Ask questions grounded in real situations
Observe and interview customers and employees to understand their difficulties. Separate facts from assumptions, then judge where AI could help.
Design Thinking × AI
Put people at the heart of AI transformation.
Start with real needs. Use AI to explore possibilities. Turn shared ideas into prototypes people can test.
01 — A direction for transformation
AI transformation starts with people.
Adopting tools is the beginning. Transformation changes employee workflows, customer experiences and organizational decisions.
Which problems deserve AI? Where does human judgment matter? How do new ways of working earn trust? Design thinking brings these questions into research, co-creation and experiments.
Design human–AI collaboration
Understand people’s abilities, needs and concerns. Define AI’s role, human decisions and points of intervention.
Turn change into experiments
Use small prototypes and pilots to test practical outcomes, usability and business value before expanding.
Invite teams into the change
Bring different roles into defining problems and exploring solutions to build shared understanding through practice.
02 — The design thinking mindset
Expand your thinking. Keep your judgment.
Practice curiosity, empathy and experimentation through specific actions. AI helps organize and expand thinking; people gather evidence, make choices and reflect.
Three practices for learning with AI
Observe, then ask
Record what you notice before asking AI to help you investigate.
Think, then generate
Form your own ideas before using AI to expand the possibilities.
Try, then judge
Test with real participants and explain your conclusions with evidence.
A deliberate learning rhythm
- Take time to understand
- Expand possibilities quickly
- Take time to decide
Make time to observe and listen, use AI to explore alternatives, then return to real evidence to decide.
Human-centered empathy
Meet people and listen before drawing conclusions. AI can organize authorized research records; check its summaries against what participants actually said.
Curiosity and openness
Write down what you do not know. Ask AI for alternative explanations and counterexamples, then investigate them rather than treating suggestions as facts.
Reframing the problem
Compare how different people experience the situation. Use AI to suggest other ways to frame the challenge; the team chooses a focus supported by evidence.
Comfort with uncertainty
Keep a visible list of facts, assumptions and open questions. AI can help organize the list; people decide which uncertainty to investigate next.
Experimentation and action
State what you want to learn before making a prototype. Use AI to draft it, then observe real people trying it and record what happens.
Collaboration and co-creation
Invite different roles to contribute and retain disagreements. AI can summarize perspectives; participants check the summary and make decisions together.
Reflection and iteration
Compare expectations with observations. AI can highlight differences; explain which evidence changed your judgment and how the next version responds.
AI can simulate perspectives. Real empathy comes from contact with people. AI-generated insights and suggestions need evidence and human judgment.
03 — Six stages, continuous learning
What do we do ourselves? Where can AI help?
Begin by understanding the question. Gather evidence, explore options and test ideas. Each stage makes human responsibility and AI’s contribution clear.
Pose / understand the problem
Your responsibilityObserve a situation, record your questions and separate facts from assumptions.
With AIHelp formulate follow-up questions, unpack assumptions and identify gaps.
Keep in mindBegin with your own observations before asking AI for solutions.
Empathize with people
Your responsibilityObserve real situations, interview people and listen carefully.
With AIRefine interview questions and organize records shared with permission.
Keep in mindSimulated users cannot replace evidence from real people.
Synthesize / define the problem
Your responsibilityCompare evidence, explain differences and choose the research focus.
With AICluster information, suggest alternative explanations and check reasoning.
Keep in mindPeople remain responsible for the final problem definition.
Explore ideas
Your responsibilityThink independently first, then discuss options and explain your choices.
With AIExpand directions, offer analogies and explore different constraints.
Keep in mindAgree on selection criteria; do not delegate the decision to AI.
Make a prototype
Your responsibilityDecide what to test and how much detail the prototype needs.
With AIDraft storyboards, scripts, interfaces or working demonstrations.
Keep in mindBuild enough to test the question; visual polish is secondary.
Test / iterate
Your responsibilityInvite real participants, observe behavior and decide what to change.
With AIOrganize feedback, compare outcomes and identify patterns.
Keep in mindConclusions need real evidence; AI cannot substitute for testing.
Testing can change the question. Return to research or problem framing whenever the evidence calls for it.
Explore each stage: tasks, outputs and human judgment
Pose / understand the problem
Explain what you have noticed and what you do not yet know. Use AI to challenge your initial framing, then decide what to investigate.
Learning evidenceInitial question, assumptions and a research plan.
