2026 International Conference on Artificial Intelligence and Design (AID 2026)
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About this event
2026 International Conference on Artificial Intelligence and Design (AID 2026)
The 2026 International Conference on Artificial Intelligence and Design (AID 2026) will be held in Daejeon, South Korea from July 13 to 15, 2026. AID 2026 aims to bring together researchers, academics, engineers, designers, and industry practitioners from around the world to exchange ideas and share recent advances in the interdisciplinary field of artificial intelligence and design.
About AID 2026
The conference seeks to foster dialogue and collaboration across computer science, design research, human-centered technologies, and creative industries. AID 2026 focuses on emerging theories, methods, and applications that connect AI technologies with design practice and innovation. Topics of interest include, but are not limited to:
- Machine learning for design
- Intelligent user interfaces
- Generative design
- Computational creativity
- Design automation
- Human-AI interaction
- Sustainable design
- Responsible AI in design systems
By providing a platform for both foundational research and real-world applications, the conference aims to promote new perspectives on how AI can support, transform, and extend design processes across diverse domains. AID 2026 is committed to building an open and high-quality forum for scholarly communication and professional exchange. The conference welcomes original research papers, technical contributions, and application-oriented studies from academia and industry.
Important Dates
- Submission Deadline: May 9, 2026
- Notification Date: June 13, 2026
- Registration Deadline: June 28, 2026
- Conference Dates: July 13-15, 2026
Call for Papers
Submissions are invited for the following tracks:
Track 1: AI Methods and Computational Design
- Deep Learning Methods
- Generative Models
- Knowledge Representation and Reasoning
- Computational Intelligence
- Design Optimization
- Data-Driven Design
- Pattern Recognition
- Explainable AI
- Decision Support Systems
- Machine Learning for Design
Track 2: Human-AI Interaction and Intelligent Interfaces
- Human-AI Interaction
- Intelligent User Interfaces
- Human-Centered AI
- Adaptive Interfaces
- Personalization and Recommendation
- Conversational Interfaces
- Collaborative Systems
- User Modeling
- Usability Evaluation
- Interactive Design Systems
Track 3: Design Automation and Creative Applications
- Generative Design
- Design Automation
- Computational Creativity
- Product Design Applications
- Industrial Design Systems
- Concept Generation
- Digital Prototyping
- Visual Computing for Design
- Smart Design Tools
- AI for Creative Practice
Track 4: Applied AI in Design and Emerging Issues
- Urban Computing
- Service Design Systems
- Sustainable Design
- Responsible AI
- Trustworthy Intelligent Systems
- Design Informatics
- Emerging Design Applications
Publication
All papers will be reviewed by two or three expert reviewers from the conference committees. After a careful reviewing process, all accepted and presented papers will be published in the ACM ICPS (ACM International Conference Proceeding Series). The proceedings will be submitted to indexing databases, such as EI Compendex and Scopus, subject to their scope and acceptance criteria.
Organizers and Supporters
- Organized by: Tianfu College of Southwestern University of Finance and Economics, China
- Supported by: Mokwon University Korea, Media Art Association, Korea
For more information, visit the official website: http://www.ic-aid.com/
Data sheet
2026 International Conference on Artificial Intelligence and Design (AID 2026) – Event Attendee & Buyer Profile Analysis
Event date: July 13–15, 2026
Location: Daejeon, South Korea
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 International Conference on Artificial Intelligence and Design (AID 2026) |
| Event Date | July 13–15, 2026 |
| Event Status | Upcoming |
| Venue | Venue not publicly confirmed on the official website |
| City | Daejeon |
| State / Region | Daejeon Metropolitan City |
| Country | South Korea |
| Organizer | Organizer not clearly labeled on the official website. The official site lists Tianfu College of Southwestern University of Finance and Economics, China, as Sponsor; supporters listed are Mokwon University and Korea Media Art Association. |
| Official Event Website | ic-aid.com |
| Event Type | International academic conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training |
| Audience Reach | Global |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for volume metrics; high for date, city, country, topic focus, and sponsor/supporter details based on official website content. |
| Main Purpose of Event | Research exchange, academic networking, paper presentation, and interdisciplinary collaboration around artificial intelligence and design applications. |
About the Event
AID 2026 is an international conference focused on the intersection of artificial intelligence and design. According to the official event website, the conference is designed to bring together researchers, academics, engineers, designers, and industry practitioners to exchange ideas and present recent advances across machine learning for design, intelligent user interfaces, generative design, computational creativity, design automation, human-AI interaction, sustainable design, and responsible AI.
