Modeling Language Acquisition in Commercial Off-the-Shelf Games: A Multiple Case Study

This paper examines how two commercial games, Heaven's Vault (2019) and Chants of Sennaar (2023), use artificial language learning as their core mechanic, and what educational game designers can learn from them. The author argues that many language-learning serious games lack fundamental design elements that commercial titles handle well, such as immersion, engagement, and feedback tailored to player actions. Using close reading and hermeneutic inquiry, informed by Second Language Acquisition theory, the study analyzes the games' mechanics through the lenses of "language learning mechanics" and "vocabulary progression," drawing on a single-researcher, think-aloud play methodology.

The analysis finds that Heaven's Vault supports vocabulary acquisition through puzzles that require negotiation of meaning, with its consistent grammar and "related words" feature enabling retrieval, while its narrative and identity performance contextualize learning. Chants of Sennaar integrates language directly into puzzle-solving, using environmental clues to drive learning and maintaining a challenge level close to the learner's zone of proximal development. However, its automatic translation feature removes opportunities for retrieval, and neither game provides generative language use, which the author identifies as a notable gap given its strong association with long-term vocabulary retention.

The study concludes that educational language games should preserve a sense of mystery, rely on negotiation of meaning through story and mechanics rather than direct instruction, and delay or withhold automatic translation to encourage retrieval. The author acknowledges the limits of a two-game, single-analyst design and proposes future work on additional titles and empirical testing of these design principles in learning outcomes.