CALL FOR PAPERS

The Conference on Computational Innovation and Social Systems (CISS) invites researchers, scholars, industry professionals, and policymakers to submit original and high-quality work that explores the evolving intersection between computational science and societal transformation. CISS provides a dynamic forum to showcase innovative technologies, analytical models, and interdisciplinary approaches that leverage computing for meaningful social impact. The conference emphasizes responsible innovation, scientific rigor, and scalable solutions to address complex real-world challenges.

As computing systems increasingly underpin governance, communication, health, mobility, education, and community well-being, CISS aims to bring together diverse perspectives to examine how advanced computational methods can shape more resilient, inclusive, and intelligent societies. We welcome submissions spanning theoretical contributions, applied research, case studies, system prototypes, and policy analyses.

Core Topics of Interest

Authors are invited to submit papers related to, but not limited to, the following areas:

1. AI-Driven Social and Organizational Systems

2. Computational Modeling and Simulation of Societal Dynamics

3. Digital Governance, Policy, and Ethics

4. Smart Cities and Intelligent Infrastructure

5. High-Performance and Cloud Computing for Societal Applications

6. Cybersecurity, Resilience, and Trustworthy Computing

7. Human–Computer Interaction and Inclusive Technologies

8. Social Data Analytics and Computational Social Science

9. Sustainable and Green Computing Ecosystems

We will also consider the following areas of research:

Education-Focused Areas of Study for CISS:

1. Computational Learning Sciences

Investigating how computational models, AI tutors, simulations, and data-driven platforms enhance learning processes and cognitive development.

2. Digital and AI-Enabled Education Systems

Examining adaptive learning platforms, intelligent feedback systems, VR/AR classrooms, and algorithmic personalization in formal and informal learning environments.

3. Learning Analytics and Educational Data Science

Using big data, predictive analytics, and machine learning to analyze student behavior, improve instruction, and support evidence-based decision-making.

4. Educational Technology Innovation and Design

Designing, evaluating, and deploying digital tools, educational apps, and interactive systems to improve engagement and learning outcomes.

5. Computational Approaches to Assessment and Evaluation

AI-supported assessment, automated scoring, data-informed evaluation frameworks, and real-time performance analytics.

6. Digital Equity, Access, and Inclusion in Education

Exploring disparities in digital access, algorithmic bias in educational tools, and the development of inclusive technological ecosystems for learning.

7. Policy, Ethics, and Governance in Technology-Driven Education

Analyzing the ethical use of educational data, AI governance frameworks, digital privacy, and responsible innovation in learning systems.

8. Online, Hybrid, and Virtual Learning Ecosystems

Research on scalable digital learning infrastructures, remote instruction models, and the sociotechnical dynamics of virtual education communities.

9. Human–Computer Interaction for Learning

Studying interface design, usability, learner experience, and interaction models that shape digital learning environments.

10. Computational Support for Teacher Education and Professional Development

Using analytics, automation, and digital platforms to support teacher training, performance evaluation, and continual professional growth.

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