Design from the outcome
Clarify what students should understand, produce and defend before selecting any model or tool.
3-DAY FACULTY DEVELOPMENT · PRACTICED FORMAT
Redesign one learning experience.
Leave with something teachable.
An intensive studio for university and vocational-college faculty who want to move beyond isolated AI tools and design responsible, discipline-specific learning experiences.
01
The faculty challenge
AI adoption is not the same as better teaching
The program starts from learning goals, evidence and disciplinary standards. Faculty then decide where AI should support inquiry, practice and feedback—and where human judgment must remain in control.
Connect knowledge and workflow
Turn course materials into a governed knowledge base, assistant or repeatable teaching workflow.
Make responsibility visible
Define disclosure, verification, data and assessment rules so AI supports—not replaces—learning.
Reference case, not a product endorsement
What a university learning assistant can make possible
Tsinghua University’s publicly documented “Qing Xiao Da” illustrates a broader design pattern: AI-supported questions and answers, study tools, resource matching and a longitudinal learning record connected to a university knowledge environment.
Trusted campus knowledge
Institutional information and course support become easier to access through a university-managed knowledge layer.
Learning-process support
Document interpretation, data analysis, reminders and resource matching can support students between classes.
Growth evidence
A record of learning outputs and progress can help students and educators see development over time.
Illustrative public reference: Tsinghua University materials on “Qing Xiao Da.” The three-day program is independently designed by AI Symbiosis Island and does not claim affiliation with Tsinghua University.
02
Three-day design studio
Experience the method, build the teaching asset, test the lesson
DAY 01
Reframe teaching for the AI era
Map one course challenge, examine responsible-use boundaries and experience prompts, toolchains and knowledge-supported learning.
OUTPUTLearning challenge canvas + AI-use boundaryDAY 02
Build an AI-supported learning flow
Design a student task, prepare the knowledge layer and prototype an assistant, agent or workflow grounded in course material.
OUTPUTRunnable teaching prototype + student task briefDAY 03
Teach, observe and improve
Run a micro-teaching demonstration, collect peer evidence and refine instructions, assessment and safeguards.
OUTPUTDemonstration + rubric + 30-day implementation planWhat faculty take back
A small set of assets that can enter real teaching
One redesigned learning task
Clear objectives, student workflow, teacher checkpoints and an explicit role for AI.
One working prototype
A course knowledge base, assistant or agent workflow built around available materials.
Evidence and integrity rules
A rubric, verification expectations, disclosure guidance and boundaries for sensitive information.
A 30-day next step
A realistic plan for piloting, gathering evidence and improving the design with colleagues.
Plan a pilot around your institution
Confirm the audience, scenario, duration, available mentors and expected evidence before delivery.