From a commercial intelligence perspective, this is primarily a knowledge-driven conference rather than a large-scale trade show. Its value lies in access to research communities, university labs, design-technology practitioners, AI application specialists, and innovation-focused institutions. It is relevant for organizations selling research tools, AI platforms, HCI software, design-tech partnerships, applied innovation services, publication support, and academic-industry collaboration offerings; however, publicly disclosed buyer-side attendee data is limited.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University researchers and faculty | Universities, research institutes, design schools | Influence research software adoption, lab tools, collaboration platforms, sponsored projects | High for software vendors, academic publishers, research infrastructure providers |
| Academic conference authors and paper presenters | Cross-disciplinary AI and design researchers | Technical evaluators and innovation scouts rather than direct procurement owners | Useful for thought leadership, beta partnerships, and pilot engagement |
| Industry practitioners in AI and design | Software firms, digital product teams, innovation studios, UX groups | Can recommend tools, platforms, and consulting partners | Relevant for product demos, partnerships, and solution awareness |
| Human-AI interaction and intelligent interface specialists | HCI labs, product design teams, intelligent UX groups | Influence evaluation and selection of prototyping, analytics, and user testing solutions | Strong relevance for UX tech, experimentation, and interface tooling providers |
| Design automation and generative design teams | Engineering design groups, industrial design labs, creative technology teams | Potential budget influence for modeling tools, simulation, generative systems, AI APIs | High relevance for design-tech platforms and applied AI vendors |
| Association and academic network leaders | Professional associations, conference committees, academic societies | Partnership, visibility, and network-access influence | Useful for sponsorships, collaborations, and regional outreach |
| Graduate students and early-stage researchers | Universities and labs | Low direct buying power but strong influence in tool adoption and future pipeline building | Relevant for community building and long-term lead nurturing |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Host city: Daejeon | Local universities, research institutes, and design/technology practitioners | Medium | Daejeon is a major South Korean research and science hub, supporting likely academic and technical participation. |
| Host region: Daejeon Metropolitan City | Regional institutions, nearby innovation centers, and academic visitors | Medium | Relevant for local research and innovation ecosystem engagement. |
| Nearby business hubs | Seoul metropolitan area, academic and technology corridors in South Korea | Medium to High | Likely draw for Korean AI, design, and higher education participants. |
| National reach | Researchers and practitioners from across South Korea | High | Conference topics support broader national appeal across AI, HCI, design, and engineering communities. |
| International reach | Asia-Pacific, China, and other international academic contributors | Medium | The event is branded as international and the official site states it aims to gather participants from around the world. |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The event is explicitly positioned as an international conference and invites participants from around the world. |
| National | Secondary reach | Daejeon location and Korea-based supporters indicate strong likely appeal within South Korea’s academic and innovation community. |
4. Sample Buyer Companies and Websites
Official current-year attendee, exhibitor, speaker, or buyer lists were not publicly disclosed in the provided official website content. The table below includes only organizations explicitly named on the official website. This event appears to be conference-led rather than exhibitor-led, so buyer-list building potential is more limited than at a commercial trade show.
