Artificial Intelligence (AI) and Data Science

Development of an Undergraduate degree concentration in Applications of Artificial Intelligence in Learning Science and Engineering

• $40,000
• Innovation Hub
• Amogh Sirnoorkar, Sanjay Rebello, and Lynn A. Bryan

Promoting Equity and Research Using Adaptive Testing to Support Individualized Instruction at Scale

• $499,867
• National Science Foundation
• Hua Hua Chang
• 10/2023-9/2025

Collaborative Research: Individualizing Instruction and Improving Research using Adaptive Testing (i3RAT)

• $158,416
• National Science Foundation
• Hua Hua Chang
• 09/2022-08/2024

Nurturing Multiplicative Reasoning in Students with Learning Disabilities in a Computerized Conceptual-modeling Environment (NMRSD-CCME)

• $2,969,894.00
• National Science Foundation
• Yan Ping Xin, Ron Tzur, Luo Si
• 2008-2015
• More info

AI Education Integration in South Korea, Taiwan, and the United States

Faculty

• Wanju Huang, Department of Curriculum & Instruction, Purdue University
• Sunnie Watson, Department of Curriculum & Instruction, Purdue University
• Bill Watson, Department of Curriculum & Instruction, Purdue University

Doctoral Students

• Chi-Jia Hsieh, Department of Curriculum & Instruction, Purdue University
• Seoljoo Kang, Department of Curriculum & Instruction, Purdue University
• Wonjin Yu, Department of Curriculum & Instruction, Purdue University

Analysis of value-based Reasoning in Generative-AI Platforms

• Bill Bridges, Department of Physics, Kansas State University
• Tyler Garcia, Department of Physics, Kansas State University
• Justin Hess, School of Engineering Education, Purdue University
• Amogh Sirnoorkar, Department of Curriculum & Instruction, Purdue University

CARE Lab Robotics with Community Engagement Project

CARE (Computer Science Education, Artificial Intelligence Education, Robotics, Emerging Technologies in Education [e.g. Virtual Reality and Augmented Reality])

The CARE lab implemented one robotics project with our community partner, Imagination Station at Lafayette, on April 7th, 2024 (https://carelab.education.purdue.edu/robotics-with-community-engagement/).

ChatGPT and Generative AI as a New Partner in Language Teaching and Learning

• Victoria Lowell, Department of Learning Design and Technology, Purdue University
• Curtis J. Bonk, Learning, Design, and Adult Education, Indiana University Bloomington
• Belle Li, Department of Learning Design and Technology, Purdue University
• Zhuo Zhang, Department of Learning Technologies, Design and School Library Media; Towson University
• Chaoran Wang, Writing Department, Colby College
• Xiangning Li, Faculty of Education, University of Cambridge

Development and analysis of physics research-based assessments using Generative-AI

• Amogh Sirnoorkar, Department of Curriculum & Instruction, Purdue University
• Sai Munikoti, Pacific Northwest National Laboratory, Washington
• Ian Arnold, Department of Physics and Astronomy, Purdue University
• Sanjay Rebello, Department of Physics and Astronomy, Department of Curriculum & Instruction, Purdue University

Development of an Instrument to Measure AI-integrated SDL Personal Attributes for Global Language Learners

• Victoria Lowell, Department of Learning Design and Technology, Purdue University
• Curtis J. Bonk, Learning, Design, and Adult Education, Indiana University Bloomington
• Belle Li, Department of Learning Design and Technology, Purdue University
• Zhuo Zhang, Department of Learning Technologies, Design and School Library Media; Towson University
• Chaoran Wang, Writing Department, Colby College

From Words to Worlds: Language Education as the Gateway to AI Literacy

• Victoria Lowell, Department of Learning Design and Technology, Purdue University
• Belle Li, Department of Learning Design and Technology, Purdue University

Investigating the Integration of AI and Extended Reality Technologies

• Victoria Lowell, Department of Learning Design and Technology, Purdue University
• Weijian Yan, Purdue University
• Belle Li, Department of Learning Design and Technology, Purdue University

