Linguistic Control in AI Text Generation: An Accessible Prompt-Based Approach Targeting L2 Spanish Absolute Beginners
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| Title: | Linguistic Control in AI Text Generation: An Accessible Prompt-Based Approach Targeting L2 Spanish Absolute Beginners |
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| Language: | English |
| Authors: | Raúl Getino-Diez (ORCID |
| Source: | Technology in Language Teaching & Learning. 2026 8. |
| Availability: | Castledown Publishers. Ground Level, 470 St Kilda Road, Melbourne, 3004, Australia. Tel: +61-3-7003-8355; e-mail: contact@castledown.com; Web site: https://www.castledown.com/journals/tltl |
| Peer Reviewed: | Y |
| Page Count: | 21 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Second Language Learning, Spanish, Novices, Artificial Intelligence, Linguistics, Technology Uses in Education |
| ISSN: | 2652-1687 |
| Abstract: | Generative artificial intelligence (AI) offers strong potential for developing customized second language learning materials and tools. However, generating texts for absolute beginners which require strict lexical and grammatical control remains a challenge. Although controlled text generation (CTG) techniques exist, they often require technical expertise and infrastructure, limiting accessibility for educators. This study evaluates, in the context of Spanish, a prompt-based approach that leverages large language models (LLMs) without fine-tuning or specialized tools. Prompts enforce linguistic constraints defined in two attachments: a categorized Spanish vocabulary list, and a set of example sentences illustrating approved Spanish grammatical structures organized by communicative function. Three variables were manipulated: "AI model" (ChatGPT-4o vs. Claude 3.5 Sonnet), "prompt type" (standard vs. extended, with constraint-enhancing techniques), and "attachment format" (rich-heavyweight vs. lightweight JSON). A secondary variable, "text type" (city descriptions, personal introductions, and dialogues), was also examined. A total of 720 texts were generated, 30 per condition. Measures included proportions of non-compliant lexical and grammatical items, user-perceived latency, and errors in vocabulary, grammar, and coherence. Model choice was the primary driver of constraint adherence, with Claude 3.5 Sonnet outperforming ChatGPT-4o. Extended prompts improved adherence across models. Attachment format showed no systematic effect on adherence, but JSON significantly reduced latency and response-time variability. Text type also influenced adherence, and error rates remained low. Findings offer educators a scalable, low-barrier solution for generating tailored beginner-level Spanish materials and AI-powered tools using LLMs, along with insights into how different design choices affect performance. This approach, transferable to other languages, provides a practical alternative to resource-intensive CTG techniques, addressing a critical gap in AI-assisted language education. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1501451 |
| Database: | ERIC |
| Abstract: | Generative artificial intelligence (AI) offers strong potential for developing customized second language learning materials and tools. However, generating texts for absolute beginners which require strict lexical and grammatical control remains a challenge. Although controlled text generation (CTG) techniques exist, they often require technical expertise and infrastructure, limiting accessibility for educators. This study evaluates, in the context of Spanish, a prompt-based approach that leverages large language models (LLMs) without fine-tuning or specialized tools. Prompts enforce linguistic constraints defined in two attachments: a categorized Spanish vocabulary list, and a set of example sentences illustrating approved Spanish grammatical structures organized by communicative function. Three variables were manipulated: "AI model" (ChatGPT-4o vs. Claude 3.5 Sonnet), "prompt type" (standard vs. extended, with constraint-enhancing techniques), and "attachment format" (rich-heavyweight vs. lightweight JSON). A secondary variable, "text type" (city descriptions, personal introductions, and dialogues), was also examined. A total of 720 texts were generated, 30 per condition. Measures included proportions of non-compliant lexical and grammatical items, user-perceived latency, and errors in vocabulary, grammar, and coherence. Model choice was the primary driver of constraint adherence, with Claude 3.5 Sonnet outperforming ChatGPT-4o. Extended prompts improved adherence across models. Attachment format showed no systematic effect on adherence, but JSON significantly reduced latency and response-time variability. Text type also influenced adherence, and error rates remained low. Findings offer educators a scalable, low-barrier solution for generating tailored beginner-level Spanish materials and AI-powered tools using LLMs, along with insights into how different design choices affect performance. This approach, transferable to other languages, provides a practical alternative to resource-intensive CTG techniques, addressing a critical gap in AI-assisted language education. |
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| ISSN: | 2652-1687 |
