LIBRO COMPLETO: The Education Revolution through Artificial Intelligence
CAPÍTULO 11

FICHA TÉCNICA

Fecha de publicación​:
04/11/2024

Doi​ del capítulo:

Título del libro: The Education Revolution through Artificial Intelligence

URL del libro:

ISBN del libro: 9788410282582

DOI del libro:

Improving Learning through Automatic Generation of AI-Based Narratives

Abstract

This chapter delves into the potential of Artificial Intelligence in education, focusing on its use to enhance students’ narrative and creative skills. It analyzes how AI-assisted storytelling, especially through Large Language Models (LLMs), can be a powerful tool for learning, exploring both its limitations and opportunities in terms of training and aiding pre-service Primary Education students in developing their writing abilities. Human interaction remains crucial in the field of education, particularly in language learning, hence the importance of understanding and correctly utilizing these emerging technologies to maximize their educational benefits. Artificial Intelligence is presented as an aid in narrative creation, capable of unlocking creative processes and generating innovative ideas, provided it is used as a tool guided by the direction and interaction of human educators. LLMs still have limitations in aspects of narrative creation that cannot be fully captured by contextual relationships between words and sequential generation alone. This approach advocates for effective collaboration between humans and machines, focusing on enhancing learning and protecting rights at the intersection of Artificial Intelligence and education.

Palabras clave

Autores

María Ribes-Lafoz, Borja Navarro-Colorado, María Tabuenca-Cuevas, José Rovira-Collado

Cómo citar

Ribes-Lafoz, M., Navarro-Colorado, B., Tabuenca-Cuevas, M., Rovira-Collado, J. (2024). Improving Learning through Automatic Generation of AI-Based Narratives. In Hervás-Gómez, C., Díaz-Noguera, M. D., Sánchez-Vera, F. (Coords.), The Education Revolution Through Artificial Intelligence (pp. 169-180). Octaedro. https://doi.org/10.36006/09651-1-11