Mapping the landscape of artificial intelligence in physics education research

• Amir Bralin, Department of Physics and Astronomy, Purdue University
• Marla Grover, Department of Physics and Astronomy, Purdue University
• Sanjay Rebello, Department of Physics and Astronomy, Department of Curriculum & Instruction, Purdue University
• Amogh Sirnoorkar, Department of Curriculum & Instruction, Purdue University

Promoting analogical reasoning in physics using Generative-AI

• Amogh Sirnoorkar, Department of Curriculum & Instruction, Purdue University
• Ian Arnold, Department of Physics and Astronomy, Purdue University
• Sanjay Rebello, Department of Physics and Astronomy, Department of Curriculum & Instruction, Purdue University

Artificial Intelligence (MS in AI) – This program is a university-wide graduate program that includes courses from the College of Education.

Educational Psychology & Research Methodology

Learning Design and Technology (Residential | Online)

Educational Psychology & Research Methodology

Learning Design and Technology

Quantitative Research, Assessment, and Evaluation in Education (QRAE) Certificate

Applications in Data Science

This program is a university-wide undergraduate program that includes courses from the College of Education. View courses here – https://catalog.purdue.edu/preview_program.php?catoid=16&poid=25991

Chang, H. (2023, December 1). Integrating Psychometric Tools into AI Development [Panel Presentation]. Trustworthy AI Lab for Education Summit. https://www.youtube.com/watch?v=Lj3ZX1GIW1o&list=PL-8_PytklE-NbIgVRNqPXdeMlfsbV9PDE&index=2

Chang, H. (2023, October 4). Building Adaptive Testing Tools to Improve Classroom Assessment in Large Introductory STEM [Presentation]. Center for Astrophysics, Harvard & Smithsonian Science Education, CfA Science Education Department, Harvard University. https://www.youtube.com/watch?v=gCmfTv3fNuE

Chang, H. (2022, June 15). A New Perspective of Computerized Adaptive Testing (CAT): From Testing to Personalized Learning [Presentation]. 2022 NCME Career Contribution Award Presentation. https://youtu.be/X15kcYkGUgA?si=0i7HT5GskpMfjMIM

Chang, H. (2018, April 14). From Adaptive Testing to Adaptive Learning [AERA E.F. Lindquist Award Lecture]. 2018 American Educational Research Association (AERA) Annual Meeting. New York, NY. https://youtu.be/1jZv3b5_-QE?si=58ztPGYTbBKWqXKi

Huang, W., & Yu, W. (2024, April 14). Top-down vs. Grassroot: A comparative study on AI integration barriers in K-12 in South Korea and the United States [Conference presentation]. 2024 American Educational Research Association (AERA) Annual Meeting, Philadelphia, PA, United States. https://www.aera.net/

Huang, W., & Yu, W. (2023, December 1). Artificial intelligence (AI) and education – voices from K-12 educators in South Korea and the United States [Conference presentation]. Trustworthy AI Lab for Education Summit, Notre Dame, IN, United States. https://lucyinstitute.nd.edu/wp-content/uploads/2023/11/TrustworthyAI_Program_2023_v2.pdf

Huang, W. & Yu, W. (2023, November 10). The collaboration between AI and educators. Indiana Association of Colleges for Teacher Education (IACTE) webinar. https://www.inaacte.org/

Huang, W., & Yu, W. (2023, October 15-19). AI education integration in South Korea from early adopter’s perspectives: A case study [Conference presentation]. AECT 2023 Convention, Orlando, FL, United States. https://convention.aect.org/

Jiang, Y., Xie, L., Lin, G., & Mo, F. Widening the Debate: The Academic Community’s Perception of ChatGPT. 2024 American Educational Research Association (AERA) Conference. Philadelphia, PA.

Jiang, Y. (2023, December 1). Exploring ChatGPT: Policy and Regulation of Digital Frameworks Through Twitter Discourse [Conference presentation]. Trustworthy AI Lab for Education Summit, Notre Dame, IN, United States. https://lucyinstitute.nd.edu/wp-content/uploads/2023/11/TrustworthyAI_Program_2023_v2.pdf

Sirnoorkar, A. (2024, February). Leveraging Generative-AI in physics education. Physics Education Research group seminar, Department of Physics and Astronomy, Purdue University.

