2026 International Conference on Artificial Intelligence for Health and Education (ICAIHE 2026)
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About this event
2026 International Conference on Artificial Intelligence for Health and Education (ICAIHE 2026)
Date: July 8–10, 2026
Venue: Tokyo, Japan
Official Website: https://www.icaihe.org/
Event Description
The 2026 International Conference on Artificial Intelligence for Health and Education (ICAIHE 2026) will be held in Tokyo, Japan, from July 8 to 10, 2026. The conference focuses on AI-enhanced and data-driven approaches to revolutionize health and education sectors. It aims to leverage advanced AI technology to provide new opportunities in healthcare and education, optimizing personal health and learning environments through emerging technologies.
ICAIHE 2026 builds upon two previous international workshops: the International Workshop on AI-Empowered Digital Health and Well-being Promotion (AI-DHWP 2025) and the International Workshop on LLM and Agentic AI for Personalized Learning (LAAPL 2025). It serves as a platform for researchers, practitioners, and graduate students to exchange ideas and gain insights into innovative AI applications in health, medicine, education, and learning, while fostering discussions on ethical considerations and future directions of AI integration into diverse human well-being contexts.
Key Highlights
- Objective: Promote human well-being through AI in health and education.
- Target Audience: Researchers, practitioners, graduate students from AI, big data, healthcare, education, and human-computer interaction fields.
- Submission Types: Full Paper (12-15 pages), Short Paper (6-11 pages), Work-in-Progress Paper (6-7 pages).
- Publication: Accepted papers will be published in ICAIHE 2026 Springer Conference Proceedings- CCIS, submitted for indexing to Ei Compendex, Scopus.
- Awards: Best Paper Award, Best Student Paper Award, Best Workshop Paper Award, Best Student Workshop Paper Award.
Important Dates
| Event | Date |
|---|---|
| Workshop/Special Session Proposal Due | February 28, 2026 (Extended) |
| Regular Paper Due | March 21, 2026 (Extended to March 31, 2026) |
| Workshop/Special Session Paper Due | March 28, 2026 (Extended to April 15, 2026) |
| Late Breaking Work Paper Due | April 15, 2026 |
| Author Notification | May 1, 2026 |
| Paper Registration Due | May 10, 2026 |
| Camera-ready Submission Due | May 15, 2026 |
Contact Information
Conference Secretary: Ms. Carly Wang
Local Organizing Chair: Dr. Ruichen Cong
Contact: contact@icaihe.org
Data sheet
2026 International Conference on Artificial Intelligence for Health and Education (ICAIHE 2026) – Event Attendee & Buyer Profile Analysis
Event date: July 8–10, 2026
Location: Tokyo, Japan
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 International Conference on Artificial Intelligence for Health and Education (ICAIHE 2026) |
| Event Date | July 8–10, 2026 |
| Event Status | Upcoming |
| Venue | International Conference Center-Waseda Campus, Waseda University |
| City | Tokyo |
| State / Region | Tokyo Metropolis |
| Country | Japan |
| Organizer | ICAIHE 2026 Organizing Committee |
| Official Event Website | www.icaihe.org |
| Event Type | International academic and professional conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Medical & Pharma; Education & Training; Science & Research |
| Audience Reach | Global academic and professional reach |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for volume metrics; confirmed for event identity, dates, city, country, and conference scope |
| Main Purpose of Event | To convene researchers, practitioners, and graduate students focused on AI-enhanced and data-driven applications in health, medicine, education, and learning, with discussion of practical innovation and ethical integration. |
About the Event
ICAIHE 2026 is an international conference dedicated to artificial intelligence applications across health and education. Based on the official event description, the conference centers on AI-enhanced and data-driven approaches that improve human well-being, personal health environments, and learning systems through technologies such as big data, IoT, wearables, sensors, and digital platforms.
