Keywords
Large language models
Educational technology
Pedagogical integration
Computational semantics
Teacher training
How to Cite
Abstract
Purpose: This glossary serves as a comprehensive terminological resource to understand generative artificial intelligence (AI) in educational contexts. It aims to bridge the gap between technical AI concepts and pedagogical practices, fostering an informed dialogue among educators, researchers, policymakers, and students.
Design/Methodology/Approach: The glossary was developed through a mixed-methods approach combining a systematic literature review (following PRISMA guidelines), natural language processing techniques (e.g., term frequency analysis, semantic clustering), and a Delphi validation process with multidisciplinary experts. Terms were selected based on pedagogical relevance, conceptual clarity, and frequency of use in educational AI discourse.
Findings: The resulting glossary offers operational definitions of key terms associated with generative AI—especially large language models (LLMs)—from the perspective of digital pedagogy, computational semantics, and cognitive sciences. It includes evolving concepts, application examples, and cross-referenced terms to support integration in teacher education and curriculum design.
Practical Implications: This glossary provides a foundational vocabulary for designing educational programs, professional development initiatives, and policy guidelines. It also supports AI literacy by enhancing educators’ critical understanding and ethical application of emerging technologies in teaching and learning environments.
Originality/Value: By aligning technical AI terminology with pedagogical frameworks, this glossary promotes the responsible and effective integration of generative AI in education. It addresses the urgent need for accessible, validated resources to empower educators and institutions in navigating the fast-evolving landscape of AI-enhanced education.
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