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1. Purpose

To promote teaching innovation, enhance student learning support, and cultivate AI literacy among faculty and students, this program encourages:

• Faculty members to integrate generative AI (G-AI) tools into course design, teaching strategies, and instructional materials.

• Students to apply G-AI tools for self-directed learning and increased learning efficiency.

2. Eligibility

Faculty:
Full-time or part-time instructors who are teaching TMU courses and integrating generative AI tools into their curriculum in the 114-1 semester.

Students (TMU enrolled students only):

• Graduate students applying G-AI tools in their research topics.

• Students with approved self-directed learning projects that clearly include a G-AI usage plan.

• Students participating in internal or external competitions (limited to one account per team).

• AI-related student clubs (limited to one account per club).

• Undergraduate students involved in project-based research courses using G-AI tools.

3. Subsidy Details

• Applicable to all types of generative AI tools.

• Subsidy amount: Up to USD $20 per month per account.

• Note: Subsidy excludes foreign transaction fees and VAT.

4. Subsidy Period

• From the date of approval through November 30, 2025.

• Reimbursement documents must be submitted to the Teaching and Learning Resources Center (TLRC), Office of Academic Affairs, by November 20, 2025 (Thursday).

• Receipts must be submitted within one month of the invoice date.

5. Quota

• Limited to 50 accounts per month.

6. Application Procedure

Application Period:
From the date of announcement until July 24, 2025 (Thursday), 17:00.

How to Apply:

• Faculty: Fill out the designated Google Form within the application period.

• Students: Complete and sign the application form. Submit a scanned copy via email to the TLRC.

  • Email: jiamei@tmu.edu.tw

  • Email Subject: "114-1 Generative AI Tool Subsidy Application – [Applicant Name]"

  • If this is your second application, please include a brief summary of your previous application and outcomes.

7. Review Process & Criteria

• The review committee will be convened by the Dean of Academic Affairs and include internal experts in generative AI.

• Evaluation Criteria:

  • Faculty: Teaching/learning goals, rationale for selected G-AI tool, instructional integration plan, and expected outcomes.

  • Graduate students: Research motivation, objectives, topic, design, and expected results.

  • Undergraduate students in self-directed learning projects: Must submit the approved project application and ensure it passes review after the semester starts.

  • Competition participants: Evaluation based on official competition documents or promotional materials.

  • AI-related student clubs: Submit detailed G-AI learning activity plans.

  • Students in research projects: Explain how G-AI tools will be applied in the research process.

Review Period: July 28 (Monday) to August 15, 2025 (Friday)
Notification of Results: August 21, 2025 (Thursday)

8. Evaluation at End of Semester

Participants are required to join the teaching outcome sharing session organized by the TLRC.

• Faculty: Please present the course, how G-AI was applied, results (both faculty and student perspectives), solutions to challenges, and other feedback.

• Students: Share how you used the G-AI tools, project outcomes, the impact of G-AI on your work, experiences, pros and cons, etc.

For further inquiries, please contact Ms. Chia-Mei Chang at the TLRC (Ext. 2153).

Generative AI Tool Subsidy Workflow

1. Faculty: Submit the Google Form.
   Students: Submit the signed application form.

2. Applications are reviewed by the G-AI project team.

3. Approved applicants must register for an account directly on the G-AI platform.

4. Submit the following within 1 month to the TLRC for reimbursement:

   • Invoice or receipt for the subscription

   • NT dollar credit card transaction statement

   • TMU Expense Proof Form (must be signed by the handler)

5. At the end of the usage period, participate in the TLRC’s teaching outcome sharing session.

6. Case closed.