Word2Vec4Kids: Interactive Challenges to Introduce Middle School Students to Word Embeddings

Abstract

As Artificial Intelligence (AI) continues to integrate into more aspects of society, equipping younger generations with foundational AI knowledge becomes increasingly critical. This paper presents Word2Vec4Kids (W2V4K), an interactive application designed to familiarize middle school students with word embeddings, a key aspect of Natural Language Processing (NLP). W2V4K leverages the Word2Vec model, allowing students to explore word associations, similarity, and vector arithmetic through engaging game modes. The application was tested with 38 middle school students aged 11-14 at a Science Technology Engineering Math (STEM)-focused charter school. Data were collected on students' interactions with the application, including screen recordings, audio, and survey responses. Results demonstrated that W2V4K effectively introduces NLP concepts to students. Qualitative observations revealed high levels of engagement with students expressing excitement and curiosity about word relationships. As they progressed through the game modes, students showed increasing confidence in predicting word associations, brainstorming relevant words, and connecting the concepts to real-world applications. Quantitative data from post-interaction surveys indicated positive learning outcomes with 44.5% of students achieving perfect scores on concept-related items. Additionally, students demonstrated an ability to critically think about language representation. This study suggests that W2V4K provides an effective and engaging method for introducing NLP concepts to middle school students, contributing to the broader goal of enhancing AI literacy among younger generations.