AAAI/ACM SIGAI Innovative AI Education Program
last updated: Edited Nov 13, 2026
Apply by filling out this form by November 24, 2026.
The IAEP Program
The 17th Symposium on Educational Advances in Artificial Intelligence (EAAI), AAAI, and ACM SIGAI are pleased to announce the AAAI/ACM SIGAI Innovative AI Education Program (IAEP) for 2027, sponsored by ACM SIGAI and AAAI. The Innovative AI Education Program (previously named the New and Future AI Educator Award) provides partial travel support for educators (early/mid career lecturers, assistant/associate professors, and other university or secondary school faculty) and future educators (full-time PhD candidates or postdocs at colleges and universities who intend a career in academia) to participate in EAAI-27, co-located with AAAI-27.
EAAI-27 provides a venue for AI education researchers and educators to share and discuss advances in teaching and learning about Artificial Intelligence, including the education and training of AI practitioners and users. Awardees are expected to participate actively in EAAI-27. Applicants should be interested in AI pedagogy, adopting new materials and resources for teaching AI, or becoming involved with the AI education community.
It is not necessary to have a paper accepted to EAAI-27 or be a past participant to apply; the Innovative AI Education Program is intended to include both new and current members of the EAAI community.
Application
As part of the application, potential participants are asked to provide a 400-500 word statement addressing one of the following "blue sky" questions:
- AI has evolved rapidly over the last few years, changing what students need to learn about it. How should AI curricula evolve and adapt to this change?
- How should standard university CS courses or K-12 CS education initiatives evolve to provide all students with AI literacy?
- In a world where students can easily use AI tools to assist with or generate work, how should assessments for AI units or courses be designed to meaningfully support and measure students’ learning?
- How can AI education prepare students to promote the responsible and equitable development and use of AI beyond the classroom?
- How can AI courses and units meaningfully embed ethical issues, such as bias, fairness, privacy, and broader societal and environmental impacts, into their core learning goals and activities, rather than treating ethics as a superficial or “checkbox” add-on?
- How might students’ increasing use of generative AI tools for foundational tasks, such as mathematical reasoning, coding, and academic writing, reshape the way we design and teach AI curricula? How can instructors responsibly implement these changes given the constraints they face?
Potential participants can also choose to answer their own “blue sky” question.
The blue sky response should be framed as if it were a section of a paper on education. Selected participants will have the opportunity to present their ideas on these topics briefly during EAAI. A collection of the blue sky responses will further be published in the ACM SIGAI’s AI Matters magazine.
To apply, fill out this form by November 24, 2026.
Past Awardees
2026
Erfan Farhadi University of Rochester
Firas Moosvi University of British Columbia
Praveen Kumar Guraja Oregon Institute of Technology
Amy Eguchi University of California, San Diego
Avinash Anand Singapore Institute of Technology
2025
Fatima Zahra University of Tennessee Knoxville
Pulkit Verma MIT
Zhonghao Shi University of Southern California
Somosmita Mitra Purdue University
Maura White University of California San Diego
2024
Rachel Lugo The Potomac School: LLMs as Catalysts for Reverse Engineering Learning, A Case for Inquiry in K-12 Education
Justin Stevens Alberta Machine Intelligence Institute: Using Analogies to Make Teaching AI More Accessible
Shira Wein Amherst College: Teaching NLP and Considering the Use of LLMs in CS Coursework
William Agnew University of Washington / Carnegie Mellon University: Co-Creating Knowledge about LLM Uses and Limitations
ACM SIGAI AI Matters article: EAAI-24 Blue Sky Ideas in Artificial Intelligence Education from the AAAI/ACM SIGAI New and Future AI Educator Program
2023
Rajiv Ratn Shah IIIT Delhi: How could we teach AI topics at an early undergraduate or a secondary school level?
Yaman Kumar IIIT-Delhi, State University of New York at Buffalo: Can we use some advances in AI to teach AI?