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Tianfu College of Southwestern University of Finance and Economics | Academic sponsor / higher education institution | Listed on the official site as Sponsor; likely relevant for academic partnerships, conference support, and research collaboration. | tfswufe.edu.cn | Dean, Research Director, Professor, Innovation Director, International Collaboration Manager | Confirmed Sponsor / Exhibitor |
| Mokwon University | University / academic supporter | Listed on the official site as Supporter; relevant for AI, media arts, design, and academic collaboration activity. | mokwon.ac.kr | Professor, Department Chair, Research Center Director, IT Director, Design Program Lead | Confirmed Sponsor / Exhibitor |
| Korea Media Art Association | Industry / association supporter | Listed as Supporter; relevant to creative technology, digital design, and media-art applications of AI. | Organization website not publicly confirmed from the provided source text | Association Director, Program Manager, Partnerships Director, Creative Technology Lead | Confirmed Sponsor / Exhibitor |
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Research Director | Research | Director | Influences research collaborations, software selection, and funded innovation activity. |
| 2 | Professor / Principal Investigator | Academic / Research | Senior | Often drives lab tooling, sponsored research, and university-industry adoption. |
| 3 | AI Research Scientist | R&D | Manager / Individual Contributor | Technical evaluator for AI tooling, model platforms, and experimentation infrastructure. |
| 4 | Director of Innovation | Innovation | Director | Useful for commercial adoption of applied AI and design transformation initiatives. |
| 5 | Head of Human-Computer Interaction | Product / UX / Research | Director / Head | Strong fit for intelligent interface, user modeling, and interaction design topics. |
| 6 | Product Design Director | Design | Director | Relevant for generative design, design automation, and creative tool evaluation. |
| 7 | CTO | Technology | C-Level | Relevant where academic innovation intersects with enterprise AI applications. |
| 8 | IT Director | Information Technology | Director | Potential owner of platform deployment and institutional technology procurement. |
| 9 | Partnerships Director | Business Development / Partnerships | Director | Important for sponsorship, institutional alliances, and research-commercial bridge building. |
| 10 | Conference / Program Manager | Programs / Events | Manager | Useful for sponsor outreach, community-building, and future event partnerships. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Research | Core fit for conference-led academic and applied research participation. | Research partnerships, software tools, data platforms |
| 2 | Higher Education | Universities and faculty are core attendee types. | Academic tech sales, lab enablement, collaboration programs |
| 3 | Information Technology & Services | Applied AI vendors and enterprise innovation teams align with event themes. | AI platform outreach, integration services, technical partnerships |
| 4 | Computer Software | Relevant for design tools, model platforms, and interface software. | Product pilots, API adoption, developer relations |
| 5 | Design | Directly aligned with conference positioning around AI and design practice. | Creative tooling, generative workflows, design automation |
| 6 | Industrial Automation | Relevant to design automation and intelligent systems topics. | Automation software, intelligent process design, prototyping systems |
| 7 | Mechanical or Industrial Engineering | Relevant for product design applications and optimization tracks. | Simulation, optimization, design engineering tools |
| 8 | Computer Hardware | Supports AI compute and prototyping environments. | Workstations, acceleration hardware, lab infrastructure |
| 9 | Education Management | Relevant for institutions managing AI and design education programs. | Curriculum support, digital learning, institutional transformation |
| 10 | Broadcast Media | Useful for media art and computational creativity applications. | Creative AI, content generation, interactive media systems |
| 11 | Online Media | Relevant for creative and digital experience applications of AI. | Interactive content tools, personalization, AI-enabled design workflows |
| 12 | Professional Training & Coaching | Relevant for AI upskilling and design-method training offerings. | Executive education, AI-design capability building |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | Official website content reviewed | No public attendance number found in provided official source text. |
| Exhibitor count | Not publicly confirmed | Unconfirmed | Official website content reviewed | Conference appears academic rather than exhibition-led. |
| Buyer count | Not publicly confirmed | Unconfirmed | Official website content reviewed | No buyer-program or hosted-buyer structure disclosed. |
| Speaker count | Not publicly confirmed | Unconfirmed | Official website content reviewed | Committee and program navigation exist, but no speaker count provided in source text. |
| Sponsor count | 1 sponsor; 2 supporters named | Confirmed | Official website sponsor section | Named organizations only; not a full sponsor prospectus. |
| Historical attendance | No verified prior-year figure found in provided source | Unavailable | Official website content reviewed | No historical edition metrics supplied in the available source text. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| AI methods and computational design | Model experimentation, data-driven design, explainable AI | Research demos, technical workshops, pilot collaborations | ML platforms, AI APIs, analytics tools, research software |
