Skip to content
NTPU AI Learn

About NTPU AI Learn

NTPU AI Learning & Practice Platform

Platform overview

NTPU AI Learn is National Taipei University’s platform for learning and practicing AI, built and operated by the NTPU Advanced Research Center for Cross-Disciplinary AI (NTPU AI4X).

The platform is organized around courses: 28 published today, spanning three application domains, campus, industry, and government. Every course combines learning content, hands-on practice, assessment, and application to real work, helping learners move from understanding a concept to actually applying it.

Claude is one of the AI tools introduced in the first course, AI 101, not the platform itself. NTPU AI Learn follows an open, vendor-neutral design principle, covering whatever AI topics, methods, and tools each course actually needs, without being locked to a single technology or vendor.

Through this design, NTPU AI Learn aims to be more than a teaching platform: a cross-disciplinary platform for learning and collaboration that connects academic research, industry practice, and public governance, drawing on National Taipei University’s existing strengths in law, business, and public affairs to drive AI applications with real practical connection and social impact.

Purpose

To help colleagues on campus, industry partners, and government staff all use AI in daily work and real decisions safely, effectively, and with good judgment, and to turn what they learn into applications the organization can discuss and adopt: learning is not the destination, it is the starting point for adoption and co-creation.

  1. Learn
    Build the right mental model and judgment for working with AI
  2. Practice
    Work through realistic files or real project scenarios
  3. Assess
    Pre- and post-tests make learning outcomes visible
  4. Apply
    Complete a Use Case Canvas that connects back to your own work or agency
  5. Use Case Data
    What learners and teams produce becomes concrete evidence a department or agency can evaluate for AI adoption
  6. Interview
    Discuss that real evidence with departments, companies, or agencies to assess feasibility and risk
  7. Implementation
    Cases that clear evaluation move into an industry pilot (the IL series) or government procurement and acceptance (the GoAI series)

Feedback and continuous improvement

When a course ends, learners can leave course feedback in about 2 to 3 minutes: course experience, learning outcomes and optional open comments. Instructors see statistics for their own sessions, an organization's HR contact sees de-identified statistics and comments, and the platform uses the feedback and anonymous form-performance numbers to keep improving courses. Feedback never shows your name and is kept for at most 24 months.

Three domains: Campus, Industry, Government

One learning methodology serves three distinct but connected domains: building individual and organizational AI literacy, co-creating real industry projects with companies, and helping government agencies build responsible AI governance and procurement capability. All three share the same course architecture, bilingual content, server-side scoring, and credential mechanics, so quality stays consistent, while each domain’s content and scenarios are designed specifically for it.

Cross-disciplinary collaboration and value co-creation

NTPU AI Learn’s three-domain design comes from NTPU AI4X’s core philosophy of industry-government-academia-research collaboration: AI’s value is not proven in a classroom, it is proven in real work, real projects, and real governance. The platform brings these four roles into one methodology, so what each one produces can connect to the next instead of operating in isolation.

  • Academia: National Taipei University leads the academic research and course design, ensuring the rigor and bilingual quality of the teaching content.
  • Research: NTPU AI4X, the Advanced Research Center for Cross-Disciplinary AI, provides the forward-looking research and methodology, and supports course practice with services such as MCP.
  • Industry: company sponsors and domain experts bring real problems through the Industry AI Co-creation Lab (the IL series), validated through proof of value, pilots, and an approved public case library.
  • Government: central and local government agencies build responsible AI application judgment, governance, procurement, and acceptance capability through the GoAI series.

This design philosophy echoes an integration pattern common among university AI centers: rather than splitting teaching, research, industry collaboration, and public governance into disconnected units, it links each stage’s inputs and outputs through one consistent evidence chain, learning records, practice artifacts, Use Case data, and review and acceptance results.

This lets work from an earlier stage keep being referenced by later research, application, and decisions, reducing duplicated effort, and lets the university, companies, and government agencies discuss AI adoption’s outcomes, risks, and value within a more consistent shared framework.

Info

Companies or agencies interested in partnering with us, project co-creation, strategic partnership, joint labs, public-sector collaboration, and more, are welcome to reach out through the Industry AI Co-creation Lab page.

Who it serves

  • Learners: read the course, download practice files, take the tests, and submit a Use Case and Exit Ticket
  • Teachers: teach with Presenter View, guide practice, and understand learning outcomes
  • Administrators: create and manage course sessions, read analytics, and export data

Learning model

Every course follows one path: learn the concepts, practice on realistic files, check your progress with tests, and turn what you learned into a Use Case for your own work.

  1. Learn
    Read the modules and confirm your understanding with short checks
  2. Practice
    Work through realistic files
  3. Assess
    Pre- and post-tests make progress visible
  4. Apply
    Complete a Use Case Canvas and an Exit Ticket

Course numbering

Courses are identified by a permanent number, such as AI 101. The number never changes when a course is renamed, so links and references stay valid over time.

Bilingual learning

The platform and every course are available in Traditional Chinese and English. Each language is written natively rather than machine-translated, and the structure is identical. When you switch languages you stay on the same page.

Hands-on learning

We believe working skill is built by doing. Courses use realistic meeting notes, reports and customer feedback so you practice in conditions close to real work.

Responsible AI use

Human

  • Defines the task and goal
  • Reviews AI output
  • Remains accountable for the result

AI

  • Assists or executes the task
  • Provides drafts and analysis
  • Does not replace judgment or responsibility

Info

Human → AI → Human: high-impact output must always be reviewed by a person before it is used.

Course completion certificates and capability certificates

Completing a course can earn two kinds of credential: a course completion certificate, which means you met that course’s own completion requirements (its lessons, practice and assessment threshold), and a capability certificate, which means a submitted artifact (such as a Use Case Canvas) was reviewed by a person against a rubric and found to demonstrate real, independent-level skill. The two are earned independently, passing an assessment alone never grants a capability certificate.

Rubrics and review results are handled only by authorized course reviewers on the platform backend. Correct assessment answers never appear on any page or file a learner can see.

Info

Certificates represent course completion and reviewed capability, not a specific job qualification or professional certification. Each certificate has its own verification link that can be shared with a third party.

Roles and help

Learners, instructors and administrators each have a public manual in the Help Center, and the platform documentation explains how the platform works. The applications instructors and administrators actually operate require authorized sign-in; the help and documentation are fully public.

What next?