Why I’m Writing Spatial AI: Bridge the Gaps

The preface to my second book, Spatial AI: Bridge the Gaps — and the reason this site exists.

When I finished writing my first book, Spatial Design: Breaking the 2D Paradigm—a creator’s guide to immersive design—I kept coming back to one question: what’s next? That question became the spark for my second book, Spatial AI: Bridging the Gaps. In it, I explore how Spatial AI, the convergence of spatial computing and artificial intelligence, is fundamentally reshaping how we work, learn, and live. Recently, I was selected as an AI Fellow at the California Community Colleges Chancellor’s Office, where I lead the Spatial AI initiative. My focus is on helping faculty transform their curriculum into immersive learning experiences—bridging theory, practice, and policy at a scale that can truly make a difference.

At the heart of this work is a simple but urgent idea: as AI takes over routine execution, human value shifts toward what makes us distinctly human—creativity, critical thinking, ethical judgment, and empathetic collaboration. Spatial AI: Bridging the Gaps offers a practical roadmap for educators, leaders, and creators to navigate this transformation by building AI literacy and redesigning systems that close critical gaps in skills, access, and innovation. This book addresses a profound shift happening in the workforce: AI is creating what many call a “disappearing middle.” Technology is splitting work into two categories—high-value “architects” who direct and design with AI, and lower-value roles increasingly shaped by automation.

Traditional mid-level positions are fading away. In their place, we’re seeing the emergence of high-agency individuals and hyper-efficient “tiny teams” that leverage powerful, low-cost AI tools, alongside a new management philosophy where humans guide autonomous AI agents rather than managing large teams. Education must respond to this transformation—and it needs to happen now. The California Community Colleges system offers a powerful case study. With over 200 AI-related courses across more than 60 colleges, the system is building an AI-literate workforce at scale. The approach is practical and accessible: offerings prioritize foundational data science as a gateway to more specialized AI and machine learning skills, while maintaining flexible pathways through degree programs, transfer options, and noncredit courses for rapid upskilling.

It’s an agile response to the explosive growth of generative AI and prompt engineering. But building AI-literate students requires more than just adding new courses—it demands a fundamental shift in how we teach. Rather than racing to detect AI-assisted work, we need to move toward AI-aware, AI-resilient assessment. This means creating assignments that cultivate skills AI can’t replicate—like deep process thinking over polished products, metacognitive reflection, and authentic articulation of reasoning. Multi-modal formats like podcasts, videos, and physical artifacts give students ways to showcase their humanity, while context-rooted assignments demand genuine engagement. AI becomes a subject of inquiry itself, analyzed for its limits, biases, and blind spots. The goal isn’t to avoid AI, but to help students use it critically and responsibly.

In my Spatial and XR AI foundation courses—which serve as professional development for CCC faculty—our team takes a comprehensive approach. We explore not just generative AI tools that support Unity-based XR development, but also the KONNECT XR framework. KXR weaves together storyboarding and spatial design principles with ADDIE and Backward Design, enabling us to create learning experiences that feel dynamic rather than static. Throughout, we emphasize preparing students for meaningful, paid work and helping them develop purpose and adaptability in an AI-driven world.

As an AI Fellow leading Spatial AI and XR initiatives, my design process begins with the learner’s experience and real-world applications, then works backward through objectives, activities, and assessments. With agentic AI emerging rapidly and ongoing conversations around AI happening both in Silicon Valley and within the Chancellor’s Office, we constantly ask ourselves: how do we design assignments that stay meaningful in an AI-rich world? The answer includes oral presentations, collaborative projects, and assessments that prioritize reflection, judgment, and process over polished outputs. Faculty become Learning Experience Designers, emphasizing project-based learning, collaboration, and authentic problem-solving.

I also draw inspiration from Reverse Bloom’s Taxonomy, which flips traditional learning on its head. Instead of starting with knowledge and building up, learners begin with a holistic vision of what they want to achieve—the “why” at a strategic and problem-framing level. Then they move to the “what” through planning and coordination, and finally to the “how,” which AI tools increasingly support. This inversion allows learners to work alongside AI while strengthening higher-order thinking and human judgment. In my Unity-based Spatial and XR AI courses, I rarely move through the design process linearly. Instead, I work in iterative loops: prototype, test, reflect, and refine.

This approach lets me stay responsive to learner needs while maintaining the rigor of formal instructional design frameworks. Rubrics are essential to this process, especially in online and project-based learning. They set clear expectations, reduce ambiguity, and promote transparency and fairness. From an instructional perspective, rubrics help maintain alignment with learning objectives and prevent inconsistent grading. When students understand exactly how their work will be evaluated, engagement increases and the quality of work improves—making rubrics both an assessment tool and a learning scaffold.

Active learning is woven throughout everything I do. Learning objects ask students to actively engage—to do, reflect, and apply—through discussion-based activities, storyboard creation, peer feedback in Zoom breakout rooms and polls, building AI teaching assistants in PlayLab, responding to C# development questions, and reimagining public-domain literature or faculty curriculum as Unity-based interactive experiences. Because my work centers on XR and AI-driven learning, I prioritize exploratory, experiential activities where learners make meaningful design decisions rather than following prescriptive steps. Clear instructions and thoughtful scaffolding help learners understand both the “what” and the “why,” and every activity explicitly connects back to Course Learning Objectives to maintain alignment.

Designing lessons this way has made alignment far more visible. When objectives, materials, interactions, and assessments are all mapped side by side, misalignment jumps out immediately. This reinforced for me the importance of treating course mapping as a living, iterative document—not a one-time checkbox. Going forward, I plan to consistently use alignment checklists informed by Reverse Bloom’s Taxonomy, Quality Matters standards, and Universal Design for Learning principles. Peer review and iterative revision will remain essential for catching blind spots and strengthening design. One question keeps guiding my work: how do we balance structure with creative freedom in immersive and AI-supported learning environments?

My goal is to maintain strong alignment and measurable outcomes while leaving room for curiosity, experimentation, and learner agency. This experience has taught me that these goals aren’t in opposition—when thoughtfully designed, structure actually creates the conditions for deeper exploration.