From a commercial intelligence perspective, this is a specialist knowledge-exchange event rather than a mass-market trade show. It is most relevant for organizations selling research tools, data infrastructure, AI platforms, digital health systems, education technology, analytics solutions, cloud environments, and innovation partnerships into universities, healthcare institutions, research labs, and applied AI programs. The strongest value lies in thought-leadership outreach, partnership development, speaker-driven prospecting, and institutional account mapping rather than high-volume attendee list building.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| Academic researchers and principal investigators | Universities, AI labs, health informatics centers, education research institutes | Shape research tool selection, pilot programs, grants, collaborations, and publication-oriented technology adoption | High for AI software, data platforms, annotation tools, analytics, cloud compute, and research partnerships |
| Healthcare AI practitioners | Hospitals, medical schools, digital health groups, clinical informatics teams | Influence solution evaluation for AI-enabled diagnostics, patient engagement, clinical data analysis, and population health | High for digital health vendors, medical AI platforms, secure data environments, and applied research services |
| Education technology and learning science specialists | Universities, e-learning centers, schools of education, EdTech labs | Guide adoption of personalized learning, LLM-enabled education tools, student analytics, and curriculum innovation | High for EdTech, adaptive learning, LMS enhancement, tutoring AI, and analytics vendors |
| Data science and AI engineering teams | Research institutions, university engineering departments, applied AI programs | Evaluate model development environments, datasets, MLOps, cloud infrastructure, and deployment frameworks | Strong for AI tooling, GPU/cloud services, security, model monitoring, and developer platforms |
| Graduate students and doctoral candidates | University programs in AI, HCI, healthcare, education, and data science | Limited direct purchasing authority but strong influence on tool usage, trial adoption, and future institutional champions | Useful for product awareness, trial usage, academic advocacy, and community building |
| Conference workshop organizers and program committee members | Academic networks, cross-institution research collaborations, specialist conference tracks | Influence visibility, partnership access, and institutional introductions across the ecosystem | Useful for sponsorship, workshop collaboration, and strategic market entry into academic channels |
| University innovation and digital transformation leaders | Higher education administration, research computing, institutional IT, digital strategy offices | Can sponsor pilots, campus-wide deployments, procurement reviews, and collaborative projects | Valuable for enterprise educational AI, governance, cloud, cybersecurity, and compliance offerings |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Tokyo | Local universities, hospitals, research labs, digital health and EdTech professionals | High | Tokyo is a major concentration point for higher education, healthcare institutions, and advanced technology organizations |
| Tokyo Metropolis and Greater Kanto | Regional academic and applied AI attendees from Yokohama, Chiba, Saitama, Tsukuba, and nearby hubs | High | Strong access to research institutions, medical centers, and enterprise technology communities |
| Japan national market | Researchers and practitioners from national universities, clinical institutions, and innovation programs across Japan | Medium to High | The event’s international positioning and Tokyo location support domestic draw beyond the host city |
| Asia-Pacific | Regional researchers and practitioners from East Asia, Southeast Asia, and Oceania | Medium | International conference branding suggests regional academic participation, though country mix is not yet publicly confirmed |
| Global | International submissions and selected attendees from broader research communities | Medium | Global reach is supported by English-language CFP, Springer proceedings, and international workshop lineage |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The event is explicitly positioned as an international conference, accepts global paper submissions, and publishes through Springer CCIS, indicating cross-border research participation. |
| National | Secondary practical reach | Japan-based institutions are likely to form a meaningful share of in-person attendance due to the Tokyo venue and domestic academic concentration. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Waseda University | University / venue host institution | Relevant for university research, AI collaboration, education innovation, and institutional technology outreach. Venue association is provided in the event details supplied. | waseda.jp | Professor, Principal Investigator, Director Research Computing, CIO, Dean, Educational Technology Director | Strong market fit; venue association supplied in event details |
| Microsoft | Technology platform provider | The official site confirms Microsoft CMT was used for peer-review management, indicating a direct conference-service relationship and relevance for AI/cloud discussions. | microsoft.com | Academic Program Manager, Cloud Solutions Architect, AI Specialist, Higher Education Account Executive | Confirmed Sponsor / Service Provider Mention |
| Springer | Academic publishing and proceedings partner | Officially identified as the proceedings publisher for accepted papers, relevant for academic partnership and conference ecosystem targeting. | springer.com | Publishing Editor, Conference Proceedings Manager, Partnerships Manager | Confirmed Sponsor / Service Provider Mention |
