Thematic Synthesis
Thematic analysis of Phases 2–4 revealed three themes: Culture, Ethics, and Public Trust; Capacity, Infrastructure, and Policy; and Strategic Readiness and Alignment. However, because the elements of Strategic Readiness and Alignment were already fully contained within the other two, we consolidated it moving forward and present the findings under the two overarching themes that most clearly reflect participants’ priorities. This consolidation does not remove this content, rather, it redistributes those elements under the two broader themes where they more logically belong. These themes synthesize hundreds of individual responses gathered through focus groups, prioritization activities, and implementation discussions, forming a comprehensive view of where Extension and agInnovation leaders believe coordinated action is most urgently needed.
Theme 1: Culture, Ethics, and Public Trust
Leaders across the Land-grant system consistently emphasized that AI adoption must remain human-centric, grounded in ethical principles, transparent practice, and public accountability. Participants warned that technical progress without safeguards could erode both credibility and community confidence.
Subtheme 1. Ethics and Human-Centrism The most widely discussed imperative was to ensure that AI strengthens rather than replaces human expertise. Participants proposed the development of a formal “human-centric” code of conduct led jointly by Extension and agInnovation Directors and Deans. This policy framework would codify expectations for human oversight in all AI-supported research, teaching, and outreach activities. Such a framework would serve as a reference point for how AI tools are used, evaluated, and communicated, ensuring that institutional values and community trust remain central to technological innovation.
Subtheme 2. Attribution and Transparency Leaders identified a strong need for consistent, verifiable attribution of AI contributions in publications and outputs. Respondents recommended standardized attribution guidelines that clearly distinguish where and how AI was used, differentiating between research papers and Extension materials. They also urged expansion of open-access publishing so AI systems can train on transparent, peer-reviewed data. This aligns with federal open-access mandates and reinforces integrity by making AI-assisted work traceable and auditable.
Subtheme 3. Risk Prevention Participants cautioned that unchecked AI adoption could inadvertently displace critical thinking and essential human skills. To prevent this, institutions should frame AI explicitly as a tool that augments human capacity. Continuous professional development and reflective training were identified as the best safeguards against dependence on automated outputs.
Subtheme 4. Public Trust Trust was recognized as the ultimate determinant of success. Leaders called for systematic efforts to measure stakeholder trust and to design AI applications that respect the comfort levels and expectations of end users. Transparent communication, demonstrated accountability, and community involvement in AI design were cited as key to sustaining public confidence in both Research and Extension programs.
Theme 2: Capacity, Infrastructure, and Policy
The second theme addresses the structural conditions necessary to scale AI responsibly: a capable workforce, collaborative frameworks, and coordinated policy governance. Participants repeatedly emphasized that without shared infrastructure and systematic training, adoption would remain fragmented and unsustainable.
Subtheme 1. AI Workforce Readiness
Training emerged as the single most dominant priority. Leaders proposed a national, tiered AI training program led by Extension, featuring beginner-to-advanced pathways for faculty, researchers, agents, and community audiences.
Key components include:
- foundational AI literacy and ethics;
- advanced modules such as prompt engineering and data validation;
- “train-the-trainer” models using computer-science expertise; and
- clear certification pathways verifying competency and responsible use.
Participants stressed that training must demonstrate tangible efficiency gains and be embedded within institutional strategic plans to reach beyond early adopters.
Subtheme 2. Collaborative Framework
No single university can meet AI demands alone. Leaders urged the creation of system-wide alliances across the Extension Committee on Organization and Policy, and other partners to coordinate resources and prevent duplication. Examples included joint funding proposals, shared centers of excellence, and cross-state learning communities. The Extension Foundation was identified as a potential platform for distributing resources, webinars, and best-practice repositories that benefit all Land-grant types (1862, 1890, and 1994). Summary
Subtheme 3. Policies and Best Practices
Governance must evolve in parallel with technology. Participants called for institution-wide policies clarifying acceptable AI use in research, teaching, and Extension outputs.
Recommendations included:
- creation of multidisciplinary task forces involving IT, compliance, communications, and academic leadership;
- standing national committees through APLU or scientific societies to update policies as technology evolves; and
- guidance framed around “dos and don’ts” emphasizing risk mitigation rather than rigid restriction.
Subtheme 4. Infrastructure, Resource Allocation, and Funding
Sustainable AI integration depends on access to physical and financial resources. Leaders underscored the need for shared data repositories, compute capacity, and broadband access, as well as new funding mechanisms to offset training and infrastructure costs. They highlighted public-private partnerships and coordinated grant strategies as essential for building durable AI infrastructure across all institutions, including those with limited internal capacity.
Summary
Across both themes, participants voiced a consistent conclusion: policy must lead technology. Human oversight, ethical governance, and coordinated capacity building are prerequisites for AI to achieve its full potential in research and community engagement. AI should not be treated as an isolated innovation but as a system-wide transformation requiring collaboration, investment, and trust.