Sirnoorkar, A., Laverty, J.T., Zollman, D., Rebello, S. & Bryan, L.A. (2023, December). Exploring student and AI generated assumptions to varying degree of promoting. Proceedings, International Conference on Physics Education (ICPE), India.

Sirnoorkar, A. (2023, October). Development and analysis of assessments that promote sensemaking in physics. Physics Education Research group seminar, Department of Physics and Astronomy, Purdue University.

Yu, W., & Huang, W. (2024, April 11). AI is no more a magic box: A case study of an AI educational program [Conference presentation]. 2024 American Educational Research Association (AERA) Annual Meeting, Philadelphia, PA, United States. https://www.aera.net/

Yu, W., Kang, S., & Huang, W. (2023, December 1). C.A.R.E (computer science, artificial intelligence, robotics, and emerging technologies) @ Purdue [Conference presentation]. Trustworthy AI Lab for Education Summit, Notre Dame, IN, United States. https://lucyinstitute.nd.edu/wp-content/uploads/2023/11/TrustworthyAI_Program_2023_v2.pdf

Yu, W. (2023, April 13-16). Investigating the barriers to introducing Artificial Intelligence (AI) education for elementary school teachers in South Korea [Conference presentation]. 2023 AERA Annual Meeting, Chicago, IL, United States. https://www.aera.net/Events-Meetings

Cetintas, S., Si, L., Xin, Y. P., & Tzur, R. (2013, October). Probabilistic latent class models for predicting student performance. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (pp. 1513-1516). ACM

Cetintas, S, Si, L., Xin, Y. P., Zhang, D., Park, J. Y. & Tzur, R. (2010). A Joint Probabilistic Classification Model of Relevant and Irrelevant Sentences in Mathematical Word Problems. Journal of Educational Data Mining, 2(1), 83-101.

Cetintas, S., Si, L., Xin, Y. P., and Hord, C. (2010). Automatic Detection of Off-Task Behaviors in Intelligent Tutoring Systems with Machine Learning Techniques. IEEE Transactions on Learning Technologies, 3( 3), 228-236.

Cetintas, S., Si, L., Xin, Y. P., Hord, C. (2010). Predicting Correctness of Problem Solving in ITS with a Temporal Collaborative Filtering Approach. In Vincent Aleven, Judy Kay, Jack Mostow, editors, Intelligent Tutoring Systems, 10th International Conference, ITS 2010, Pittsburgh, PA, USA, Proceedings, Part I. Volume 6094 of Lecture Notes in Computer Science, pages 15-24, Springer.

Cetintas, S., Si, L., Xin, Y. P. and Hord, C. (2009). Predicting correctness of problem solving from low-level log data in intelligent tutoring systems. In Proceedings of the 2nd International Conference on Educational Data Mining (EDM)

Cetintas, S., Si, L., Xin, Y. P., Hord, C. & Zhang, D. (2009). Learning to identify students’ off-task behavior in intelligent tutoring systems. In V. Dimitrova, R. Mizoguchi, B. du Boulay, A. Graesser (Eds.), Proceedings of the 14th International Conference on Artificial Intelligence in Education (pp. 701-¬703). Amsterdam, Netherlands: IOS Press.

Cetintas, S., Si, Luo., Xin, Y. P., Zhang, D., Park, J. (2009). Automatic text classification of mathematical word problems. In Proceedings of the 22nd International FLAIRS Conference (pp. 27-32). AAAI Press.

Jiang, Y., Xie, L., Lin, G., & Mo, F. (2024). Widen the debate: What is the academic community’s perception on ChatGPT?. Education and Information Technologies, 1-20. https://doi.org/10.1007/s10639-024-12677-0

Xin, Y. P., Tzur, Si, L. Hord, C., Liu, J., Park, J. Y. (2017). An intelligent tutor-assisted math problem-solving intervention program for students with learning difficulties. Learning Disability Quarterly, 40(1), 4-16. doi:10.1177/0731948716648740

Ozen, Z. (2024). Developing a first-level gifted identification tool: A machine learning application. Dissertation Chair: Nielsen Pereira

Karakis, N. (2021). Predictors of early postsecondary stem persistence of high-achieving students: an explanatory study using machine learning techniques. Dissertation Chair: Nielsen Pereira