Alexi Orchard University of Waterloo: Using Critical Design Methods to Teach AI Ethics
Carolyn Jane Anderson Wellesley College: Making AI Education More Interdisciplinary Through Computational Creativity
ACM SIGAI AI Matters article: EAAI-23 Blue Sky Ideas in Artificial Intelligence Education from the AAAI/ACM SIGAI New and Future AI Educator Program
2022
Zhuoyue Lyu University of Toronto: How could we make AI education more interdisciplinary?
Emmanuel Johnson University of Southern California: AI education can be leveraged to promote diversity in the field
David Johnson Uppsala University: Embodied AI Ethics
Henry Chai Carnegie Mellon University: Multidisciplinary Ethics in AI
Daniel Garijo Universidad Polite ́cnica de Madrid: Bridging the gap between AI courses and Open Science
Christopher MacLellan Drexel University: How to make AI more interdisciplinary
ACM SIGAI AI Matters article: EAAI-22 Blue Sky Ideas in Artificial Intelligence Education from the AAAI/ACM SIGAI New and Future AI Educator Program
2020
Ryan Blake Jackson Colorado School of Mines: Early AI Education without Computers
Lisa Zhang University of Toronto: How could/should AI courses incorporate ethics?
Adam Smith UC Santa Cruz: Incorporating ethics into AI curricula / Early-level teaching techniques
Qian Yang Duke University: How can we create a more inclusive AI/AI Education community?
Jiacheng Liu Shanghai Jiao Tong University: How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields?
Rajiv Ratn Shah IIIT Delhi: How can AI topics be taught at an undergrad/secondary level?
Paul Taele Texas A&M University: Teaching AI through Hands-on Intelligent User Interfaces Research
2019
Marion Neumann Washington Univeristy in St. Louis: A First Introduction to Modeling and Learning using the Data Science Workflow
Abhijit Suresh University of Colorado Boulder: Using analogies to introduce AI topics in secondary schools
Giulia Toti University of Houston: Machine Learning for Freshmen
Zhen Bai University of Rochester: Tangible Data: Towards Hands-on AI Education for K-12 Students
Michael Guerzhoy Princeton University: Teaching Machine Learning Across Campus by Centering Models
Pat Virtue Carnegie Mellon University: Connecting AI Concepts to Secondary School Mathematics
2018
Stephanie Rosenthal Chatham University: How to teach AI to early undergraduates?
Ansaf Salleb-Aouissi Columbia University: How could/should AI courses incorporate AI Ethics into the curriculum?
Raghuram Ramanujan Davidson College: How to teach A.I. topics to early undergraduates or secondary school students?
Rushit Sanghrajka University of Utah: How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields?
Zachary Pardos UC Berkeley: How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields?
Second Year Program: Joshua Eckroth Stetson University, Claudia Schulz TU Darmstadt, Tom Williams Colorado School of Mines
2017
Claudia Schulz Imperial College London: Bridging Across Disciplines
Francesco Maurelli Jacobs University Bremen: Student-Centric Discovery
John Lee Antioch University: AI in K-12 Education
Joshua Eckroth Stetson University: The Role of Ethics in AI Education
Mark Crowley University Waterloo : AI Education through Real-World Problems
Richard G. Freedman U. Massachusetts Amherst: Making AI Concepts More Accessible
Rogelio E. Cardona-Rivera North Carolina State University: Rethinking the AI Ethics Education Context
Tiago Machado New York University: Lifelong Kindergarten for AI
Tom Williams Tufts University: Training Students in AI Ethics
ACM SIGAI AI Matters article: Blue sky ideas in artificial intelligence education from the EAAI 2017 new and future AI educator program
Questions may be directed to the current EAAI symposium Co-Chairs or Awards Chairs:
Lisa Zhang (Co-Chair), University of Toronto Mississauga (lczhang@cs.toronto.edu)
Bradford Mott (Co-Chair), North Carolina State University (bwmott@ncsu.edu)
Marion Neumann (Awards Chair), Washington University (m.neumann@wustl.edu)
Rajagopal Venkatesaramani (Awards Chair), Northeastern University (r.venkatesaramani@northeastern.edu)
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