Human judgmentBegin with your own observations before asking AI for solutions.
Empathize with people
Prepare open questions, obtain consent and document people’s own words. Check AI summaries against the original records and seek out different experiences.
Learning evidenceField notes, interview records and follow-up questions.
Human judgmentSimulated users cannot replace evidence from real people.
Synthesize / define the problem
Compare observations across stakeholders. Distinguish recurring patterns from isolated comments, document uncertainty and explain why this problem deserves attention.
Learning evidenceAn evidence-backed problem statement and unresolved questions.
Human judgmentPeople remain responsible for the final problem definition.
Explore ideas
Generate initial ideas before using AI. Compare cultural or user value, practical constraints and feasibility; record which ideas were rejected and why.
Learning evidenceSeveral concepts, comparison criteria and reasons for selection.
Human judgmentAgree on selection criteria; do not delegate the decision to AI.
Make a prototype
Choose the smallest experience that can test a key assumption. Use AI to help build it, review the content and prepare tasks participants can try.
Learning evidenceA testable prototype, tasks and a feedback plan.
Human judgmentBuild enough to test the question; visual polish is secondary.
Test / iterate
Compare results with your initial assumptions and agreed criteria. Keep contradictory feedback and return to earlier stages when the evidence changes your understanding.
Learning evidenceTest records, changes and the evidence behind them.
Human judgmentConclusions need real evidence; AI cannot substitute for testing.
04 — Co-create the change
Make AI transformation a shared practice.
Bring the people affected by a challenge into the same conversation: employees and customers in an organization, or teachers, students and project partners in a course. AI supports documentation and exploration so the group can spend more time listening, questioning and deciding together.
01 · Questions to investigate
Young participants: what helps someone understand and take part?
Practitioners: which meanings and practices need careful explanation?
02 · A hypothesis to test
Understand, participate, sustain
Could a practitioner-led experience connect cultural meaning with hands-on participation?
AI groups research notes. The team checks evidence, differences and unanswered questions.
03 · Prototype and learn
Heritage experience prototype
Hear the story · reviewed with practitioners
Try the practice · observe participation
Pilot with practitioners, community partners and young participants. Check understanding, experience and operating effort.
Illustrative questions, hypothesis and prototype; not interview findings or completed project results.
See how AI helps before, during and after the workshop
Before: build a shared starting point
Use AI to organize research, interviews and business materials, surface preliminary clues and open questions, and help prepare the agenda and activities. Bring traceable evidence into the room.
During: deepen exploration and dialogue
Cluster notes, summarize discussion, propose alternatives and generate storyboard or prototype sketches. Anonymous input and perspective summaries can help more voices enter the conversation. Participants choose and decide together.
After: move from ideas to experiments
Organize concepts, decision rationale and unresolved questions. Help create experiment plans, responsibilities and follow-up materials so teams know what to validate next and how to measure it.
Workshop outcomes belong to the participants. Check AI’s summaries with the team, retain minority views and unresolved disagreements, and protect personal information in research materials.
05 — From understanding to action
Leave with experiments your team can run.
A focused Design Thinking × AI workshop helps your team turn shared learning into concrete next steps.
- An AI opportunity map Identify problems and potential value across customer and employee journeys.
- Priority challenges and assumptions Agree on what to solve first and what needs validation.
- A human–AI collaboration concept Describe task responsibilities, human intervention and experience requirements.
- A testable prototype Show a new product, service or way of working in a concrete situation.
- A pilot and learning plan Define owners, validation methods and success criteria.
AIDT for higher education
Learn through real projects. Explore the future of higher education in China with AIDT.
AI is changing how knowledge is accessed and redefining the capabilities graduates need. Higher education in China faces a new challenge: helping students understand real needs, ask valuable questions, cross disciplinary boundaries and turn knowledge into actions and outcomes that can be tested.
Artificial Intelligence Design Thinking (AIDT) brings artificial intelligence and design thinking together: human needs set the direction, real projects organize learning, AI expands exploration, and sustained practice builds judgment and creativity.
Strategic partnership
Partnering with the School of Design Thinking, Communication University of China to advance project-driven education.
WorkspaceX and the School of Design Thinking, Communication University of China are strategic partners. Together, we bring design thinking in education, the exploration of CUC AIDT Lab — AI⁺ Future Creativity Lab, and human–AI collaboration into a shared initiative. Our collaboration focuses on courses, faculty development, workshops and real projects to develop an AIDT teaching model that can be practiced, evaluated and continually improved.
