Interdisciplinary Seminar

Interdisciplinary Seminar

General Courses

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All Schools

1Year

IA9991GCA7

2.00Credits

講義・演習 (Lecture/Seminar)

Spring semester 2026

(Spring semester)

Syoji Kobashi

Graduate School of Engineering

Japanese

4／9

Since this course is offered as an intensive program, regular office hours are not established. Students may consult with the instructor during breaks or before and after the class as needed.

Please submit all inquiries and questions regarding the course via the UNIPA Q&A system, where they will be shared with all instructors.

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2-2◎／1-2〇

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[Course Objectives]
Through working on introductory topics while actively exchanging ideas and opinions with students from different academic backgrounds, this course aims to deepen students’ identity as members of the University of Hyogo and to cultivate the foundations of interdisciplinary thinking through cross-disciplinary learning. By engaging in practical learning beyond departmental boundaries, students will develop problem-solving skills.

[Learning Outcomes]
1) Students will acquire a broad range of knowledge and skills that form the foundation of interdisciplinary thinking.
2) Students will be able to act proactively and autonomously toward self-realization.

Subtitle:
Broadening Horizons Beyond Academic Boundaries

Keywords:
Interdisciplinary Learning, Interdisciplinary Thinking, Practical Learning, Problem-Solving Skills

[Course Description]
This seminar is an interdisciplinary practicum open to students from all faculties, regardless of their academic background, prior coursework, or experience with AI or programming. The course minimizes lecture-based instruction and emphasizes hands-on, student-centered activities. Students from diverse disciplines form teams to identify issues related to everyday life or university settings and work on planning, prototyping, and validating AI-based solutions.
No programming skills are required. By utilizing no-code tools and AI-assisted code generation, students translate their ideas into tangible and functional forms and examine their effectiveness and limitations. Rather than focusing on technical sophistication or completeness, the course places importance on the process of testing ideas, evaluating outcomes, and iteratively improving solutions.
Throughout the course, students experience the entire workflow, from problem identification and solution design to prototyping, functional testing, and evaluation. Methods for validation, including the use of no-code tools and AI-assisted implementation, are introduced and practiced during class sessions. Group discussions and prototyping activities are conducted primarily in classrooms or seminar rooms used for the course, and shared campus spaces may be utilized as needed.
Expected deliverables include simple application or service prototypes that demonstrate problem-solving concepts, interface or screen design mockups, and functional demonstrations. By completing this seminar, students are expected to become capable of using AI as an accessible tool to realize their own ideas, to test and refine those ideas, and to clearly communicate their solutions to others.

[Course Schedule]
This course is offered as an intensive summer program consisting of three days (15 class sessions in total).

1. Orientation: Overview of course objectives and structure, self-introductions, and team formation
2. Issue-sharing workshop: Sharing and discussing issues related to everyday life and university settings
3. Problem analysis: Organizing background information, target users, and problem structures
4. Approaches to AI utilization: Examples of AI applications and key considerations
5. Solution ideation: Generating ideas for AI-based solutions
6. Solution ideation (continued): Clarifying service or application objectives, functions, and usage scenarios
7. Setting evaluation criteria: Determining how to assess solution effectiveness
8. Planning implementation: Selecting approaches using no-code tools and AI-assisted development
9. Prototyping I: Implementing solutions using no-code tools and AI assistance
10. Prototyping II: Functional testing and refinement
11. Improvement I: Revising solutions based on prototype results
12. Improvement II: Reassessing validation methods and refining implementations
13. Evaluation: Verifying effectiveness based on assumed usage scenarios
14. Preparation for presentation: Organizing deliverables and evaluation outcomes
15. Final presentations and wrap-up: Team presentations, discussions, and overall review (confirmation of learning outcomes)

In-person

This course will be conducted in a face-to-face format.

Fully permitted

When using generative AI, students must comply with "The University Policy on the Use of Generative AI in Education (for Students)."

In this course, the use of generative AI is fully permitted for in-class activities, preparation and review, and the creation of deliverables. However, generative AI is intended as a supportive tool for thinking and prototyping, and submitting AI-generated outputs as-is as final deliverables is not permitted. Students are required to verify facts, evaluate the appropriateness of AI-generated content, and revise it based on their own ideas.

If generative AI is used, students must appropriately disclose its use in their deliverables or presentations and pay careful attention to copyright, personal data, and confidential information. Details regarding the use of generative AI should follow the instructions provided by the instructor.

No textbook is required. All necessary materials will be distributed via UNIPA.

The following resources are provided as references to support understanding of the course content. They are not required readings and may be consulted as needed.

• Official documentation and tutorials related to generative AI and no-code tools (introduced during the course)
• Introductory materials on problem-based learning (PBL) and design thinking (introduced during the course)

[Preparation]
Students are encouraged to think in advance about issues or inconveniences in everyday life or university settings that could be improved.
(Estimated total time for the entire course: approximately 1 hour)

[Review]
During and after the intensive course, students should reflect on group discussions, prototyping, and evaluation activities, and organize their own learning outcomes. Additional time may be spent refining deliverables and preparing for presentations.(Estimated total time for the entire course: approximately 2–3 hours)

This course is conducted using an active learning approach that emphasizes student participation. Students from different majors and academic backgrounds work in groups to identify issues related to everyday life and university settings, and to discuss their underlying causes and contexts.

Through in-class activities, students experience a full cycle of learning, including group discussions, idea visualization, solution design, prototyping, evaluation, and presentations. By using no-code tools and generative AI, students are able to prototype their ideas in a functional form while minimizing the need for programming, and to examine the effectiveness and limitations of their proposed solutions.
Instructors act as facilitators, supporting discussions and hands-on activities while encouraging peer interaction and mutual feedback. Through these active learning processes, students develop the ability to think independently, collaborate with others, and engage in practical problem-solving.

Student performance is evaluated comprehensively based on participation in the intensive course activities, according to the following criteria:

1. Participation and contribution to group activities (40%)   Active engagement in group discussions, prototyping, and collaborative problem-solving activities is evaluated.
2. Deliverables and their content (40%)   The appropriateness of problem setting, the quality of solution design, the creativity of prototyping and evaluation, and the clarity of explanation are assessed. Emphasis is placed on the process of trial, evaluation, and improvement rather than technical sophistication or completeness.
3. Presentation and reflection (20%)   Clarity and logic in final presentations, as well as the ability to reflect on learning outcomes and insights gained throughout the course, are evaluated.

Evaluation results and feedback on assignments and deliverables will be provided through UNIPA. When appropriate, general feedback and explanations of evaluation criteria will also be given during class sessions.

This course is open to students from all faculties, and no prior knowledge of AI, programming, or specific academic fields is required. The course is conducted in an intensive format and focuses on group-based activities such as discussions, prototyping, and presentations.

Students are required to bring their own laptop computers to class. The university network (Eduroam) will be used, and instructions on tools and environments will be provided during the course.

As the course emphasizes group work, students are expected to participate actively and collaborate respectfully with others. Due to the intensive nature of the course, attendance at all sessions is expected in principle.

Not applicable.