At C21U, we recognize that research in innovative education takes many forms. We work with faculty, staff, administrators, and students, addressing both course- and program-level questions. We investigate learning at multiple levels, from individual courses to institution-wide systems, using qualitative, quantitative, and computational methods to generate insights that inform decision-making and advance Georgia Tech’s leadership in educational innovation.
Advancing the Science and Practice of Lifetime Learning
Our team’s research aims to improve learner experiences and outcomes by generating evidence that informs instructional design, program development, and strategic innovation. We study learning across diverse environments, including classrooms, blended learning settings, online programs, and emerging digital platforms, with a focus on designing equitable, scalable systems that respond to learners’ needs.
Our work spans the learner lifecycle — from access and admissions to engagement, skill development, and long-term success — enabling the Institute and its partners to deliver transformative learning experiences grounded in data and research.
Through this work, we help ensure that educational innovation is evidence-based and aligned with the evolving needs of learners and the workforce.
AI in Education
Artificial intelligence (AI) is transforming how learning experiences are designed, delivered, and evaluated. We explore how AI can support personalized learning, enhance feedback and assessment, improve decision-making, and enable new forms of interaction between learners and learning environments. Our work examines the opportunities and implications of AI to help educators and institutions adopt it thoughtfully, responsibly, and effectively.
We focus on designing AI-enabled learning systems that are evidence-based and scalable, ensuring that we ground technological innovation in learning sciences. We study how AI influences cognitive engagement, metacognition, motivation, and the development of durable skills. Through experimentation, simulation, modeling, and implementation, we generate research that informs policy, pedagogy, and institutional strategy.
Projects in this area include:
- Implementing Socratic Mind and exploring generative AI and assessment practices in higher education
- Digital Twin of Self-Regulated Learners: Effects of Metacognition, Feedback, and Task Complexity
- Leveraging Large Language Models to Detect and Summarize Cognitive Presence in Online Discussion Forums (2025 DEMOcon Best Paper Award)
- LOR Insights (LORI): AI-Based Leadership Skill Assessment for Online Master’s Program
Immersive and Emerging Technologies
Our researchers investigate how immersive technologies, such as virtual reality and extended reality, can enhance learning, engagement, and durable skill development. We study learners' and instructors' experiences in these environments and identify factors that influence adoption and effectiveness.
By exploring emerging technologies alongside AI and analytics, we aim to inform the design of next-generation learning environments that foster deeper learning and meaningful experiences.
Data-Driven Education
We conduct research using large-scale learning data to understand learner behaviors, engagement patterns, and outcomes across online and hybrid environments. Using advanced analytics and machine learning, we generate insights that support continuous improvement and proactive learner support.
Learning Analytics for Lifetime Learning
Our researchers leverage various learning analytics and machine learning techniques using big learning data generated across different online learning environments designed for lifetime learning.
- Developing Financial Aid Prediction Models for Undergraduate Applicants
- Predicting Cognitive Presence in At-Scale Online Learning: MOOC and For-Credit Online Course Environments
- Comparative Analysis of the Feature Extraction Approaches for Predicting Learners' Progress in Online Courses: MicroMasters Credential versus Traditional MOOCs
- Influential Text-Based Features in Predicting Admission Status of Online Degree Applicants
- Predicting Applicant Admission Status for Georgia Tech's Online Master's in Analytics Program
- Analyzing Learner Engagement in a MicroMasters Program Compared to Non-Degree MOOC
VIP: Data-Driven Education
Our researchers, with support from a Vertically Integrated Projects team and graduate research assistants, analyze large-scale datasets on millions of learners enrolled in Georgia Tech's online courses. These data sets include Clickstream, event type, and discussion forum data, which are used to identify behavioral patterns and improve course instructional design. In addition, the team is focusing on integrating AI into education to enhance learning experiences, including its use in online courses to provide personalized learning paths and improved student feedback.
Collaborative Research
The research team often collaborates with faculty from diverse disciplines on interdisciplinary STE(A)M research, including:
- Relating Neural Mechanisms for Learning to Instructional Events in Online Learning Environments. In collaboration with researchers from Georgia Tech's School of Psychology, the ongoing work was presented at the 2022 International Mind, Brain, and Education Society (IMBES) Conference.
- An exploration of incorporating visual art activities into a STEM course (i.e., STEAM) that would help enhance students' creativity and problem-solving, both of which exemplify 21st-century skills. The research was done in collaboration with faculty in Civil Engineering; this paper is currently a manuscript in preparation.
Partnerships, Collaborations, and Consultations
C21U is always seeking research partnerships and collaboration opportunities both within and beyond the Georgia Tech community. If you would like a research consultation or to submit news about a recent grant or proposal you've won, please send all relevant information to Jonna Lee.
