Scramble to Understand: A Conceptual Framework for Gamified, Ai-Explained Word-Reordering Practice in English Language Teaching

In this paper, a conceptual plan for an English language learning activity is presented in which the familiar jumbled-word format is maintained as the main activity and what happens to the learner who mistakenly answers a jumbled word is altered. The suggested design involves providing a group of jumbled words to a learner and allowing them 1-5 attempts to correct and type the words into a grammatically correct sentence. Correct answer within the allotted number of attempts is accepted and the learner proceeds to the next problem, as is done with conventional scramble games. The difference is that when there is an incorrect order, an open-source large language model (LLM) is called that analyzes the learner's order, pinpoints the grammatical principle violated, explains the violation in plain language, and demonstrates how the sentence should be corrected. This paper does not present an implementation, prototype or experimental trial, but is a conceptual proposal to encourage discussion among ELT practitioners, curriculum designers and educational technologists of the possibility that explanation-driven feedback could be overlaid upon an already popular form of gamified feedback. It places the idea in the context of the existing literature on gamification in language teaching (L2L), corrective feedback (L2CFF), and the new large language model (LLM) technology for grammar explanation, and examines its potential for mapping onto the four core language skills: Listening, Speaking, Reading and Writing (LSRW).

Gamification and jumbled-word games (JWGs), scrambled sentences; corrective feedback; large language models; open-source LLM; English language teaching; LSRW skills

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