Seminar Internasional DKV ISI Yogyakarta

Seminar Summary

The seminar explored how visual communication design education can combine manual practice, local cultural knowledge, and artificial intelligence without treating AI as a replacement for human creativity. Across the presentations, speakers argued that AI makes it easier to produce many images and variations, but increases the importance of deciding what is relevant, meaningful, ethical, and appropriate for an audience.

Main Concepts

• Design is a process, not just a finished image. It includes research, judgment, experimentation, systems of working, and the thinking behind creative decisions.
• AI can assist production, but people provide direction and meaning. Human imagination, empathy, lived experience, cultural understanding, and responsibility distinguish purposeful design from a generic result.
• Manual skills remain valuable. Drawing and making by hand help designers notice form, pressure, materials, composition, and problems that may be missed on screen.
• Local culture is a source of creative knowledge. Cultural references should be researched and interpreted in context, not treated as decorative motifs or used without regard for the communities connected to them.
• Education should assess reasoning as well as outcomes. Students should be able to explain their choices, show how their work developed, and identify the limits of what they know.

Techniques and Creative Processes Discussed

Using AI as Part of a Design Workflow

A recurring process was:

1. Start with an idea or question grounded in the designer’s imagination, experience, or research.
2. Frame the task clearly, including the audience, purpose, context, and constraints.
3. Write a specific prompt and provide relevant references or details rather than relying on a broad request.
4. Generate alternatives using an AI tool suited to the task.
5. Direct and curate the results by comparing options against established criteria.
6. Refine the chosen direction through human judgment, editing, testing, and attention to meaning and aesthetics.
7. Document the process, including what was made by hand, what AI contributed, what was rejected, and why.

The speakers stressed that prompts benefit from strong reading, writing, research, and visual reference skills. AI outputs should be checked rather than accepted automatically.

Manual Practice and Visual Foundations

Manual exercises can help students develop:

• Observation and control of materials
• Awareness of composition, spacing, and form
• The ability to recognize visual problems and unexpected possibilities
• Criteria for judging later digital or AI-generated work

The purpose of a hand-drawn or physical exercise should be explicit. A sketch can record an existing idea, but the act of drawing can also reveal a new relationship or expose a weak assumption.

Film and Animation Workflows

Heru Jalal described AI-assisted production as part of a broader filmmaking process:

• Pre-production: Research, develop the premise and script, prepare voice-over, and create storyboards.
• Production: Execute the planned scenes, using AI where it supports the intended visual work.
• Post-production: Edit, composite, and refine the material.

He emphasized that AI does not remove the need for a strong foundation in visual communication, cinematography, storytelling, and production planning. Human-written scripts, performance, voice work, editing, and creative direction can remain central even when AI assists with visual generation.

Local Culture, Research, and Responsibility

Muhammad Zafran presented questions for assessing cultural references:

• Source: Where did the material come from, and can others trace it?
• Meaning: What evidence supports the interpretation?
• Use: Is the reference appropriate for this particular design and audience?
• Authority: Who has the knowledge or standing to question the interpretation?

He also stressed consent, attribution, clear access conditions, and community involvement. Permission to view material does not automatically mean permission to upload it to an AI service. Some knowledge may be appropriate for public use; other material may need to remain restricted or private.

Teaching and Assessment Proposals

Zafran proposed connecting five areas through an AI Synergy framework:

• Curriculum
• Pedagogy
• Assessment
• Technology
• Governance

A studio exercise could set clear conditions for AI use before, during, and after generation. Students might first make their own observations or studies, then use AI to explore bounded questions, and finally edit, prototype, and test their work.

One illustrative assignment asked students to design an invitation for a paper-folding workshop. They would study and fold a butterfly form, compare two layout options, check which communicates the activity most clearly, and document their AI use and revisions. The example rubric proposed:

• Cultural context: 25%
• Judgment and iteration: 25%
• Communication and craft: 20%
• Verification and disclosure: 15%
• Transferable learning: 15%

Zafran described this as a discussion proposal, not a universally validated grading system.

Practical Advice from the Speakers

• Treat AI as a tool or assistant, not an independent author or substitute for human judgment.
• Build strong foundations in visual literacy, storytelling, research, and design principles.
• Make prompts specific by supplying context, audience, references, and constraints.
• Compare generated alternatives against criteria established before generation.
• Keep evidence of the creative process, including sketches, rejected directions, revisions, and decisions.
• Disclose AI contributions and consider copyright, attribution, consent, and community interests.
• Test designs with their intended audience rather than relying on personal preference alone.
• Develop local knowledge and cultural sensitivity alongside technical skills.
• In education, evaluate how students think and decide, not only how polished the final image appears.
• Consider a small studio pilot, then review learning outcomes, access, documentation requirements, and feedback before expanding the approach.

Examples Shared

Arif Caksono discussed AI as a way to realize ideas that might previously have been too costly or technically difficult. His examples included experiments with music and visuals, locally grounded game concepts, and a proposed GPS-triggered audio tour built around historical narratives. He also used a participatory story exercise to show how choices, memory, empathy, and relationships can shape the meaning of a generated design.

Heru Jalal contrasted AI-assisted production with earlier photography and design workflows, which often required extensive equipment, setup, time, and expense. He argued that AI can reduce production barriers while making creative direction and preparation more important.

David Christian described changes in professional work: as producing images becomes cheaper, judgment, authorship, and responsibility become more valuable. He recommended portfolios that show decisions and process, not just finished results, and described the importance of combining local knowledge with skills that can reach global clients.

Featured Speakers and Contributors

• Arif Caksono — Multidisciplinary artist and content creator
• Heru Prasetyo Jalal (Heru Jalal) — AI filmmaker, creative entrepreneur, and educator
• Dr. Muhammad Zafran — Design educator and researcher; his name is rendered inconsistently in the auto-generated captions
• David Christian — Founder of an academy focused on practical skills and AI
• Mirza Maulana Muhammad Musa — Moderator and artistic director
• Muhammad Shahuddin — Dean of the Faculty of Fine Arts and Design, who formally opened the seminar

The captions also refer to Widyawati, a Microsoft government-communications professional, in connection with an earlier session.