the director of the Disciplinary Comprehension Lab. Her research examines how reading and writing processes vary across disciplines and across readers. She is interested in how AI can be used to study and support these processes through educational technologies.

Yinying Wang, Ph.D.

Yinying Wang, Ph.D. 

Yinying Wang is an associate professor of educational leadership in Educational Policy Studies at Georgia State University. Her research interest intersects technology, decision making, neuroscience and social network analysis in educational leadership and policy. She is also an associate faculty member in the Neuroscience Institute at Georgia State University.

Graduate Research Associates
Ali Heidari – Doctoral Student

Ali Heidari – Doctoral Student 

Ali 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). He earned his master’s degree in Applied Linguistics from the Department of Applied Linguistics at Georgia State University. Ali uses various natural language processing tools and computational and behavioral experiments to study learner experience (LX) during feedback information processing within technology-enabled formative feedback information systems.

Beck Graefe – Doctoral Student

Beck Graefe – Doctoral Student 

Beck Graefe is currently a first-year doctoral student in the University of Miami’s (UM) Research, Measurement, and Evaluation program. His research interests include health and educational disparities, and intersectional research methods. Beck has experience teaching STEM courses at the secondary level through the undergraduate level. He also has worked to promote the participation of underrepresented students in STEM fields through programmatic activities that provide academic and psychosocial support to undergraduates at UM. In the future, he hopes to develop methods that reduce bias in research and to apply those methods to reduce marginalization of vulnerable populations.

Celia Rubio – Doctoral Student

Celia Rubio – Doctoral Student 

Celia completed her undergraduate studies at the University of Miami with a major in Psychology and a double minor in Biology and Art and Art History. Now that she has completed her master’s, she is continuing as a doctoral student in the Research, Measurement and Evaluation program. As an employee of Miami-Dade County Public Schools, Celia is naturally drawn to issues faced by the school district, particularly in light of the COVID-19 pandemic. Her broad research interests center around academic achievement in the vulnerable student population. She is particularly interested in how statistical techniques could be applied to positively influence academic growth trajectories of underprivileged students

Cris Rocha Vicentini – Doctoral Student

Cris Rocha Vicentini – Doctoral Student 

Cristiane Vicentini is a Ph.D. candidate at the University of Miami. She earned an M.A. in TESOL from Hawaii Pacific University, and an M.S. in Instructional Design and Technology from the University of Tampa. Her research interests include TESOL, multiliteracies, multimodality, and the use of technology for language teaching and learning.

Crystal Budrage – Doctoral Student

Crystal Budrage – Doctoral Student 

Crystal Bundrage is a Ph.D. student in the College of Education and Human Development at Georgia State University. She received her master’s degree in Business Administration from the University of South Florida, and her bachelor’s degree in Business Administration from Stetson University. She has spent over a decade supporting technology-enhanced instruction and online learning through the development and facilitation of workshops and professional development courses in the higher education setting. Her research focuses on the various instructional approaches and practices used in a blended synchronous learning environment.

Golnoush Haddadian – Doctoral Student

Golnoush Haddadian – Doctoral Student 

Golnoush Haddadian is a Graduate Research Associate and a Ph.D. student at College of Education and Human Development, Georgia State University (GSU). She has received her master’s degree in Applied Linguistics (TEFL) from Sharif University of Technology. Using IRT as its psychometric framework, she developed a Computerized Adaptive Test of Written Receptive Vocabulary (CATWRV); a desktop-based software to test English vocabulary knowledge of foreign language learners. As a researcher, she is fervently interested in inviting technology to understand how people learn and help them learn more effectively. Her main areas of research include investigating adaptive technologies to improve learning and designing innovative technological solutions to facilitate learning.

Hua Ran - Doctoral Student

Hua Ran - Doctoral Student 

Hua Ran is a Ph.D. student candidate in the Department of Teaching and Learning at the University of Miami. Her research focuses on various instructional approaches/models (technology-based instruction, research-based instruction called cognitively guided instruction, online discussion-based) to broaden students’ innovative/interdisciplinary learning practices across multiple subjects (i.e., mathematics, science, and literacy). Recently, she is particularly interested in minority students/learners (i.e., academically disadvantaged students, English language learners, etc.) and kinds of instructional approaches that help them productive learning. She is proficient in various advanced statistical methods (i.e., HLM, propensity score matching, comprehensive meta-analysis) and attempts to apply big data mining and Bayesian analysis in educational fields. She has published in high-quality academic journals and has presented numerous papers nationally and internationally in some top conferences (i.e., ICLS and AERA).

Jinho Kim – Doctoral Student

Jinho Kim – Doctoral Student 

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.

Liping Yang – Doctoral Student

Liping Yang – Doctoral Student 

Liping Yang is a Ph.D. student in STEM education at the University of Miami. She earned a bachelor's degree in education from Sichuan Normal University in China and a master's degree in international education from Teachers College, Columbia University in the United States. She had extensive teaching expertise in a variety of cultural settings. Her research interests include the use of cutting-edge technology such as augmented reality, virtual reality, augmented reality, mixed reality, robots, game-based learning, and artificial intelligence in a variety of learning contexts to increase students' engagement and outcomes.

Murat Kasli – Doctoral Student

Murat Kasli – Doctoral Student 

Murat Kasli earned his Bachelor’s Degree in Mathematics Education from Gazi University (Ankara, Turkey) with a teaching license in High School Education. Before joining the Research, Measurement, and Evaluation Department at the University of Miami, he worked in mathematics education studies as an author, content developer, and course designer for three years. He has provided more than 30 workshops on mathematics education for the teacher training program. He has worked as a Psychometrician Intern at the AICPA and ACT, Inc, and conducted research projects focusing on data forensics. His current research interests include detecting test fraud, item response theory, and cognitive diagnostic models.

YooJin Bae – Doctoral Student

YooJin Bae – Doctoral Student 

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.

Alumni
Shyama Bhuvanendran Sheela, M.S.

Shyama Bhuvanendran Sheela, M.S. 

Master of Science in Computer Science Computer Science, Georgia State University, December 2018 Shyama contributed to the SMART development in the following areas:

  • Natural language processing, social network analysis, data visualization
  • Web programming

Swathi Kiran Reddy Pallamreddy, M.S.

Swathi Kiran Reddy Pallamreddy, M.S. 

Master of Science in Computer Science Computer Science, Georgia State University, May 2018 Swathi worked on SMART project with the following major contributions:

  • System architecting and database design
  • Natural language processing, social network analysis, data visualization
  • Web programming