Artificial Intelligence (AI) is revolutionizing higher education by enabling personalized learning and adaptive assessment. AI-driven personalized learning tailors educational content to individual students, enhancing engagement and improving learning outcomes. Simultaneously, adaptive assessment leverages AI to dynamically adjust evaluations based on students’ progress, ensuring fair and effective measurement of competencies. This chapter explores the transformative impact of AI on pedagogy in higher education, focusing on its applications, benefits, and challenges. It discusses how AI-powered learning analytics, intelligent tutoring systems, and automated feedback mechanisms contribute to a more student-centered educational approach. Besides that, ethical considerations, data privacy, and the role of educators in an AI-enhanced learning environment are examined. By integrating AI into personalized learning and adaptive assessment, universities can enhance teaching effectiveness and foster more inclusive and efficient learning experiences. This chapter provides insights into best practices and future directions for AI implementation in higher education pedagogy.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

AI-Driven Personalized Learning and Adaptive Assessment: Transforming Higher Education Pedagogy

  • Adiyono,
  • Mahyudin Ritonga,
  • Sukarno,
  • Kukuh Wurdianto,
  • Ali Said Al Matari

摘要

Artificial Intelligence (AI) is revolutionizing higher education by enabling personalized learning and adaptive assessment. AI-driven personalized learning tailors educational content to individual students, enhancing engagement and improving learning outcomes. Simultaneously, adaptive assessment leverages AI to dynamically adjust evaluations based on students’ progress, ensuring fair and effective measurement of competencies. This chapter explores the transformative impact of AI on pedagogy in higher education, focusing on its applications, benefits, and challenges. It discusses how AI-powered learning analytics, intelligent tutoring systems, and automated feedback mechanisms contribute to a more student-centered educational approach. Besides that, ethical considerations, data privacy, and the role of educators in an AI-enhanced learning environment are examined. By integrating AI into personalized learning and adaptive assessment, universities can enhance teaching effectiveness and foster more inclusive and efficient learning experiences. This chapter provides insights into best practices and future directions for AI implementation in higher education pedagogy.