Workshop 1: 2025 Principle and practice of data and Knowledge Acquisition Workshop
The PKAW workshop series has been an integral part of PRICAI for nearly two decades.
PKAW has provided a forum for researchers and practitioners to discuss the state-of-the-art in the area of knowledge acquisition and machine intelligence (MI, also Artificial Intelligence, AI). PKAW 2025 will continue the above focus and welcome the contributions to the multi-disciplinary approach of human and big data-driven knowledge acquisition and AI techniques and applications.
Organizers:
Dr. Shiqing Wu, City University of Macau, Macao SAR (
Dr. Weihua Li, Auckland University of Technology, New Zealand (
Website:
Workshop 2: The 2nd International Workshop on Educational Artificial Intelligence (IWEAI 2025)
The 2nd International Workshop on Educational Artificial Intelligence (IWEAI 2025) is dedicated to exploring the transformative impact of AI in education. We aim to bring together leading researchers, educators, and technologists to discuss the latest advancements, ethical considerations, and practical applications of AI in educational settings.
The IWEAI 2025 workshop strongly aligns with the overarching theme of advancing AI technology and its practical applications, as emphasized by PRICAI 2025. Building on the success of IWEAI 2024 at PRICAI 2024, IWEAI 2025 will continue to serve as a valuable complement to PRICAI 2025, offering unique insights into the pivotal role of AI in transforming education while aligning with PRICAI's broader mission of advancing AI technology and its applications.
Organizers:
Prof. Yuncheng Jiang, South China Normal University (
Prof. Gang Li, Deakin University
Workshop 3: HIDDEN-RAD: Unlocking Causal Explanations in Medical AI and Beyond
As general AI reasoning capabilities continue to advance, it remains a critical challenge to develop systems capable of articulating hidden causal reasoning and generating accurate explanations in specialized domains—a crucial aspect for building trustworthy AI. The NTCIR-18 HIDDEN-RAD challenge serves as a valuable case study in this area, focusing specifically on radiology. Building on this foundation, our workshop invites research contributions addressing causal reasoning, explanation generation, and hallucination detection across various domains. We aim to bring together researchers working on explanation frameworks, domain-specific knowledge integration, evaluation methodologies, and cross-domain applications. By sharing insights and methodologies across fields, we seek to advance the state-of-the-art in creating AI systems that can not only reach accurate conclusions but explicitly explain their reasoning in ways that domain experts find meaningful and trustworthy.
Organizers:
Prof. Key-Sun Choi, Konyang University, South Korea (
YoungGyun Hahm, Teddysum Inc., South Korea
You-Sang Cho, Konyang University, South Korea
Jin-Dong Kim, Database Center for Life Science, Japan
Workshop 4: Quantum Computing for Search and Optimization Problems
In the last decade, the use of quantum computers for solving combinatorial search and optimization problems has attracted increasing interest from both the quantum community and the AI community, as many problems in AI can be formalized as combinatorial search problems. This amounts to creating of a new research field: "quantum optimization". Topics include, but are not limited to:
- Design and development of quantum algorithms, quantum-inspired algorithms, and hybrid quantum-classical algorithms for optimization problems and AI search problems
- Theoretical analysis of quantum algorithms for optimization and AI search problems
- Problem representation in QUBO (Quadratic Unconstrained Binary Optimization)
- Applications of Quantum Annealing and Quantum Approximate Optimization Algorithm (QAOA) to optimization and AI search problems
- Quantum optimization for computer vision
- Decomposition of large-scale optimization and search problems for quantum hardware
- Benchmarking and performance comparison between different architectures
Organizers:
Prof. Philippe Codognet, Sorbonne University, Japan (
Prof Cristian Calude, the University of Auckland, New Zealand
A\Prof. Patrice Delmas, the University of Auckland, New Zealand
Dr Michael Dinneen, the University of Auckland, New Zealand
Workshop 5: The 7th International Workshop on Democracy and AI
Recent advancements in machine learning, natural language processing, and multi-agent systems have significantly increased the reach and influence of artificial intelligence (AI) in our everyday lives. AI-driven technologies are reshaping how we process, monitor, and manage information and services—with implications for evidence-based policy planning, decision-making, and public service delivery. Conversational AI tools, for instance, show great promise in enhancing democratic engagement by enabling scalable, inclusive, and participatory civic processes. These systems can help tailor public services, connect citizen ideas, and foster greater social inclusion. However, alongside these opportunities come significant risks. One of the central concerns is the lack of accountability in AI-generated content and decisions, which may profoundly impact individuals and communities by spreading misinformation or making opaque decisions. This workshop invites contributions that explore the current and future roles of AI in democratic contexts. We aim to foster a critical dialogue on how to harness AI for democratic good, while addressing the ethical, technical, and societal challenges it brings.
