▲ 生成式AI 在教學上之使用

When AI Evolves, Classrooms Must Advance Too: Cheng Shiu University Joins the TAICA Alliance to Bring Top-University AI Courses into Technical–Vocational Education

As AI technologies continue to evolve, higher education must move forward with equal momentum. After joining the Taiwan AI College Alliance (TAICA), Cheng Shiu University (CSU) officially introduced the rigor, pace, and challenges of top-tier university AI courses into its campus—aiming for one simple goal: to broaden students’ horizons and strengthen their ability to navigate the future.

From “I Want to Drop the Class” to “I Can Keep Up”: Growth Begins When Students Don’t Run Away

In the very first week of TAICA’s Machine Navigation and Exploration course, many students felt overwhelmed.
Some struggled with the learning load, others considered withdrawing; out of the original 18 students, 12 contemplated dropping the course.

Upon learning this, CSU’s Center for Teaching and Learning Development immediately activated a support plan:

• Assisting students with course adjustment

• Providing individualized advising

• Assigning co-teaching faculty to sit in and support comprehension

• Monitoring each student’s progress throughout the semester

No matter which course students ultimately chose, the university’s message was clear:
don’t retreat—find the path that fits you.

Most students who switched to Generative AI: Text and Image Generation successfully completed the course and gained stronger confidence in handling next-generation AI tools.

Learning Is No Longer a Solo Battle: Faculty and TAs Learn and Run Alongside Students

To ensure students could truly keep pace with TAICA standards, CSU developed a “three-pillar support model”:

1. Faculty Support
Departments assigned co-teaching instructors to attend every class session and offer immediate guidance.

2. Learning Assistance
Additional teaching assistants were hired to help with assignments, monitor progress, and resolve learning bottlenecks.

3. Equipment & Learning Environment
Unified instruction took place in dedicated departmental computer labs to ensure consistent hardware and software availability.

Questions were discussed right after class so they would not accumulate week after week.

Even when enrollment in Machine Navigation and Exploration dropped to fewer than ten students, the College of Engineering continued to assign co-teaching faculty—ensuring that no student was left to learn alone.

Learning at Top-University Level, CSU Students Deliver Strong Results

With strong academic and technical support, CSU students achieved notable outcomes:

• Generative AI Course: 57 enrolled / 51 passed — 89.47% pass rate

• Machine Navigation and Exploration: 6 persisted / 5 passed

Beyond high grades, students demonstrated meaningful growth.
Several were even recommended to participate in National Tsing Hua University’s final project workshop, where they exchanged ideas and advanced skills with peers from across Taiwan.

Next Steps: Focus on Juniors and Seniors; 23 CSU Courses Submitted for TAICA Recognition

Assessment results show that third- and fourth-year students adapt more effectively to TAICA’s pace and depth. Therefore, the university will focus future TAICA expansion on junior, senior, and graduate-level learners.

CSU has also identified and submitted 23 AI-related courses for TAICA credit recognition. Once approved, students can earn TAICA credits directly through existing CSU core and elective courses—reducing academic burden and increasing motivation.

Clear Goal Ahead:

Expand Access, Encourage Challenge, and Inspire Students to Create the Future**

CSU’s vision is straightforward:
to give more students the opportunity to learn AI professionally, take on advanced coursework, and produce impactful projects that shape the future.

Through strong alliances, comprehensive support, and AI-integrated curriculum pathways, CSU continues to bring top-tier AI education into the heart of technical–vocational learning—helping students step out of their comfort zones and step into their full potential.

▲ Generative AI Course

▲ Machine Navigation and Exploration Course

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