Education Sciences–Pedagogy

• Learning science & educational psychology (motivation, self-regulation, cognitive load, feedback).
• Curriculum & instruction (lesson design, task complexity, scaffolding, inquiry / problem-based learning).
• Assessment & evaluation (formative/summative, rubrics, performance assessment, validity/reliability).
• Language & literacy pedagogy (reading/writing, EFL/ESL, academic writing, plain-language pedagogy).
• Inclusive education & UDL (differentiation, accommodations, accessibility practices).
• Teacher education & professional learning (mentoring, practicum supervision, reflective practice).
• Design-Based Research (DBR) and intervention studies focused on classroom change.

Interfaces with Management & Governance

• Educational leadership and change management tied to pedagogy and student success.
• Quality assurance and accreditation practices (learning outcomes assessment, curriculum review cycles, program evaluation).
• Governance of curriculum (program design, mapping to competencies/standards).
• Institutional research: analytics for improvement (not surveillance); evidence-based decision making.
• Policy design and evaluation affecting classroom/assessment practices.
• Resource allocation and scheduling insofar as they impact instructional quality and equity (e.g., class size, contact hours, lab access).
• Professional standards, ethics, and compliance in teaching/assessment.

Artificial Intelligence in Education (AIED)

• LLM-supported tutoring/writing, feedback generation, adaptive/personalized learning, knowledge tracing, mastery learning, multimodal learning with AI.
• Assessment redesign (authenticity-by-design, oral/performance tasks), AI-aided scoring with validity evidence, academic integrity in AI-rich contexts.
• Coplanning/coteaching with AI, lesson design assistants, formative analytics dashboards, workload-relief tools; effects on teacher judgment and professional growth.
• Simulation/VR with generative agents, competency frameworks, micro-credentials, transfer-of-training outcomes, safety-critical training evaluation.
• Effectiveness/efficacy studies, learning analytics for AIED evaluation, psychometrics (instrument development/validation; DIF), replication studies, systematic reviews/meta-analyses, benchmark studies linked to learning outcomes.
• QA/accreditation with AI, early-alert & student-success systems, RIMS/CRIS + knowledge graphs supporting curriculum and research-informed teaching, procurement standards, risk/impact assessments.
• Privacy/security (e.g., de-identification, federated learning), bias/fairness audits, accessibility/UDL, multilingual/low-resource contexts, compute/cost sustainability and green AI.