🏷️ Concept
AI in Higher Education
AI in Higher Education — the integration of artificial intelligence into university teaching, learning, assessment, and administration. Higher education is the most-studied context in the wiki, with over 100 articles examining how AI transforms college-level instruction, institutional policy, and student experience.
AI in higher education research spans every function of the university: from AI tutoring and automated grading to faculty development, academic integrity, institutional governance, and student support. The wiki's higher education articles cluster around several key themes.
Institutional transformation
Institutional change frameworks analyze how universities adapt to AI — not just at the classroom level but across policy, governance, and organizational structure. The EPIQ-AI framework reframes faculty readiness as a sociotechnical alignment challenge involving epistemic, pedagogical, institutional, and quality domains. Rethinking universities in the AI era examines whether current institutional models can accommodate AI-driven education.
Student experience at scale
Large-scale studies of authentic student AI use and GenAI availability and satisfaction document how students actually use AI — revealing gaps between institutional policy and everyday practice. Workforce preparation surveys connect AI use in higher education to employment outcomes.
Faculty and teaching
Faculty Development research examines how instructors adopt, resist, or adapt to AI. Teacher AI adoption studies identify confidence, support, and attitude as key predictors. AI-assisted discretionary feedback research explores whether AI increases instructor feedback quality and quantity.
Assessment and integrity
Academic Integrity and AI assessment reform research grapple with how universities should redesign evaluation for an AI-capable student body. Detection-centered approaches are giving way to Authentic Assessment and process-based evaluation.