| Current-year attendee / buyer organization list | Not publicly released | The provided official materials confirm conference scope, dates, and submission structure, but do not publish a current-year attendee, exhibitor, sponsor, or institutional participant directory. | — | — | Attendance figure not publicly confirmed by the organizer |
Suitable for B2B attendee list building only with caution. This event appears stronger for institutional account-based targeting, speaker/committee mapping, and research partnership outreach than for high-confidence attendee list extraction from public sources.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Professor / Principal Investigator | Research | Director / VP / Individual Contributor | Core decision influencer for research collaborations, tooling, pilot use cases, and grant-supported projects |
| 2 | Director of Research Computing | IT / Research Infrastructure | Director | Owns compute environments, data pipelines, and technical enablement for AI workloads |
| 3 | Chief Information Officer | Information Technology | C-Level | Important for institution-wide AI adoption, governance, systems integration, and budget sponsorship |
| 4 | Director of AI / AI Research Lead | AI / Data Science | Director / Head | Relevant for model development, experimentation, tooling evaluation, and external AI partnerships |
| 5 | Clinical Informatics Director | Clinical Informatics / Digital Health | Director | Key for healthcare-side evaluation of AI-enabled clinical and operational applications |
| 6 | Dean / Associate Dean | Academic Leadership | VP / Director | Can influence strategic priorities, budget support, partnerships, and program adoption |
| 7 | Educational Technology Director | Education Technology / Learning Innovation | Director | Critical for AI learning tools, analytics, personalization, and digital pedagogy adoption |
| 8 | Research Program Manager | Research Administration | Manager | Useful for practical project coordination, trial setup, vendor onboarding, and grant execution |
| 9 | Data Science Manager | Data Science / Analytics | Manager | Important for implementation feasibility, evaluation criteria, and technical buying influence |
| 10 | Head of Digital Health Innovation | Innovation / Digital Health | Director / Head | Relevant for partnerships, pilot deployments, innovation procurement, and translational AI projects |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Higher Education | Direct fit for universities, labs, and academic leadership | Research tools, education AI, data platforms |
| 2 | Research | Matches research institutes and applied science organizations | Partnerships, datasets, AI experimentation environments |
| 3 | Hospital & Health Care | Relevant to digital health and clinical AI participants | Clinical analytics, secure data systems, patient-facing AI |
| 4 | Information Technology & Services | Covers implementation partners and institutional IT organizations | Infrastructure, consulting, integration |
| 5 | Computer Software | Strong fit for AI, analytics, LLM, and learning software vendors or buyers | Model platforms, apps, workflow tools |
| 6 | Education Management | Relevant to institutional learning operations and education administrators | AI-enabled teaching and student support |
| 7 | E-Learning | Aligns with personalized learning and digital education themes | Adaptive learning, tutoring AI, learner analytics |
| 8 | Biotechnology | Relevant where health AI intersects with biomedical data and research | Applied AI research and translational science |
| 9 | Medical Practice | Relevant to clinician-led innovation groups and practice-based AI exploration | Clinical efficiency and decision support |
| 10 | Medical Devices | Relevant where wearables, sensors, and digital health devices are part of AI data flows | Device analytics, monitoring, and connected health |
| 11 | Health, Wellness & Fitness | Fits well-being and personalized health themes referenced on the site | Consumer or institutional wellness AI |
| 12 | Computer Hardware | Applies to compute infrastructure and AI acceleration environments | GPU systems, edge devices, performance environments |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Not confirmed | No public attendance number in the supplied official materials | No estimate stated to avoid unsupported claims |
| Exhibitor count | Not publicly confirmed | Not confirmed | Conference website content reviewed | This appears to be a conference format rather than an exhibition-led event |
| Buyer count | Not publicly confirmed | Not confirmed | No buyer-registration data published in supplied official text | Attendee base is likely research-led rather than formal hosted-buyer structured |
| Speaker count | Not publicly confirmed | Not confirmed | Speaker pages exist but counts were not included in the supplied official text | Keynote and invited speaker sections are present on the website navigation |
| Sponsor count | Not publicly confirmed | Partially evidenced | Microsoft acknowledged for CMT service; broader sponsor list not provided | Do not treat as a complete sponsor directory |
| Historical attendance | No direct prior-year ICAIHE attendance available | Historical / prior-year evidence unavailable | ICAIHE 2026 developed from two prior workshops, but no attendance figures were supplied | Avoid extrapolation without official data |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Artificial Intelligence | Model development, evaluation, deployment, and applied AI use cases | Demo practical workflows for research, teaching, and healthcare analytics | AI platforms, model tooling, MLOps, LLM support |
| Healthcare AI | Digital health innovation, patient data insights, clinical decision support | Position around outcomes, privacy, interoperability, and pilot evidence | Clinical analytics, health data infrastructure, secure AI environments |
| Education AI | Personalized learning, learner analytics, digital instruction support | Show measurable learning enhancement, faculty usability, and governance | Adaptive learning, tutoring AI, analytics dashboards, LMS extensions |