Organizers:
Asst.Prof. Jawad Haqbeen, Kyoto University, Japan (
A\Prof.Rafik Hadfi, Kyoto University, Japan(
Prof. Takayuki Ito, Kyoto University, Japan(
Asst.Prof. Anastasija Nikiforova, University of Tartu, Estonia(
Prof. Tokuro Matsuo, Advanced Institute of Industrial Technology, Japan (
Workshop 6: Workshop on Artificial Intelligence for Upstream Petroleum Industry (AI4UPI)
Artificial Intelligence (AI) is fundamentally transforming the upstream oil and gas sector by enabling intelligent, efficient, and increasingly autonomous exploration and production. With the rise of large language models (LLMs) and vision models (LVMs), and multimodal AI systems, the industry is entering a new era of digital intelligence—where structured and unstructured data from logs, reports, images, and sensors are processed at scale to drive real-time decision-making across the entire upstream lifecycle.
This workshop highlights the most recent advancements and applications of AI in upstream domains including reservoir characterization, drilling and completion design, production optimization, and sustainability enhancement. Special emphasis is placed on the use of large models to automate workflows, improve well performance predictions, and enhance flow assurance and equipment reliability.
In addition, the workshop explores how AI contributes to economic evaluation and cost estimation in well construction and reservoir transformation, ensuring that digital solutions not only enhance technical outcomes, but also deliver quantifiable business value. As the industry pursues greener and more responsible development, AI plays a crucial role in reducing carbon footprints through methane detection, energy efficiency optimization, and CO₂ storage modeling.
Beyond the oil and gas domain, the workshop embraces the growing role of AI in renewable and low-carbon energy systems. With global energy systems undergoing unprecedented transitions, AI offers powerful tools to accelerate the adoption of wind, solar, geothermal, and hydrogen technologies. Applications include improved site selection, predictive maintenance of renewables infrastructure, energy yield forecasting, and smart, optimized designs and operational control.
The workshop also welcomes research that integrates AI for cross-domain optimization between traditional and renewable energy sources—such as hybrid energy systems, AI-guided energy storage strategies, and intelligent demand-side management. These innovations are particularly relevant to regions like Aotearoa New Zealand, where environmental sustainability, biodiversity conservation, and climate resilience are national priorities.
By bringing together researchers, engineers, and industry practitioners across sectors, this workshop aims to foster cross-disciplinary collaboration and accelerate the intelligent, sustainable, and decarbonized development of the global energy ecosystem.
Organizers:
Dr Feng Deng, Research Institute of Petroleum Exploration & Development, China National Petroleum Company (
Prof Alan Brent, Victoria University of Wellington, New Zealand
Dr Yili Ren, Research Institute of Petroleum Exploration & Development, China National Petroleum Company
Workshop 7: Workshop on Intelligent Marine Technology
The Workshop on Intelligent Marine Technology aims to bring together researchers and practitioners working at the intersection of artificial intelligence and marine science. The focus is on novel AI methodologies for marine robotics, ocean data analysis, underwater communication, and autonomous maritime systems. With increasing attention on ocean sustainability and exploration, intelligent technologies play a key role in enhancing operational efficiency, decision-making, and autonomous control in challenging underwater environments. This workshop aligns with PRICAI 2025's focus on practical AI applications across diverse domains.
Organizers:
Prof. Gai-Ge Wang, Ocean University of China (
Dr. Qi Chen, Victoria University of Wellington
Prof. Junyu Dong, Ocean University of China
Workshop 8: Representation Learning and Clustering (RLC'25)
Data clustering and representation learning play an indispensable role in data science. They are very useful to explore massive data in many fields, including information retrieval, natural language processing, bioinformatics, recommender systems and computer vision. Despite their success, most existing clustering methods are severely challenged by the data generated by modern applications, which are typically high dimensional, noisy, heterogeneous and sparse or even collected from multiple sources or represented by multiple views where each describes a perspective of the data. This has driven many researchers to investigate new clustering models to overcome these difficulties. One promising category of such models relies on representation learning. Indeed, learning a good data representation is crucial for clustering algorithms and combining the two tasks is a common way of exploring this type of data. The idea is to embed the original data into a low dimensional latent space and then perform clustering on this new space. Both tasks can be carried out sequentially or jointly; combining the two tasks is a common way of exploring this type of data. Hence, one main goal of the workshop is to bring together the leading researchers who work on state-of-the-art deep unsupervised feature extraction and clustering models, and also the practitioners who seek novel applications. In summary, this workshop is an opportunity to:
- Present the recent advances in representation learning and clustering methods including multi-view clustering and semi-supervised learning which are not explored well.
- Outline potential applications that could inspire new approaches.
- Evaluate the effectiveness of new clustering models compared to classical approaches in terms of interpretability of clusters and Scalability.
Organizers:
Lazhar Labiod, Université Paris Cité (
Prof Mohamed Nadif, Université Paris Cité (