Danielle R. Thomas
I am a Systems Scientist/Faculty within the Human-Computer Interaction Institute at Carnegie Mellon University. Concurrently, I am the Research Lead at PLUS—Personalized Learning Squared, a research project led by Prof. Ken Koedinger at CMU, in collaboration with Carnegie Learning, Inc. and Stanford University.
My research interests focus on improving student learning outcomes and equitizing educational access through 1) the advancement of AI in education, 2) the development of hybrid human-AI tutoring systems, and 3) understanding how inequity impacts students' learning.
As a former middle school teacher, school admin, and teacher educator, my first-hand experiences fuel the mission of the PLUS project of doubling math learning for 10,000 students by 2026!
Check out my CV for information about my experiences, education, and notable publications. Over the past year I have first-authored papers in conference proceedings, such as Artificial Intelligence in Education (AIED), Learning Analytics and Knowledge (LAK), and the International Journal of STEM Education.
What's happening now...
Hot off the press...
In this systematic review, we determine the average STEM student outperforms ~70% of their peers. Most notably, underrepresented minority students benefit given one caveat—they must be given the opportunity. [Journal article link]
Danielle R. Thomas & Karen H. Larwin
International Journal of STEM Education (2023)
This workshop highlights the challenges and opportunities of AI-in-the-loop math tutoring and encourages discourse in the AIED community. Access papers and presentations here.
Vincent Aleven, Richard Baraniuk, Emma Brunskill, Scott Crossley, Dora Demszky, Stephen Fancsali, Shivang Gupta, Kenneth R. Koedinger, Chris Piech, Steve Ritter, Danielle R. Thomas, Simon Woodhead, Wanli Xing
AIED2023: 24th Artificial Intelligence in Education Conference (2023)
We introduce Personalized Learning Squared (PLUS), a human-AI tutoring platform designed to improve tutoring efficiency. PLUS leverages student-facing AI-powered math software and a tutor-facing personalized dashboard to provide the right support, to the right student, and at the right time.
Danielle R. Thomas, Shivang Gupta, Erin Katz, Cindy Tipper, Kenneth R. Koedinger
in 16th Annual Learning Ideas Conference (2023)
We introduce a method of providing explanatory feedback to human tutors on their responses to open-ended questions leveraging LLMs using named entity recognition.
Jionghao Lin, Danielle R. Thomas, Feifei Han, Shivang Gupta, Wei Tan, Ngoc Dang Nguyen, Kenneth R. Koedinger
Workshop at 24th Artificial Intelligence in Education Conference (2023)
Towards the Future of AI-Augmented Human Tutoring in Math Learning
We compare the performance of humans and GPT-4 in identifying criteria of praise by tutors to students. GPT-4 performs moderately well is some areas but underperforms in recognizing sincerity and authenicity- not surprising, yet paves the way for future work.
Dollaya Hirunyasiri, Danielle R. Thomas, Jionghao Lin, Kenneth R. Koedinger, Vincent Aleven
Workshop at 24th Artificial Intelligence in Education Conference (2023) Towards the Future of AI-Augmented Human Tutoring in Math Learning
We introduce an AI-based method of autograding online tutor lessons. Comparing two methods of training set creation using learnersourced tutor responses and by prompting ChatGPT. Our findings show a constructive use of ChatGPT for pedagogical purposes that is not without limitations.
Danielle R. Thomas, Shivang Gupta, Kenneth R. Koedinger
AIED2023: 24th Artificial Intelligence in Education Conference (2023)
We show tutors perform ~20% better from pretest to posttest on our short scenario-based lessons similar to situational judgement tests. How would you respond to a student who has just made a math error?
Danielle R. Thomas, Xinyu Yang, Shivang Gupta, Adetunji Adeniran, Elizabeth McLaughlin, Kenneth R. Koedinger
LAK2023: 13th International Learning Analytics & Knowledge Conference (2023)
Comparing the achievement of 70 students participating in a hybrid tutoring program compared to a matched control, we found the learning gain among participating students was nearly double that of students not participating.
Danielle R. Chine, Cassandra Brentley, Carmen Thomas-Browne, J. Elizabeth Richey, Abdulmenaf Gul,... Kenneth R. Koedinger
AIED2023: 23rd Artificial Intelligence in Education Conference (2022)