| Big Data | Managing larger research and application datasets | Engage with scalability, governance, and data engineering value | Data lakes, analytics engines, ETL, warehousing |
| IoT, wearables, and sensors | Capture and analyze real-world health and learning signals | Connect device data to actionable insights and applied research | Wearable analytics, edge AI, monitoring platforms |
| Human-computer interaction | Usability, trust, personalization, and adoption in sensitive contexts | Frame solutions around adoption, explainability, and user experience | UX research, explainable AI, interface design tools |
| Ethics and responsible AI | Bias mitigation, transparency, governance, and policy alignment | Lead with compliance, review frameworks, and institutional safeguards | Governance software, audit tooling, policy consulting |
| Cloud and research infrastructure | Reliable compute, storage, collaboration, and reproducibility | Discuss deployment speed, security, scaling, and cost control | Cloud platforms, HPC, storage, collaboration environments |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | Strong fit for suppliers serving higher education, research, digital health, AI infrastructure, and learning innovation. |
| Decision-maker availability | Medium | Academic conferences attract influential experts, but not all attendees hold direct procurement authority. |
| Data collection potential | Medium | Public attendee data appears limited; stronger results likely from committee, speaker, author, and institutional mapping. |
| Apollo targeting potential | High | Good fit for account-based targeting across universities, hospitals, research centers, software providers, and education organizations. |
| Geographic targeting potential | High | Tokyo, Japan, and broader APAC targeting can be combined with global research-sector filters. |
| Best outreach approach | High | Use thought-leadership messaging, research collaboration framing, pilot language, and institution-specific use cases. |
| Overall lead quality | High | Best for specialized, high-value institutional leads rather than volume-based event prospecting. |
| Best use case | High | ABM targeting, partnership development, university and hospital outreach, and speaker/author ecosystem engagement. |
| Limitations / risks | Medium | Public attendee verification is limited, attendance metrics are unconfirmed, and many participants may be academic influencers rather than immediate buyers. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Higher Education; Research; Hospital & Health Care; Information Technology & Services; Computer Software; Education Management; E-Learning; Biotechnology; Medical Practice; Medical Devices | Align targeting with the event’s university, research, health, and AI application base |
| Departments | Research; Information Technology; Engineering; Education; Innovation; Operations | Focus on technical, academic, and implementation stakeholders |
| Seniority | C-Level; VP; Director; Head; Manager; Professor-equivalent where available | Reach both strategic sponsors and day-to-day evaluators |
| Job titles | Chief Information Officer, Director of Research Computing, Director of AI, Professor, Principal Investigator, Clinical Informatics Director, Dean, Educational Technology Director, Research Program Manager, Data Science Manager | Capture the most relevant institutional decision-makers and influencers |
| Geography | Japan; Tokyo; Greater Tokyo Area; APAC; global research-intensive institutions | Prioritize local relevance first, then expand to global academic matches |
| Employee size | 201–500; 501–1,000; 1,001–5,000; 5,001+ | Larger institutions are more likely to support AI pilots and cross-functional projects |
| Keywords | artificial intelligence, digital health, health informatics, clinical AI, personalized learning, educational technology, learning analytics, HCI, big data, wearables, sensors, responsible AI | Improve precision around event-adjacent themes |
| Technologies | AI/ML platforms, cloud infrastructure, analytics tools, collaboration environments | Useful where Apollo enrichment supports technology-based filtering |
| Revenue range | Use selectively; more applicable to commercial tech vendors and private institutions than universities | Avoid over-restricting academic targets |
| Company type | Educational institutions, hospitals, research institutes, software companies, digital health organizations | Supports segmented campaigns by buyer environment |
Suggested Apollo Search Logic: ("artificial intelligence" OR "AI" OR "machine learning" OR "digital health" OR "health informatics" OR "personalized learning" OR "learning analytics" OR "educational technology") AND (university OR hospital OR research OR lab OR institute) AND (Professor OR "Principal Investigator" OR CIO OR "Director of AI" OR "Director of Research Computing" OR "Clinical Informatics Director" OR Dean OR "Educational Technology Director").
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| ICAIHE 2026 Official Website | Official event website | Event name, dates, city, country, conference purpose, audience description, important deadlines, publication information, and historical lineage from prior workshops | High |
| Springer LNCS Guidelines | Official publisher reference linked by event | Publication framework and conference proceedings context referenced by the event site | High |
| User-supplied event details | Provided input | Venue detail: International Conference Center-Waseda Campus, Waseda University. This venue detail was not present in the copied official website text supplied for review. | Medium |
| Verification note | Research limitation | No public current-year attendee list, exhibitor directory, sponsor roster, or confirmed attendance volume was included in the supplied official materials. Conference suitability is therefore strongest for ABM and institutional targeting rather than event list certainty. | High |
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