Description of Navigating the Landscapes of Design Research: Finding Your Way

This book provides emerging and established designers and design educators, along with their collaborators, with a means to understand how and why the landscape of design research has come to exist as it does, and how and why its various destinations and pathways are connected.

It is an accessible “guide” for those in and around design who have little to no experience with planning and conducting research to inform their decision-making. Specifically, this book makes the case that an integration of research and design is critical in a world shaped by an increasingly complex and pervasive amalgam of challenges: social, technological, environmental, economic, and political. The need to expand shared understandings of research has also become acute because this landscape is increasingly traversed by those who are seeking evidence-based outcomes to their needs rather than solutions shaped by aesthetic polish, subjective client demands, or simple cost-benefit formulas.
This book will be of interest to scholars and designers working in design studies and design research, as well as to design practitioners who are increasingly called upon to understand and act on data gleaned from evidence-based research. In addition to writings by Professors Gibson and Owens, this text includes chapters from fellow design researchers Helen Armstrong and Mathew Peterson, Leslie Atzmon and Diana Nicholas, Peter Hayward Jones, Cassini Nazir, and Dennis Cheatham.

• Reframing the Marketing Plan with AI as a Planning Partner
  • Free vs. Paid: What’s the Difference?
• Using AI to Build a Stronger Marketing Foundation
  • Activity: Audience & Messaging Depth
  • Activity: Messaging
  • Using AI to Make Smarter Channel Decisions
  • Activity: Channel Fit and Reality Check
  • Activity: Setting Goals for Your Marketing Efforts
  • Activity: Building the Plan
  • Activity: Implementation and Measuring Success
• Final Takeaways

A few slides from this session:

A few slides from the presentation:

Presentation Sections

• Reframing the Business Plan with AI as a Planning Partner
• Free vs. Paid: What’s the Difference?
• Activities: Using AI to Refine Successful Business Plan Details
  • Entrepreneurial Blind Spots
  • The Business Idea Always Sounds Good in Your Mind
  • Activity: Product Reality Check
  • Activity: Customer Clarity
  • Activity: Value Proposition Depth
• Competitive Research
  • Activity: AI for Market Research
• Pricing Strategy
  • Activity: AI as Pricing Scenario Simulator
  • Strategy and Implementation
• Final Takeaways

Below are some slides from the presentation,

Well, the game isn’t big enough unless it scares you a little.

Cmdr. William Riker

I’m not the biggest fan of Star Trek: The Next Generation, but Commander Riker’s words from “Pen Pals,” Season 2, Episode 15, hit the heart of what it feels like to learn something new.

It’s scary.

Any time I take on something new and feel over my head, I’m learning. I am adding experience points: new knowledge, skills, and thinking. Recently, AI has been my game of choice. The tools are constantly changing, and it’s not clear how they will impact us all.

What game are you playing?

The AI Provost Fellows will present a series of short reports on results and progress from multiple AI initiatives at Miami. We’ll begin by sharing findings from the all-campus student and faculty surveys, highlighting key trends from both groups. Then we’ll discuss some projects and initiatives that have happened this year and those coming up in the future.  We’ll share some benchmarking of how Miami’s approach to AI compares with what other institutions are doing.  And we will discuss the new initiative “AI in the Majors,” a faculty-driven program integrating discipline-specific AI literacies in programs at Miami. We will highlight some of the work by the thirteen departments that have participated in the first cohort this spring. We’ll conclude by synthesizing what we’ve learned from the Miami community over the past year and invite the audience to help shape our next steps.

In November 2026, my fellow Miami University AI Provost Fellow, Heidi McKee, and I will have the pleasure of presenting our work in AI at the Lilly Conference on College Teaching Conference. Our session description is below.

It can feel like the rapid evolution of artificial intelligence (AI) calls for a complete reworking of what and how college instructors teach, a feeling compounded by pressures to “meet the moment—and quickly!” But this sense of urgency should not short-circuit best practices for education. This plenary session draws on research across the curriculum to showcase frameworks and strategies where instructors drive the design of classroom, program, and institutional (re)visions in this time of technological transition. The presenters, co-facilitators of Miami University’s “AI in the Majors” initiative, advocate for building from instructors’ collective wisdom, professional expertise, and field practices to craft the futures we want for our students and ourselves as we all adapt to teaching and learning with and about AI.

This session examines the principles of effective AI collaboration through my experience rebuilding a WordPress codebase with Claude over nine months, during which I witnessed the tool evolve from an often-frustrating, sometimes-useful novice to a pro-level, agentic collaborator. I’ll demonstrate the role of expertise in AI collaboration by showing real conversations where my domain knowledge was essential for guiding, evaluating, and correcting AI suggestions. I’ll also share examples of learning through collaboration, including moments when I operated outside my expertise (such as diagnosing and recovering from a security breach) and how existing competencies were crucial to achieving desired results. The session will identify transferable principles that apply across disciplines beyond coding: how expertise shapes better prompts, why productive struggle matters, and what foundational competencies enable useful AI collaboration.

Participants will discover implications for teaching and learning with AI: why students need disciplinary competencies before they can collaborate effectively with AI, and how we can teach AI literacy as an extension of expertise rather than a replacement for learning. Through actual AI conversation examples from both coding and non-coding contexts, the session will reveal collaboration patterns across different types of work, emphasizing that AI tools amplify expertise rather than replace it, with practical takeaways for cultivating this approach in teaching and learning.

Below are a few slides from the presentation.