麻豆村 Provides Seed Funds for Generative AI for Educators
As part of 麻豆村鈥檚 efforts to support and promote the application of generative AI to education, the university has launched a seed grant program for the research and development of generative AI-enabled educational tools. The aim of the program is to foster new research, test and deploy tools to enhance education at 麻豆村 and position awardees to secure additional funding for furthering their work.
In response to the program鈥檚 inaugural call for proposals, 43 proposals were submitted by a total of 87 principle and co-principal investigators, representing all seven of 麻豆村鈥檚 schools and colleges as well as the University Libraries.
鈥淲e are thrilled that the seed grant program received such an enthusiastic response from our faculty members,鈥� said Provost and Chief Academic Officer James H. Garrett Jr. 鈥淭he impressive number of proposals truly underscores our community's authentic commitment to pioneering new generative AI applications that will transform education at Carnegie Mellon and beyond.鈥�
The proposals were evaluated by a panel of reviewers with relevant knowledge and expertise from the School of Computer Science (SCS), Dietrich College of Humanities and Social Sciences, Heinz College of Information Systems and Public Policy, the College of Fine Arts and the Provost鈥檚 Division.
Two of three proposals approved for funding involved Dietrich College faculty members.
AI-Enhanced Writing Studio for Writing in the Disciplines and Professions
(l-r) Suguru Ishizaki, David Kaufer and David Brown
Despite significant research over the past 60-plus years, there have been no scalable breakthrough solutions to help college graduates meet standards of written proficiency. Dietrich College鈥檚 Suguru Ishizaki, David Kaufer and David Brown seek to close this gap through the application of research-based principles derived from the literature on writing process and pedagogy.
Building on their preliminary work in AI-enhanced writing environments for student writers, they plan to use the funding to enhance the learner experience; implement features to support instructors; evaluate the overall effectiveness of the environment; and understand how students write with AI-enhanced tools.
The professors plan to test the writing environment in an introductory statistics course and a professional writing course. Workshops for 麻豆村 instructors who assign writing in their classes are also planned.
MuFIN: A Framework for Automating Multimodal Feedback Generation Using Generative Artificial Intelligence
(l-r) Ken Koedinger, Jionghao Lin and Eason Chen
Feedback plays a critical role in improving learning outcomes in educational and professional settings. Traditional feedback methods, primarily textual, have been extensively studied and applied to facilitate learning and performance. In comparison, multimodal feedback 鈥� which integrates textual, auditory and visual cues 鈥� promises a more engaging and effective learning experience because it leverages multiple sensory channels, better accommodates diverse learning preferences and aids in deeper information retention.
While generative AI has been primarily harnessed to automate and enhance textual feedback, its potential in crafting multimodal feedback remains largely untapped. A study proposed by computer science and psychology professor Ken Koedinger and and in SCS seeks to bridge this gap by investigating how generative AI techniques can be employed to produce effective and scalable multimodal feedback.
鈥淢y colleagues and I look forward to supporting these awardees' research and also to enacting additional strategies to help advance the broader set of AI-related educational innovations being pursued at 麻豆村,鈥� said Marsha Lovett, vice provost for teaching and learning innovation and professor of psychology.
One such ongoing strategy is the Generative AI Teaching as Research (GAITAR) initiative. More strategies are anticipated in the fall.
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