High Intelligent Tutoring Systems in Higher Education: Ethical, Regulatory and Pedagogical Foundations for Academic Success

The handbook presents the conceptual, regulatory, and practical framework needed to understand and design High Intelligent Tutoring Systems (HITS) as key tools for academic success in higher education. Drawing on a review of the state of the art on Intelligent Tutoring Systems, their historical evolution, and their applications in learning personalization, dropout prevention, academic and career guidance, and performance analytics, the document shows how AI can transform teaching, assessment, and tutoring processes across formal, non-formal, and informal education. It also analyses major regulatory and ethical frameworks (UNESCO, OECD, EU, FATE, EU AI Act), concerns about bias, transparency, privacy, and user acceptance, as well as real case studies (MATHia, Panther Retention Grant, Brightspace Insights, Course Signals, IBM Watson, among others) that illustrate effective uses and implementation challenges. Based on focus groups and questionnaires with students, professors, and tutors, it identifies needs and priorities (personalization, effective assessment, workload reduction, system integration, accessibility and inclusion) and proposes recommendations for a user-centred, ethical, and equitable implementation of HITS aligned with continuous quality improvement in education.