This study explores Cipher, an adaptive language learning game tailored for the under-resourced Irish language, aimed mainly at primary school students. By integrating text analysis techniques, Cipher dynamically adjusts its difficulty based on the player’s language proficiency, offering a customised learning experience. The game’s narrative involves decoding spells to access Irish myths and stories, combining language learning with cultural elements. Development involved collaboration with educators to align the game content with curriculum standards and incorporate culturally relevant materials. This paper outlines the game’s development process, emphasising the use of text analysis for difficulty adjustment and the importance of engaging, educational gameplay. Preliminary results indicate that adaptive games like Cipher can enhance language learning by providing immersive, personalised experiences that maintain player motivation and engagement.

Digital game-based language learning (DGBLL) can help with the language learning process. DGBLL applications can make learning more enjoyable and engaging, but they are difficult to develop. A DBGLL app that relies on target language texts obviously needs to be able to use texts of the appropriate level for the individual learners. This implies that text classification tools should be available to DGBLL developers, who may not be familiar with the target language, in order to incorporate suitable texts into their games. While text difficulty classifiers exist for many of the most commonly spoken languages, this is not the case for under-resourced languages, such as Irish. In this paper, we explore approaches to the development of text classifiers for Irish. In the first approach to text analysis and grading, we apply linguistic analysis to assess text complexity. Features from this approach are then used in machine learning-based text classification, which explores the application of a number of machine learning algorithms to the problem. Although the development of these text classifiers is at an early stage, they show promise, particularly in a low-resourced scenario.

This paper provides an overview of the Cipher engine which enables the development of a Digital Educational Game (DEG) based on noticing ciphers or patterns in texts. The Cipher engine was used to develop the Cipher: Faoi Gheasa, a digital educational game for Irish, which incorporates NLP resources and is informed by Digital Game-Based Language Learning (DGBLL) and Computer-Assisted Language Learning (CALL) research. The paper outlines six phases where NLP has strengthened the Cipher: Faoi Gheasa game. It shows how the Cipher engine can be used to build a Cipher game for other languages, particularly low-resourced and endangered languages in which NLP resources are under-developed or few in number.

This paper describes Cipher – Faoi Gheasa, a ‘game with a purpose’ designed to support the learning of Irish in a fun and enjoyable way. The aim of the game is to promote language ‘noticing’ and to combine the benefits of reading with the enjoyment of computer game playing, in a pedagogically beneficial way. In this paper we discuss pedagogical challenges for Irish, the development of measures for the selection and ranking of reading materials, as well as initial results of game evaluation. Overall user feedback is positive and further testing and development is envisaged.

In this paper, we present a game with a purpose (GWAP) (Von Ahn 2006). The aim of the game is to promote language learning and ‘noticing’ (Skehan, 2013). The game has been designed for Irish, but the framework could be used for other languages. Irish is a minority language which means that L2 learners have limited opportunities for exposure to the language, and additionally, there are also limited (digital) learning resources available. This research incorporates game development, language pedagogy and ICALL language materials development. This paper will focus on the language materials development as this is a bottleneck in the teaching and learning of minority and endangered languages.