Topics of Interest, but are not limited to
Authors are invited to submit complete, original, unpublished research papers to the 1st International Conference on Artificial Intelligence in Engineering and Applied Science (ICAIEAS2026). The papers may address theoretical, methodological, or practical aspects of Computer Science and Engineering and should revolve around the conference theme.
The conference topic includes the following (but not limited to) tracks:
Track 1: Applied Science
The Applied Science track focuses on the use of Artificial Intelligence to advance practical scientific applications and solve real-world problems. This track welcomes interdisciplinary research that applies AI methods to scientific investigation, experimentation, and innovation. Submissions should demonstrate how AI techniques enhance scientific processes, analysis, or outcomes.
Suitable submissions include, but are not limited to:
- AI applications in environmental and sustainability science
- AI-driven modeling and scientific simulation
- Data science and computational methods in applied research
- Smart systems and IoT in scientific applications
- Materials science and AI-assisted discovery
- Renewable energy optimization using AI
Authors should submit to this track if their work primarily applies AI to scientific research or practical scientific challenges.
Track 2: Education
The Education track explores the integration of Artificial Intelligence into teaching, learning, and educational systems. It welcomes research on AI-enhanced educational tools, learning environments, and pedagogical innovations. Papers may address both theoretical frameworks and practical implementations of AI in education. To position Artificial Intelligence in Education (AIED) within an Engineering & Applied Science conference theme, the key is to frame education as an engineered system that can be modeled, optimized, simulated, monitored, and controlled using AI and engineering principles.
Suitable submissions include, but are not limited to:
- AI-powered learning platforms and intelligent tutoring systems
- Learning analytics and adaptive education
- AI in STEM and engineering education
- Educational data mining
- Virtual and augmented reality in learning
- Curriculum innovation supported by AI
- AI-Assisted Teaching of Reinforced Concrete Beam Design Using Machine Learning Models
- Predicting Flexural Behavior of RC Beams Using AI as a Teaching Tool in Structural Engineering Courses
- Machine Learning-Based Learning Platforms for ACI-318-19 Design Education
- AI-Driven Visualization of Failure Modes in Reinforced Concrete Structures
- Artificial Intelligence-Assisted Learning in Biomedical Engineering Education
- Application of Machine Learning Tools in Teaching Biomedical Signal Processing
- AI-Based Educational Platforms for Medical Device Engineering Training
- Intelligent Tutoring Systems for Biomedical Engineering Students
- AI-Driven Personalized Learning in Biomedical Engineering Programs
- Teaching ECG Signal Analysis Using Machine Learning Models
- Artificial Intelligence-Based Learning for Biomedical Data Interpretation
- AI-Assisted Educational Tools for Physiological Signal Processing
- AI-Based Virtual Patient Simulation in Health Engineering Education
- Machine Learning Applications in Biomedical Engineering Training
- Artificial Intelligence for Teaching Health Monitoring Systems
- AI-Assisted Learning in Medical Device Engineering Courses
- Machine Learning Applications in Teaching Health Monitoring Systems
Authors should submit to this track if their research focuses on improving education or learning processes using AI technologies in a multidisciplinary context.
Track 3: Engineering
The Engineering track covers AI-driven advancements in engineering design, development, and industrial applications. It welcomes contributions that demonstrate how AI enhances engineering systems, processes, and innovation across disciplines.
Suitable submissions include, but are not limited to:
- AI in robotics and automation
- Smart manufacturing and Industry 4.0
- AI for civil, mechanical, and electrical engineering systems
- Predictive maintenance and optimization
- Autonomous systems and intelligent control
- Engineering design supported by machine learning
Authors should submit to this track if their work primarily contributes to engineering solutions or technologies powered by AI.
Track 4: Health
The Health track focuses on the application of Artificial Intelligence to healthcare and health sciences. It encourages interdisciplinary research that improves healthcare delivery, diagnostics, and patient outcomes through AI technologies.
Suitable submissions include, but are not limited to:
- AI in medical imaging and diagnostics
- Health informatics and clinical data analytics
- Telemedicine and digital health solutions
- Wearable and smart health monitoring systems
- Biomedical engineering supported by AI
- AI for public health and disease prediction
Authors should submit to this track if their research applies AI to healthcare systems, medical technologies, or health sciences.