Abstract
We developed a virtual assistant that enables students to access interactive content adapted for an introductory undergraduate course on artificial intelligence. This chatbot is able to show answers to frequently asked questions in a hierarchical structured manner, leading students by either voice, text or tactile input to the content that better solves their questions and doubts. It was developed using Google Dialogflow as a simple way to generate and train a natural language model. Another convenience of this platform is its ability to collect usage data that is potentially useful for lecturers as learning indicators. The main purpose of this paper is to outline the methodology that guided our implementation so that it can be reproduced in different educational contexts and study chatbots as tools for learning. At the moment, several articles, news and blogs are writing about the potential, implementation and impact chatbots have in general contexts, however there is little to no literature proposing a methodology to reproduce them for educational purposes. In that respect, we developed four main categories as a generic structure of course content and focused on quick implementation, easy updating and generalization. The final product received a general approbation of the students due to its accessibility and well structured data.
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Acknowledgements
Authors would like to acknowledge the financial support of Writing Lab, TecLabs and NOVUS Grant of Tecnológico de Monterrey, México, for the production of this work.
Authors thank Diego Solís Valles for presenting the classification of the underfitting and overfitting problem. We also thank Diego Adolfo José Villa, Uriel Ávila, and Moisés Benavides for their aid in the implementation of this chatbot.
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Reyes, R., Garza, D., Garrido, L., De la Cueva, V., Ramirez, J. (2019). Methodology for the Implementation of Virtual Assistants for Education Using Google Dialogflow. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_35
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