Gamified, Learner-Built Applications for Language Study: Effectiveness, Limitations, and Implications

Abstract

This paper explores how foreign-language learners can enhance learning efficiency by designing and using personalized digital applications rather than relying solely on commercial language-learning tools. While platforms such as Duolingo dominate the market, their course offerings do not always meet specific learner needs, exemplified by the lack of Thai courses for Japanese speakers. To address such gaps, the study presents two case studies in which learners created their own applications tailored to their goals, proficiency levels, and learning preferences. The first case examines a Japanese learner of Thai who developed a simple Python-based app to master Thai consonant characters. By incorporating randomization and active recall, the app transformed rote memorization into an engaging task and significantly improved retention. The second case focuses on an advanced Japanese learner of English who built a JavaScript-based vocabulary app targeting high-difficulty lexical items often neglected by mainstream apps, resulting in measurable vocabulary growth. The discussion highlights that these apps were intentionally designed for single users—the developers themselves—allowing extreme personalization without concern for general usability. Although such specificity limits generalizability, recent generative AI tools lower technical barriers, enabling more learners to create similar tools. Importantly, both applications are open-source, allowing others to adapt them to their own needs. The study concludes that learner-built apps hold great promise for personalized language learning, while also posing challenges for scalability and broader applicability.

Keywords: Personalized Learning, Learner-developed Applications, ICT-supported Language Learning

DOI: 10.54941/ahfe1007979