Applications of Machine Learning in Content Generation for Educational Video Games
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Over the past few years, students have become increasingly unmotivated to read their assigned textbooks as an accompaniment to classroom lectures and activities. Reading the textbook is known to improve comprehension and overall student performance in classrooms. If reading the textbook was reformatted into a more engaging experience, perhaps it would improve student motivation and knowledge retention. Teaching students the importance of learning while also motivating them to do well in class will help them gain the knowledge and grades needed to land competitive jobs after they graduate college. Game-Based Learning (GBL) is an emerging field of study that attempts to use video games to create interactive educational experiences. Game-Based Learning has been shown to have educational merit, being well-known for providing intrinsic motivation for students to learn (most often, as a supplement to traditional coursework). With GBL in mind, is it possible to generate interactive game content from textbooks using machine learning (ML) and artificial intelligence (AI) that can replace or supplement the source material in terms of educational content in a traditional classroom setting? Our team proposes to lay the groundwork for future research in Game-Based Learning and Machine Learning at the LIVE Lab undergraduate research lab (Texas A&M University, College of Architecture, Dept. of Visualization) by attempting to reformat school textbooks into interactive chatbot AIs with the assistance of knowledge compilation & fact-retrieval systems designed for generating educational video game content.
natural language processing
Donelan, Lloyd J; Patel, Kishan J; Lenzen, Brenton Allen (2020). Applications of Machine Learning in Content Generation for Educational Video Games. Undergraduate Research Scholars Program. Available electronically from