ASU-GSV Summit Summary April 12th - 15th, 2026 - San Diego, CA RECAP

I recently attended the ASU+GSV Summit, where I had the opportunity to connect with a wide range of EdTech partners, innovators, and fellow educators who are actively shaping the future of AI in education. The energy around artificial intelligence—especially its practical application in teaching, learning, and student success—was both inspiring and validating.

One of the most valuable aspects of the experience was building new relationships with educators and solution providers who are deeply engaged in rethinking how we prepare students for an AI-driven world. These conversations reinforced the importance of moving beyond theory and into real, applied use cases that directly impact student outcomes.

If you weren’t able to attend, I highly recommend catching up on key sessions and discussions from the summit here: click here

Below are some of my key takeaways:

Key Themes and Takeaways – Day 1

Day 1 centered on the role of leadership, policy, and system-level change in education. A major theme was the shift from focusing on access to emphasizing outcomes. While California continues to serve millions of students, the discussion highlighted that enrollment alone is no longer a sufficient measure of success. Completion, workforce alignment, and economic mobility are becoming the new benchmarks.

Speakers emphasized that the solutions to many education challenges are already known, but implementation remains difficult due to political pressures, institutional inertia, and complex governance structures. Effective leadership requires prioritizing long-term student success over short-term popularity.

There was also a strong focus on reconnecting education to opportunity. Students and families are increasingly questioning the value of education, especially in relation to employment and cost of living. This has created urgency around career-connected pathways, skills-based learning, and stronger partnerships between education and industry.

• Shift from access to outcomes
• Leadership and policy are the primary barriers
• Stronger connection between education and careers

Actions and Next Steps
Institutions should begin by redefining success metrics. Move beyond enrollment and access, and instead track completion, job placement, and wage outcomes. This requires closer collaboration with institutional research teams and, possibly, new data dashboards that connect education to workforce outcomes.

There is also a need to strengthen partnerships with industry. Programs should be reviewed and updated to ensure they align with current labor market needs. Advisory boards should become more active and integrated into curriculum decisions, rather than merely symbolic.

At the leadership level, the next step is to identify one or two high-impact initiatives and scale them. Pilot programs are no longer sufficient. Leaders should focus on removing internal barriers—policy, scheduling, funding structures—that prevent innovation from expanding.

• Redefine metrics to focus on outcomes and economic mobility
• Strengthen and operationalize industry partnerships
• Move at least one innovation from pilot to scale

Key Themes and Takeaways – Day 2

Day 2 focused heavily on the impact of artificial intelligence on teaching, learning, and workforce preparation. A key takeaway was that AI is not the disruption itself, but rather a force exposing long-standing weaknesses in traditional education models. Systems built around content delivery and standardized outputs are increasingly misaligned with how students learn and how work is evolving.

There was a clear shift toward applied, experience-based learning. The most effective models emphasized practice, interaction, and real-world problem solving rather than passive consumption of information. AI tools are accelerating this shift by enabling simulation, feedback, and personalized learning at scale.

At the same time, concerns were raised about over-reliance on AI. Students are already using AI tools in unstructured ways, often bypassing institutional controls. This creates risks around reduced critical thinking, diminished effort, and dependency. The need for AI literacy—beyond simple tool use—was emphasized, including understanding limitations, ethics, and responsible application.

• AI as an accelerator of change, not the root problem
• Movement from content to applied learning and practice
• Growing need for AI literacy and critical thinking

Actions and Next Steps
Faculty and departments should begin redesigning assignments to be AI-aware. This means creating work that requires application, reflection, and real-world context—tasks that cannot be easily completed with basic AI. Oral presentations, project-based work, and iterative assignments should become more common.

Institutions should also implement AI literacy across disciplines. This goes beyond teaching tools; it includes helping students understand how AI works, where it fails, and how to use it responsibly. Short modules or embedded lessons across courses can begin this process quickly.

Another key step is to provide faculty support. Workshops, playbooks, and shared examples of AI-integrated teaching should be developed to help instructors work in isolation. The goal is to shift from experimentation to coordinated adoption.

• Redesign assignments to be AI-aware and application-based
• Introduce AI literacy across courses and programs
• Provide structured support and training for faculty

Key Themes and Takeaways – Day 3

Day 3 explored the future of learning models, student engagement, and the skills required in an AI-driven world. A major theme was the distinction between different types of learners, particularly the contrast between achievement-focused students and those who demonstrate curiosity, adaptability, and resilience. The latter group is better aligned with future workforce demands.

The concept of “structured friction” emerged as an important idea. Students need opportunities to engage in debate, encounter opposing viewpoints, and challenge their own assumptions. Rather than avoiding discomfort, effective learning environments should intentionally incorporate it to build critical thinking and intellectual humility.

There was also a strong emphasis on redesigning education systems to support exploration, agency, and interdisciplinary learning. AI can play a role in this transformation if used intentionally, but simply layering technology onto existing models will not produce meaningful change.

• Importance of curiosity, adaptability, and student agency
• Value of structured disagreement and critical thinking
• Need for system redesign, not incremental change

Actions and Next Steps
Programs should intentionally build opportunities for structured debate and critical thinking. This can include requiring students to argue opposing viewpoints, critique AI-generated responses, or analyze multiple perspectives on a topic. The goal is to develop intellectual flexibility and resilience.

There is also a need to redesign learning experiences to promote student agency. Assignments should allow for choice, creativity, and real-world relevance. Interdisciplinary projects can help students see connections across fields and apply knowledge in meaningful ways.

Finally, institutions should consider creating student-led AI or innovation councils. These groups can provide feedback on assignments, test new tools, and help guide responsible AI use from a student perspective. This not only improves implementation but also builds leadership and engagement.

• Integrate structured debate and critical thinking into coursework
• Design learning experiences that promote agency and exploration
• Establish student-led groups to guide AI and innovation efforts

Element451 Offers strong Agentic Tools Higher Education

We met with leadership from Element451, one of the most promising EdTech partners advancing AI and agentic AI solutions for the California Community Colleges system. The session provided an opportunity to explore platform capabilities, ask strategic implementation questions, and identify scalable best practices. Key recommendations included establishing a California Community Colleges user group to meet monthly or bi-monthly via Zoom, creating a collaborative space for sharing insights, use cases, and innovations. Noteably, California Community Colleges already have access to preferred pricing through CollegeBuys, which lowers barriers to entry and supports broader systemwide implementation.

APPLE Showcases best use practices and Education Partners

Apple and San Diego State University held a session to describe how APPLE partners with higher education to provide time and talent to advance initiatives such as the ZIP Incubator. By partnering with Apple on initiatives, they provide additional knowledge, training, and access to resources to help ensure students connect with the best case examples and resources to implement their projects and startup businesses.

After-Hours Observations on AI from EdTech Partner Sessions

During after-hours sessions at the ASU+GSV Summit, including insights from Mark D. Milliron of National University and Chris Phillips of Google, several key observations about AI in education emerged: first, a reframing of artificial intelligence as “collective intelligence,” positioning AI as a collaborative partner that may reduce resistance and normalize its role in idea development; second, a recognition that AI use exists on a spectrum rather than a binary, with the most meaningful opportunities occurring when faculty intentionally design assignments that incorporate varying levels of AI engagement; and third, a clear need for expanded faculty training, as many educators are still exploring how to effectively use these tools, with efforts like Google’s forthcoming “snackable and stackable” professional development modules signaling a growing emphasis on accessible, ongoing learning to support thoughtful integration of AI into teaching and course design. AI Professional from Google/Coursera is a good place to start for many.