| Human-AI interaction | Intelligent interfaces, adaptive UX, usability evaluation | UX-focused thought leadership and evaluation partnerships | User testing platforms, interface frameworks, personalization engines |
| Generative design | Design ideation acceleration and automated concept generation | Product demonstrations, innovation pilots, case-study sharing | Generative design tools, simulation software, cloud compute |
| Design automation | Workflow efficiency, automation of repetitive design tasks | Process-improvement and integration discussions | Automation platforms, integration services, intelligent design systems |
| Computational creativity | New creative workflows and media-generation capabilities | Creative-tech partnerships and media-art applications | Creative AI tools, content generation systems, interactive media software |
| Sustainable design | Design approaches that improve efficiency and responsible innovation | Applied research and institutional partnerships | Sustainability analytics, lifecycle modeling, optimization tools |
| Responsible AI | Governance, transparency, trustworthy design systems | Policy and methodology discussions with research leaders | AI governance software, audit frameworks, ethics consulting |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Medium | Relevant for research, innovation, design-tech, and academic solution providers; less relevant for broad commercial product sourcing. |
| Decision-maker availability | Medium | Faculty, research directors, and innovation leaders may attend, but direct procurement roles are not a stated core audience. |
| Data collection potential | Low | No public attendee list, buyer program, or exhibitor directory confirmed in the available source. |
| Apollo targeting potential | High | Event themes map well to Apollo filters by industry, department, seniority, and AI/design keywords. |
| Geographic targeting potential | High | Strong focus on South Korea and broader international academic networks makes geo-segmentation practical. |
| Best outreach approach | High | Use thought-leadership outreach, collaboration offers, technical demos, and conference-adjacent networking rather than aggressive transactional sales. |
| Overall lead quality | Medium | Good for specialized AI/design B2B and academic partnerships; weaker for mass buyer-list sales. |
| Best use case | High | Ideal for niche prospecting into AI research, HCI, design innovation, and university collaboration targets. |
| Limitations / risks | High | Limited public transparency on attendees, venue, and attendance metrics reduces confidence for direct attendee-list monetization. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Research; Higher Education; Information Technology & Services; Computer Software; Design; Industrial Automation; Mechanical or Industrial Engineering; Education Management; Broadcast Media; Online Media | Aligns targeting with AI-design, HCI, and academic innovation audiences. |
| Departments | Research, Engineering, Information Technology, Product, Design, Innovation, Partnerships, Education | Filters to functions most likely to care about AI and design applications. |
| Seniority | C-Level, VP, Director, Head, Manager, Senior | Focuses on technical and institutional decision makers. |
| Job titles | Research Director, Professor, Principal Investigator, AI Research Scientist, Director of Innovation, Head of HCI, Product Design Director, CTO, IT Director, Partnerships Director, Program Manager | Captures both academic and applied-commercial contacts. |
| Geography | South Korea; Daejeon; Seoul; broader APAC; China; global academic hubs | Supports local event proximity and international conference relevance. |
| Employee size | 11–50; 51–200; 201–500; 501–1,000; 1,001–5,000; 5,001+ | Covers research labs, universities, and technology organizations of varied scale. |
| Keywords | artificial intelligence, machine learning, generative design, computational creativity, design automation, human-AI interaction, intelligent interface, user modeling, explainable AI, responsible AI, sustainable design | Mirrors official event themes for thematic targeting. |
| Technologies | AI/ML platforms, UX tools, design software, cloud compute, analytics stacks | Useful where Apollo or enrichment tools support technology signals. |
| Revenue range | Use optional filter only for commercial organizations; avoid over-restricting university targets | Preserves coverage across education and research entities with non-commercial profiles. |
| Company type | Private, Public, Nonprofit, Educational Institution, Association | Matches the mixed academic, industry, and association composition of the event. |
Suggested Apollo Search Logic: Target contacts using combinations such as: (“artificial intelligence” OR “machine learning” OR “generative design” OR “human-AI interaction” OR “design automation” OR “computational creativity”) AND (research OR design OR innovation OR HCI OR product) with geography centered on South Korea and broader APAC. Prioritize Director+ seniority, then expand to senior researchers and professors for relationship-based outreach.
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| AID 2026 Official Website | Official event website | Event name, dates, city, country, conference themes, important deadlines, sponsor and supporter names, publication information | High |
| Official website content provided in user brief | Primary-source extract | Confirmed that venue, attendance count, buyer list, exhibitor directory, and speaker roster were not publicly disclosed in the supplied source text | High for omission-based verification |
Verification note: This event is suitable for specialized B2B prospecting in AI, design, higher education, and research collaboration, but it is not currently ideal for large-scale confirmed attendee list building because public participant disclosure is limited.
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