Referencias bibliográficas

“Alabdulkarim, A., Li, S., & Peng, X. (2021). Automatic Story Generation: Challenges and Attempts. arXiv https://doi.org/10.48550/ARXIV.2102.12634
Alhussain, A. I., & Azmi, A. M. (2022). Automatic story generation: A survey of approaches. ACM Computing Surveys, 54(5), 1-38. https://doi.org/10.1145/3453156
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). https://doi.org/10.1145/3442188.3445922
Breithaupt, F., Otenen, E., Wright, D. R., Kruschke, J. K., Li, Y., & Tan, Y. (2024). Humans create more novelty than Chatgpt when asked to retell a story. Scientific Reports, 14(1), 875. https://doi.org/10.1038/s41598-023-50229-7
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv https://doi.org/10.48550/ARXIV.1810.04805
Dijk, T. A. van, & Kintsch, W. (1983). Strategies of Discourse Comprehension. Academic.
Gemini Team Google, Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., Silver, D., Petrov, S., Johnson, M., Antonoglou, I., Schrittwieser, J., Glaese, A., Chen, J., Pitler, E., … Vinyals, O. (2023). Gemini: A Family of Highly Capable Multimodal Models. General Technical Report. HuggingFace. https://doi.org/10.48550/ARXIV.2312.11805
Gottschall, J. (2012). The Storytelling Animal: How Stories Make Us Human. Houghton Mifflin Harcourt.
Greimas, A. J. (2015). Sémantique Structurale: Recherche de Méthode. Universitaires de France.
Jewitt, C., & Kress, G. R. (Eds.). (2003). Multimodal Literacy. Peter Lang.
Liang, J.-C., & Hwang, G.-J. (2023). A robot-based digital storytelling approach to enhancing EFL learners’ multimodal storytelling ability and narrative engagement. Computers & Education, 201, 104827. https://doi.org/10.1016/j.compedu.2023.104827
Lytle, S. R., & Kuhl, P. K. (2017). Social interaction and language acquisition: Toward a neurobiological view. In E. M. Fernández, & H. S. Cairns (Eds.). The Handbook of Psycholinguistics (pp. 615-634). Wiley. https://doi.org/10.1002/9781118829516.ch27
Manuvinakurike, R., Sahay, S., Manepalli, S., & Nachman, L. (2023). Zero-shot Conversational Summarization Evaluations with Small Large Language Models. arXiv. https://doi.org/10.48550/ARXIV.2311.18041
Mar, R. A., Li, J., Nguyen, A. T. P., & Ta, C. P. (2021). Memory and comprehension of narrative versus expository texts: A meta-analysis. Psychonomic Bulletin & Review, 28(3), 732-749. https://doi.org/10.3758/s13423-020-01853-1
Marzano, R. J., & Kendall, J. S. (2007). The New Taxonomy of Educational Objectives. Corwin.
McAdams, D. P. (2019). “First we invented stories, then they changed us”: The evolution of narrative identity. Evolutionary Studies in Imaginative Culture, 3(1), 1-18. https://doi.org/10.26613/esic.3.1.110
Medina, A. L., & Pilonieta, P. (2006). Once upon a time: Comprehending narrative text. In J. S. Schumm (Ed.). Reading Assessment and Instruction for All Learners (pp. 222-261). Guilford.
Ministerio de Educación (2023). Real Decreto 205/2023, de 28 de marzo, por el que se establecen medidas relativas a la transición entre planes de estudios, como consecuencia de la aplicación de la Ley Orgánica 3/2020, de 29 de diciembre, por la que se modifica la Ley Orgánica 2/2006, de 3 de mayo, de Educación. Boletín Oficial del Estado, 75, de 29 de marzo de 2023 (pp.45712-45717). https://www.boe.es/eli/es/rd/2023/03/28/205
Pauls, L. J., & Archibald, L. M. (2021). Cognitive and linguistic effects of narrative-based language intervention in children with developmental language disorder. Autism & Developmental Language Impairments, 6, 239694152110158. https://doi.org/10.1177/23969415211015867
Pérez y Pérez, R., & Sharples, M. (2023). An Introduction to Narrative Generators: How Computers Create Works of Fiction. Oxford University.
Ramesh, A., Pavlov, M., & Goh, G. (2021). DALL·E: Creating Images from Text. General Report. OpenAI.
Ribes-Lafoz, M., & Navarro-Colorado, B. (2023). Aprovechamiento de ChatGPT en la enseñanza de lengua extranjera en educación superior. In D. Ortega-Sánchez, & A. López-Padrón (Eds.). Educación y sociedad: claves interdisciplinares. Octaedro.
Rovira-Collado, J., Martínez-Carratalá, F., Miras, S., & Ribes-Lafoz, M. (2022). Teacher stories from the future: Technology for the language and literature classroom. In S. Mengual Andrés, & M. Urrea Solano (Eds.). Education and the Collective Construction of Knowledge (pp.173-187). Peter Lang.
Sinding, M., Heydenreich, A., & Mecke, K. (2024). Narrative and Cognition in Literature and Science. De Gruyter.
Stanton, A. (Dir.) (2008). WALL·E. Pixar.
Tabuenca-Cuevas, M. (2021). Una visión de la cultura a través de los cuentos juveniles de terror Scary Stories. In J. Rovira-Collado, & J. I. Jerez-Martínez (Eds.). Ficción fantástica, lectura multimodal y prosopografía: Cultura fan y superhéroes (pp.131-143). Marcial Pons. Ediciones Jurídicas y Sociales.
Thorndyke, P. W. (1977). Cognitive structures in comprehension and memory of narrative discourse. Cognitive Psychology, 9(1), 77-110. https://doi.org/10.1016/0010-0285(77)90005-6
Torres, J. (2023). La inteligencia artificial explicada a los humanos. Plataforma.
Verga, L., & Kotz, S. (2013). How relevant is social interaction in second language learning? Frontiers in Human Neuroscience, 7. https://www.frontiersin.org/articles/10.3389/fnhum.2013.00550
Wu, Y., Barquero, L. A., Pickren, S. E., Taboada Barber, A., & Cutting, L. E. (2020). The Relationship Between Cognitive Skills and Reading Comprehension of Narrative and expository texts: A longitudinal study from grade 1 to grade 4. Learning and Individual Differences, 80, 101848. https://doi.org/10.1016/j.lindif.2020.101848
Yenduri, G., M, R., G, C. S., Y, S., Srivastava, G., Maddikunta, P. K. R., G, D. R., Jhaveri, R. H., B, P., Wang, W., Vasilakos, A. V., & Gadekallu, T. R. (2023). Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, potential applications, emerging challenges, and future directions. arXiv. https://doi.org/10.48550/ARXIV.2305.10435”

Ir al contenido