At CALST (Co-study Group on Advanced Learning Science and Technology), we view learning as one of the most profound intellectual activities in human life. Our mission is to advance this critical intellectual endeavor through innovative research in artificial intelligence and beyond, focusing on the modeling and systematization of learning processes.
Mission Statement
Encourage In-depth Discussion: To encourage logical and in-depth discussion of the complexities of learning as an intellectual activity, and to promote wide-ranging debates within advanced learning science and technology, free from conventional constraints and excuses.
Enhancing Research Skills: To assist members in refining their research skills, emphasizing presentations and feedback that push the boundaries of conventional knowledge, with the goal of developing groundbreaking contributions to the field.
Facilitating Knowledge Exchange: To act as a nexus for the exchange of insights, methodologies, and research findings from diverse fields, understanding learning as both a pervasive activity in human endeavor and a phenomenon uniquely shaped by individual contexts and experiences.
Foster Collaboration: To cultivate a collaborative atmosphere in which the integration of perspectives from fields such as information technology, cognitive science, educational engineering, and practical educational methodologies is essential to a comprehensive understanding of learning.
Support Career Development: To provide a supportive network that navigates the broad and interdisciplinary implications of learning research across academia and industry, enabling members to confidently explore their career trajectories.
Research Topics
Recognizing the inherent complexity and multifaceted nature of learning, CALST embraces discussions on a wide range of research topics that reflect our interdisciplinary ethos. These include research on:
Artificial Intelligence in Education: Investigating how AI can customize and enrich learning experiences by adapting to the diverse natural learning processes of individuals.
Knowledge Engineering for Learning: Developing tools and technologies—drawing on advances in knowledge engineering and applied ontology—to support and enhance learning through intelligent tutoring systems and adaptive learning environments.
Learning Sciences and Engineering: Creating ecosystems that leverage findings from the learning sciences to generate hypotheses, design educational systems, and improve learning outcomes through empirical testing.
Applications of Cognitive Science: Applying cognitive science theories to analyze and improve fundamental learning processes, with attention to the variability of learning across different contexts and experiences.
Technology-Enhanced Learning: Examining how technologies such as virtual and augmented reality, mobile platforms, and online collaboration tools can transform educational experiences and outcomes.
Human-Computer Interaction in Education: Designing educational technologies that prioritize effective and intuitive interaction, enhancing the learning experience through user-centered design.
Learning Analytics and Educational Data Mining: Using data analytics and machine learning to derive actionable insights from educational data, aiming to improve teaching practices and understand the diverse characteristics of learners and learning environments.
Through discussions related to these topics, CALST aims to encourage our members to cultivate innovative ideas and perspectives, fostering a rich, dynamic, and evolving environment of discussion and discovery.