On September 25, educators, administrators, employers, and workforce partners gathered at San Diego Mesa College for Work Ready in the Age of Artificial Intelligence, a K-16 Regional AI Convening organized by the Border Region Talent Pipeline K-16 Collaborative. The purpose was to examine how AI is changing work and what that means for curriculum, career pathways, and student experiences across high schools, adult education, community colleges, and universities.
I was invited to facilitate two breakout sessions that moved from workforce change to educational response. The first brought together a deliberately cross-sector group. The live engagement data captured 25 connected profiles: five industry representatives, five educators, ten administrators or staff members, and five workforce partners. We discussed how AI is already being used at work, what employers will need from entry-level talent, what an AI-prepared student should know and be able to do, and how industry and education might collaborate more meaningfully. The second session was a smaller conversation with eight participants focused on what those workforce signals require inside education, including changes to curriculum, assessment, faculty support, and implementation.
I am grateful to the organizers for the invitation and to the participants who shared their experiences so openly. Their range of perspectives made the conversations both practical and unusually honest.
I left the convening genuinely encouraged. It was one of the first times I had been in a space like this where the conversation had clearly moved beyond fear. It was not dominated by anti-AI sentiment or limited to plagiarism and cognitive offloading. The room recognized that AI is already reshaping education and work. The conversation had advanced to the harder and more useful questions: How do we integrate it responsibly? What limitations must we address? What do educators and students need for that work to succeed?
The group expressed a clear sense of urgency. AI and workforce expectations are changing quickly, while curriculum, governance, and professional learning systems move much more slowly. That gap is widening. Unless institutions address it, students may graduate into workplaces that have changed faster than their education.
I was also glad to hear strong agreement in the room about what progress should mean. It should not be measured by the number of AI tools an institution provides. It should be measured by evidence of student learning and workforce relevance. Are students developing disciplinary knowledge, sound judgment, responsible AI fluency, and experience solving real-world problems? Strategic collaboration with industry matters because it connects AI literacy to the work graduates will actually encounter.
So, how do we move forward?
Two intertwined barriers stood out to me. The first is fragmentation. As one participant observed, education is a segmented system. School districts and institutions are developing their own approaches to AI, and within them, individual educators are often left to make their own decisions about whether, when, and how to teach it. As a result, students' AI learning can vary widely from one district, program, or classroom to another. Some students may receive a strong foundation, some may encounter a few isolated tools, and others may receive little preparation at all. That makes it difficult to build shared AI literacy or create a coherent pathway from education into the workforce.
The second barrier is capacity. Faculty members are expected to help students develop AI literacy, yet many have not had the opportunity to build their own foundational understanding of generative AI. There is also no clear, shared approach for helping them develop it. One K-12 educator made the cost of implementation especially clear. Educators must invest significant time and effort in building their own AI knowledge, redesigning learning experiences, testing new approaches, and evaluating the results. Protected time, compensation, professional learning, technical access, leadership support, and real decision authority therefore determine whether, and to what extent, AI integration happens in the classroom. Institutions cannot treat this work as one more responsibility that faculty will somehow absorb.
These barriers require two complementary responses. My first view is direct: I believe we are approaching much of faculty development backward. Too much training starts with use cases, prompt templates, sample assignments, and rubrics. Those resources may be useful, but they do not replace understanding how generative AI works, how models are built and trained, what their limitations are, and why those limitations matter (e.g. algorithmic bias, student privacy, data security). They need to understand prompt engineering fundamentals and prompt design strategies. Faculty need access to experts who understand those foundations and can make them clear to people without technical backgrounds. With that knowledge, faculty can integrate AI holistically into their thinking, preparation, teaching, and disciplinary practice rather than borrowing isolated examples that may not fit their work.
The second response addresses fragmentation. Where feasible, institutions should offer a common AI fundamentals course taught by a qualified instructor. Instead of asking every faculty member to independently teach AI literacy, schools could ensure that all students pass through one well-designed foundational experience. Faculty could then build on that shared foundation within their own disciplines rather than having to create it independently in every classroom.
Together, these two investments (foundational professional learning for faculty and a shared fundamentals course for students) could create the infrastructure that meaningful AI integration requires. This is actually one of the reasons I founded Wolff Technologies: to provide that foundational bridge between the technical realities of generative AI and the educators and institutions preparing students to work with it.