Call for Papers-SI on Explainable and Trustworthy AI 2026–2027

Special Issue on Explainable and Trustworthy AI for Intelligent Engineering Systems and Sustainable Innovation in Resource-Constrained Environments

Aims and Scope

Artificial Intelligence (AI) is transforming engineering, infrastructure, transportation, energy, manufacturing, education, healthcare, and other technology-driven domains. As AI systems become increasingly autonomous and complex, ensuring their explainability, transparency, robustness, fairness, safety, privacy, and accountability has become essential for their responsible deployment.

These challenges are particularly significant in resource-constrained environments, where limited computing resources, connectivity, data availability, infrastructure, energy, and technical capacity may affect the deployment and sustainability of intelligent systems.

This Special Issue of the Canadian Journal of Artificial Intelligence for Learning and Engineering Innovation (CJAILEI) invites high-quality research on the design, development, evaluation, and deployment of explainable, trustworthy, sustainable, and resource-efficient AI for engineering and related applications. Particular attention is given to approaches that combine scientific and technological innovation with reliability, human oversight, sustainability, and applicability in real-world and resource-constrained environments.

Guest Editors

Special Issue Information

Topics of interest include, but are not limited to:

• Explainable and interpretable Artificial Intelligence (XAI)
• Trustworthy, responsible, and human-centered AI
• Robustness, fairness, privacy, security, and AI safety
• AI-enabled engineering and Cyber-Physical Systems
• Intelligent transportation systems, smart cities, and smart infrastructures
• Digital Twins and AI-enabled simulation
• Generative AI, Large Language Models, RAG, and agentic AI for engineering
• Knowledge graphs, ontologies, neuro-symbolic AI, and knowledge-based systems
• Frugal AI, Edge AI, TinyML, Green AI, and resource-efficient machine learning
• AI for sustainable engineering, energy, manufacturing, healthcare, agriculture, and resilient infrastructure
• AI for engineering education, intelligent tutoring, learning analytics, and virtual laboratories
• AI adoption in emerging economies and resource-constrained environments

Original research articles, review articles, methodological contributions, experimental and simulation studies, case studies, system architectures, and substantial perspective papers are welcome. All submissions should demonstrate a clear scientific contribution, methodological rigor, and relevance to the scope of the Special Issue.

Manuscript Submission Information

Authors are invited to prepare their manuscripts according to the CJAILEI Author Guidelines and submit their papers through the journal's online submission system. Manuscripts should clearly demonstrate their relevance to the Special Issue and provide appropriate methodological validation.

Manuscripts must be original, must not have been previously published, and must not be simultaneously submitted to another journal. All submissions will undergo the journal's established peer-review process.

Important Dates

Call for Papers: August 2026

Optional Abstract Submission: September 30, 2026

Full Paper Submission: December 15, 2026

First Review Decisions: February 2027

Revised Manuscripts: March 2027

Final Decisions: April 2027

Publication of Special Issue: June 2027

Publication and Open Access

CJAILEI is an international, peer-reviewed, open-access journal focusing on artificial intelligence, learning, engineering innovation, intelligent systems, and sustainable technological development.

No Article Processing Charges (APCs) are charged until January 31, 2027. According to the journal's current policy, submission, peer review, editorial processing, and publication are free of charge to authors during this period.

Contact

For inquiries regarding this Special Issue, please contact the CJAILEI Editorial Office: cjailei@cuspidescience.com.