In mid-October, the Computing Community Consortium (CCC) will convene a visioning workshop designed to define and recommend improvements to the core components of the artificial intelligence research ecosystem (AIRE). The AIRE workshop — led by Manish Parashar (University of Utah), Vandana Janeja (University of Maryland Baltimore County), Abhishek Bhattacharjee (Princeton University), Edith Elkind (Northwestern University), David Jensen (University of Massachusetts Amherst), Jofish Kaye (Inflection AI & CRA-I Council), William Regli (University of Maryland), and Michela Taufer (University of Tennessee Knoxville) — brings together research community leaders to examine how the structures that support AI research, from peer review and publishing to funding and partnerships, can keep pace with a rapidly changing field.
The workshop will take place October 13-15, 2026, at the Chaminade Resort in Santa Cruz, CA. Over two and a half days, participants will take part in an intensive, laptop-free program focused on consensus-building, working together to define strategic directions and priorities for the AI research ecosystem based on community input.
Understanding the Stressors on AI Research
AI technologies are advancing quickly, and deployed AI systems have become major drivers of economic and social change. Yet the AI research ecosystem itself has also been evolving rapidly.
“In a world increasingly focused on adoption of AI with its myriad uses, potential misuses and concerns around AI security, there is a renewed need to consider all the stressors on the AI Research Ecosystem so that much needed and well thought out AI research can thrive,” says workshop co-chair Vandana Janeja, Professor of Informatics Systems at the University of Maryland Baltimore County. “I am hopeful that this workshop will lead to recommendations on how best to support this ecosystem into the next several years.”
The workshop’s specific focus areas emerged from months of community input. Through a Consider.it forum, researchers could submit stressors they saw affecting the AI research ecosystem and respond to those raised by others. In parallel, the AIRE Task Force convened virtual roundtables by sector, bringing together perspectives from government (including DoE, NSF, NIH, and NASA), industry (including Microsoft Research), professional societies (including AAAI), and academia (including Carnegie Mellon University and Brown University). The findings from both efforts led to four pillars, which participants will explore in depth at the workshop:
- Publication and peer review: Conference reviewing systems, journal publishing systems, and pre-publication distribution
- Infrastructure and resources: The computing, data, and funding resources, including Federal funding programs, that make AI research possible
- Interdisciplinarity: How AI research connects with and draws on other fields
- Public-private partnerships: Research translation, translational ecosystems, and standard agreements for industry funding of academic research
“While AI offers amazing opportunities for the academic AI research community, it is also straining the academic research ecosystem, including publication and review structures, academic reward structures, research infrastructure, and partnerships,” says workshop co-chair Manish Parashar, Chief AI Officer at the University of Utah. “I look forward to the AIRE visioning workshop, where we will come together to understand these stressors, brainstorm mitigation strategies, and develop an action plan to restore a vibrant AI research ecosystem.”
Learn More and Stay Up-to-Date
The final sessions of the workshop will focus on synthesizing these discussions into recommendations, ultimately informing a CCC report expected in Spring 2027.
CCC looks forward to welcoming participants to the workshop. To stay informed about updates and outcomes from the AIRE workshop, follow the CCC Blog and LinkedIn Spotlight Page. We also recommend checking out Janeja and Parashar’s recent Medium post, “Envisioning the Future AI Research Ecosystem” for more information about their initial findings from the field.
This material is based upon work supported by the U.S. National Science Foundation (NSF) under Award No. 2300842 and 2619366. These awards support the Computing Community Consortium (CCC), a programmatic committee of the Computing Research Association (CRA). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
