You show them.

HANDOLabs rewards humans with stock tokens for training robots through demonstration. By distributing training to people via video-recorded “missions,” HANDO can collect high-fidelity data on how people complete daily, seemingly mundane tasks. The very tasks robots might someday do to free humans for other, less repetitive work.

A colleague emailed me today about how to have productive conversations about AI with people who strongly oppose using AI tools. Below is part of my response. (Transparency: I told the recipient I would add this to a Field Notes post on my site).

I am an optimist like you, but I am also a skeptic. I think it is ideal for people to be skeptical of AI tools (and, well, just about everything). I have found that when I tell people I am skeptical of AI, even as Director of the Miami Center for AI, it encourages them. Skeptics haven’t “given up” or made up their minds as cynics would. The skeptic tests out any information or idea they receive for its validity. The skeptic is open to possibilities. They haven’t made up their mind entirely, but they also don’t believe everything they’re told. The skeptic thinks critically.

Our society needs critical thinkers and feelers—deep thinkers who are open to discussion and want to understand (and can change their minds if they encounter ideas that are more accurate or disprove their assumptions). I think that’s wonderfully optimistic and includes people from all perspectives in the conversation as we make meaning together. But we have to be open—to listen, to understand, and to change our minds, or accept that there are many perspectives on complex matters (and AI just happens to be one of them).

That was a lot. Sorry. Maybe I got off track. But I hope that encouraging faculty, students, and alumni to question what it means to be an artist and human during rapid technological innovation, and to develop interventions that the Earth and beings on it need to thrive, sparks folx to get excited about work in an AI era that’s sorely needed.

I participated in a panel convened by Chris Prokes, Director of the AI Excellence Institute at Sinclair Community College, for the Dayton AI Day Conference, organized by Technology First. The session was a rich opportunity to share how the University of Dayton, Sinclair Community College, Wright State University, and Cedarville University approach teaching and learning with AI, and to share how we at Miami University are doing this work. Chris’s questions below sparked rich discoveries.

1. When you hear the phrase “AI-ready graduate,” what does that mean at your institution?
2. What is one concrete example of how your institution is helping students build AI literacy right now?
3. How are you helping students move beyond simply using AI tools toward using them with judgment, accuracy, ethics, and professional responsibility?
4. Where are you seeing the most natural fit for AI in student learning: general education, specific majors, career preparation, student support, or somewhere else?
5. What are faculty struggling with most as they adapt to AI, and what kinds of support or professional development seem to be working?
6. How are your institutions addressing academic integrity while still encouraging students to learn how to use AI appropriately?
7. What are you hearing from employers about the AI skills or habits they expect from new graduates?
8. Looking across the Southwest Ohio region, what is one thing higher education institutions could do together to better prepare students for an AI-enabled workforce?

Takeaways

Fostering graduates’ critical thinking skills and adaptability were significant throughlines during the session—participants shared that AI literacy is crucial for graduates to determine when to use AI and when not to, and to assess its outputs for reliable results. Some faculty at participating institutions are facilitating this through custom AI tools that they built to supplement in-person teaching. Participants shared that relationships between instructors and students cannot be replaced by AI tools. AI can supplement learning in some exciting ways, but human interaction can produce significant learning gains.

We all agreed that transparency when using AI is a must, whether in teaching and learning or in the workplace. Each institution also empowers faculty to set their own AI policies to tailor acceptable use to each course’s goals. I was particularly interested in the idea that faculty at some institutions are developing a base of departmental AI policies to reduce wide variance across courses that can often be very frustrating for students to navigate. All institutions shared that they have integrated modules, courses, or components that introduce students to AI tools and ethical AI use. During the session, I was excited to share more about Miami’s AI in the Majors initiative, where faculty in each department are integrating AI into one of their degree programs’ learning outcomes (props, Miami!).

I look forward to more opportunities to learn with institutions in our region as we navigate teaching and learning with AI and how to prepare graduates for success. It’s a constantly shifting landscape—but that’s what makes this work exhilarating!

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?