Connect classrooms with society and disciplines with meaningful problems. Let teachers and students learn through creating together.
One real problem. A complete learning journey.
An interdisciplinary team can connect design, communication, social research and technology in one project. Teachers guide research quality; project partners contribute their experience and help evaluate the work.
Illustrative project
Bring living heritage into young people’s everyday lives
How might we work with heritage practitioners, communities and young people to design a public cultural service that balances cultural accuracy, participation and operational feasibility?
Research question card
Ask how cultural meaning, participation and practical operation can work together. Record assumptions before proposing a solution.
Fieldwork and interviews
Learn from practitioners, community partners and young people; record consent, sources and differing views.
Stakeholders and insights
Map needs and tensions, then support the chosen problem with research evidence.
Compare alternative services
Compare an exhibition, a guided experience and a digital story using cultural accuracy, participation and operating effort.
An experience-service prototype
Prototype a story, a hands-on activity and the service flow; review cultural content with practitioners.
Pilot feedback and iteration
Observe real participation and understanding. Record partners’ operating effort and explain what the next version changes.
Teaching project example illustrating the AIDT learning process; not a completed university partnership project.
Explore the full learning journey
Pose / understand the problem
Ask how cultural meaning, participation and practical operation can work together. Record assumptions before proposing a solution.
Empathize with people
Learn from practitioners, community partners and young people; record consent, sources and differing views.
Synthesize / define the problem
Map needs and tensions, then support the chosen problem with research evidence.
Explore ideas
Compare an exhibition, a guided experience and a digital story using cultural accuracy, participation and operating effort.
Make a prototype
Prototype a story, a hands-on activity and the service flow; review cultural content with practitioners.
Test / iterate
Observe real participation and understanding. Record partners’ operating effort and explain what the next version changes.
Each stage leaves a traceable learning record: how the problem was understood, why a solution was chosen and how the team changed its judgment all become part of teaching.
Explore teaching mindsets, cooperation and assessment
Build the mindset to face uncertainty in the AI era.
Design thinking gives AI use a direction. Empathy asks who technology affects. Curiosity invites better questions. Openness and collaboration help disciplines develop shared understanding. Experimentation encourages early testing, while reflection helps students recognize bias and take responsibility.
AI can help students form more hypotheses, compare more possibilities and receive timely feedback. Students must verify information, explain their choices and take responsibility for their work. Real interviews, field observation and user feedback remain essential evidence for learning.
A workshop where teachers, students and AI explore together.
Design thinking workshops connect teachers, students, industry partners and real users around a shared challenge and a direction for action.
Before a workshop, AI helps organize background material and research tasks. During it, AI supports documentation, synthesis, new perspectives and rapid prototyping, leaving more time for listening, questioning and creating together. Afterwards, AI helps organize action plans, track experiments and synthesize feedback so classroom ideas can develop through practice.
Teachers guide learning, uphold research quality and facilitate reflection. Teams make the key decisions together.
Start with one course. Build lasting capacity for innovation.
Our collaboration focuses on four directions.
AIDT project courses
Bring real challenges, interdisciplinary collaboration and prototype testing into course design.
Faculty development and co-creation workshops
Help teachers design project tasks, facilitate human–AI collaboration and develop assessment for the AI era.
Industry and social innovation projects
Enable students to explore with real project stakeholders and discover the practical value of their disciplinary knowledge.
Teaching research and reusable cases
Document project processes, learning evidence and teaching reflections to build resources that can be reused and continually improved.
Make learning visible. Ground educational innovation in evidence.
Assess question quality, use of evidence, reasons for selecting a solution, collaboration, iteration and individual reflection alongside the final work. At stage reviews, students explain what they did, where AI helped, which suggestions they rejected and what evidence changed their judgment.
WorkspaceX provides a shared workspace for this journey, connecting project materials, team discussions, AI collaboration, prototypes and assessment feedback so teachers and students can keep projects moving.
Begin an AIDT education initiative together.
Start with a real challenge, a project course or a teacher–student co-creation workshop to explore learning for the AI era in Chinese higher education.
Start with a meaningful challenge
Build shared understanding. Validate real change.
Invite your team to explore AI’s possibilities. Let design thinking guide the direction, AI strengthen insight and action, and real human needs shape every step.
Design a co-creation workshop around your team’s goals. Email us at hello@boardx.us.