Artificial Intelligence Applications in Educational Learning and Assessment
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Beschreibung
This volume gives an overview on how Artificial Intelligence (AI) is transforming educational learning and assessment by addressing challenges in test development, administration, scoring, and student learning progression. AI integrates advanced technologies into teaching and evaluation, enhancing learning experiences, personalizing education, and streamlining assessments through data-driven algorithms and machine learning techniques. AI-powered systems adapt to individual learners, provide real-time feedback, and automate administrative tasks like grading.
Applications of AI in education include automated item generation (AIG), natural language processing (NLP), learning analytics, adaptive learning, intelligent tutoring systems, and automated scoring. Recently, generative AI has enabled large-scale test item development, such as "the item factory," which automates item generation, quality assurance, and crowdsourcing techniques for adaptive testing. AI enhances assessment accuracy by reducing human biases and errors while enabling personalized learning paths that cater to different student needs and learning styles. It empowers educators with data-driven insights, improving instructional strategies and early intervention for struggling students. Additionally, AI's scalability allows for the efficient delivery of personalized education without increasing teaching staff.
However, ethical concerns, including bias, data privacy, and equitable access are also carefully addressed. Rather than replacing educators, AI should serve as a collaborative tool that enhances teaching and learning, fostering a more inclusive, effective, and student-centered education system.
This book is useful for graduate students, researchers and education policy makers.
This volume gives an overview on how Artificial Intelligence (AI) is transforming educational learning and assessment by addressing challenges in test development, administration, scoring, and student learning progression. AI integrates advanced technologies into teaching and evaluation, enhancing learning experiences, personalizing education, and streamlining assessments through data-driven algorithms and machine learning techniques. AI-powered systems adapt to individual learners, provide real-time feedback, and automate administrative tasks like grading.
Applications of AI in education include automated item generation (AIG), natural language processing (NLP), learning analytics, adaptive learning, intelligent tutoring systems, and automated scoring. Recently, generative AI has enabled large-scale test item development, such as "the item factory," which automates item generation, quality assurance, and crowdsourcing techniques for adaptive testing. AI enhances assessment accuracy by reducing human biases and errors while enabling personalized learning paths that cater to different student needs and learning styles. It empowers educators with data-driven insights, improving instructional strategies and early intervention for struggling students. Additionally, AI's scalability allows for the efficient delivery of personalized education without increasing teaching staff.
However, ethical concerns, including bias, data privacy, and equitable access are also carefully addressed. Rather than replacing educators, AI should serve as a collaborative tool that enhances teaching and learning, fostering a more inclusive, effective, and student-centered education system.
This book is useful for graduate students, researchers and education policy makers.
Gives an overview of how Artificial Intelligence is transforming educational learning Shows how AI applications empower educators and education policy makers with data-driven insights Discusses ethical concerns, including bias, data privacy, and equitable access
Autor*in
Alina A. von Davier
Themen in »Artificial Intelligence Applications in Educational Learning and Assessment«
Large Language Models AI in Education Automated Item Generation Automated Scoring Learning Analytics Supervised and unsupervised learning Agentic AI Large Language Models (LLMs) Automated Item Generation (AIG) Automated Scoring (AS) Personalized Learning Adaptive Learning Adaptive Assessment Metacognition Self-regulation