Artificial intelligence in educational technology, focusing on AI across educational settings. Foundational concepts of AI and machine learning. Theoretical perspectives, historical development, current state, and future potential of AI for learning. Application of AI across P-12, higher education, and other educational settings. Ethical considerations, privacy protection, and responsible AI use.
Athena Title
AI in Educational Technology
Equivalent Courses
Not open to students with credit in EDIT 9400E
Grading System
A - F (Traditional)
Student learning Outcomes
Students will describe foundational concepts and historical development of AI and machine learning in educational technology.
Students will analyze the historical development, key terms, concepts, and promising applications of AI and machine learning in educational technology, including the use of AI tools for teaching and learning.
Students will critically review the foundations of generative AI and ChatGPT, explain the types of data outputs they produce, and evaluate their applications in educational technology.
Students will develop the skills to analyze and apply AI and machine learning educational technologies in various educational contexts, including P-12, higher education, learning design, etc.
Students will develop familiarity with the current state of research in AI in educational technology and critically evaluate and analyze research findings in this area.
Students will explore and implement AI and machine learning applications in P-12 and higher education, focusing on subject-specific applications, productivity enhancements through prompt engineering, assignment redesigns, student engagement, individualized feedback, and educational data analysis.
Students will explore and address ethical compliance and risk aspects of AI in educational technology, such as fairness, transparency, and privacy protection, as well as unique perspectives on AI, including human-centered AI, explainable AI, and AI education for young children.
Topical Outline
Historical, foundational, and theoretical perspectives on AI in educational technology
Evolution from early educational computing to current AI applications
Conceptual and theoretical underpinnings
Generative AI and large language models in educational technology
Capabilities, constraints, and affordances
Prompt engineering and content generation
Implications and risks for educational technology
AI as an educational technology to support teaching and learning in P-12 and higher education contexts
Planning, assessment, and feedback automation
Personalized learning and adaptive content delivery
Academic integrity, plagiarism detection, and ethical classroom use
Critical engagement with AI tools and platforms in educational technology
Selection, demonstration, and critical evaluation
Integration strategies for teaching, learning, and research
Ethical, compliance, and governance issues in AI for educational technology
Fairness, bias, and transparency
Privacy protection and data stewardship
Risk assessment and regulatory frameworks
Design and implementation of AI-enhanced learning experiences
Human-centered AI in educational technology
Adaptive and personalized learning materials
AI for accessibility and compliance
