Glossary of Generative Artificial Intelligence for Education: A Conceptual and Pedagogical Framework

Authors

DOI:

https://doi.org/10.37497/rev.artif.intell.educ.v6ii.47

Keywords:

Generative artificial intelligence, Large language models, Educational technology, Pedagogical integration, Computational semantics, Teacher training

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.

Author Biography

Jairo Alberto Galindo-Cuesta, La Salle University

Full-Time Professor, Researcher, and University Lecturer. He is a doctoral candidate in Education (UNED, Spain), with specializations in AI for Content Generation (IEBS), E-Learning Project Management (UOC), and Virtual Learning Environments (OEI). He holds a Master's in Hispano-American Linguistics (Caro y Cuervo). His research focuses on language didactics, teacher training, and the pedagogical, critical, and creative use of technology in education. http://www.escrituradigital.net 

References

Austin, J. L. (1962). How to do things with words. Oxford University Press.

Bajtín, M. M. (1982). Estética de la creación verbal. Siglo XXI Editores.

Baltrusaitis, T., Ahuja, C., & Morency, L. P. (2019). Multimodal machine learning: A survey and taxonomy. IEEE transactions on pattern analysis and machine intelligence, 41(2), 423-443. DOI: https://doi.org/10.1109/TPAMI.2018.2798607

Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. fairmlbook.org.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.

Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT Press.

Damasio, A. R. (1994). Descartes' error: Emotion, reason, and the human brain. Putnam.

Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Orienta-Konsultit.

Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. Basic Books.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Harris, Z. S. (1954). Distributional structure. Word, 10(2-3), 146-162. DOI: https://doi.org/10.1080/00437956.1954.11659520

Immordino-Yang, M. H., & Damasio, A. (2007). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, Brain, and Education, 1(1), 3-10. DOI: https://doi.org/10.1111/j.1751-228X.2007.00004.x

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. DOI: https://doi.org/10.1038/s42256-019-0088-2

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511815355

Lenci, A. (2018). Distributional models of word meaning. Annual Review of Linguistics, 4, 151-171. DOI: https://doi.org/10.1146/annurev-linguistics-030514-125254

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1-35. DOI: https://doi.org/10.1145/3560815

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054. DOI: https://doi.org/10.1177/016146810610800610

Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans. Farrar, Straus and Giroux.

Mitchell, T. M. (1997). Machine learning. McGraw-Hill.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 1135-1144. DOI: https://doi.org/10.1145/2939672.2939778

Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Salomon, G. (1993). Distributed cognitions: Psychological and educational considerations. Cambridge University Press.

Searle, J. R. (1969). Speech acts: An essay in the philosophy of language. Cambridge University Press. DOI: https://doi.org/10.1017/CBO9781139173438

Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3-10.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), 257-285. DOI: https://doi.org/10.1016/0364-0213(88)90023-7

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., ... & Fedus, W. (2022). Emergent abilities of large language models. arXiv preprint arXiv:2206.07682.

Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599. DOI: https://doi.org/10.1016/j.compedu.2019.103599

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70. DOI: https://doi.org/10.1207/s15430421tip4102_2

Published

2025-06-13

How to Cite

Galindo-Cuesta, J. A. (2025). Glossary of Generative Artificial Intelligence for Education: A Conceptual and Pedagogical Framework. Review of Artificial Intelligence in Education, 6(i), e047. https://doi.org/10.37497/rev.artif.intell.educ.v6ii.47

Issue

Section

Articles