T1. INTELLIGENT TUTORING AND ADAPTIVE LEARNING AGENTS
SCOPE:
Core ITS research on agentic, adaptive, and generative tutoring systems that reason, teach, and collaborate with human learners. Focus on cognitive fidelity, explainability, and evidence-based design
A. AI FOR EDUCATION: TUTORS, COACHES & ORCHESTRATORS
Intelligent tutoring systems; adaptive dialogue systems; generative AI tutors; large language models for education; multimodal learner feedback; affect-aware scaffolding; student modeling; context-aware pedagogy; cognitive diagnosis; dynamic learner profiling; teacher–AI orchestration; personalized curriculum design; interactive coaching agents; metacognitive prompting; automated feedback explanation; self-regulation; co-teaching systems; pedagogical reasoning engines; hybrid classroom orchestration; learning companion agents; social learning; formative assessment automation; explainable adaptive systems.
FOCUS: AI systems that directly teach, guide, or co-learn with humans through adaptive, generative, and multimodal intelligence.
B. EDUCATION FOR AI: TUTOR DESIGN SCIENCE & EVIDENCE
Learning science integration; instructional design frameworks; education for AI literacy; evidence-based pedagogy; experimental validation; usability testing; cognitive load analysis; human-subject experimentation; open educational datasets; benchmark construction; model interpretability studies; mixed-methods evaluation; longitudinal classroom research; educational psychology; psychometric alignment; reliability and validity testing; user-centered ITS design; ethical experimentation; open-science practices; replication protocols; teacher training for AI integration.
FOCUS: empirical, methodological, and theoretical research about how educational science informs ITS and AI design, ensuring scientific rigor, replicability, and ethical grounding.