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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2501.15247 |
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| _version_ | 1866915125313667072 |
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| author | Lin-Zucker, Miao Bellassen, Joël Zucker, Jean-Daniel |
| author_facet | Lin-Zucker, Miao Bellassen, Joël Zucker, Jean-Daniel |
| contents | The use of chatbots in language learning has evolved significantly since the 1960s, becoming more sophisticated platforms as generative AI emerged. These tools now simulate natural conversations, adapting to individual learners' needs, including those studying Chinese. Our study explores how learners can use specific prompts to engage Large Language Models (LLM) as personalized chatbots, aiming to target their language level based on the Common European Framework of Reference for Languages (CEFR) and the European Benchmarking Chinese Language (EBCL) project. Focusing on A1, A1+ and A2 levels, we examine the teaching of Chinese, which presents unique challenges due to its logographic writing system. Our goal is to develop prompts that integrate oral and written skills, using high-frequency character lists and controlling oral lexical productions. These tools, powered by generative AI, aim to enhance language practice by crossing lexical and sinographic recurrence. While generative AI shows potential as a personalized tutor, further evaluation is needed to assess its effectiveness. We conducted a systematic series of experiments using ChatGPT models to evaluate their adherence to constraints specified in the prompts. The results indicate that incorporating level A1 and A1+ characters, along with the associated reference list, significantly enhances compliance with the EBCL character set. Properly prompted, LLMs can increase exposure to the target language and offer interactive exchanges to develop language skills. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15247 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prompting ChatGPT for Chinese Learning as L2: A CEFR and EBCL Level Study Lin-Zucker, Miao Bellassen, Joël Zucker, Jean-Daniel Computation and Language Artificial Intelligence The use of chatbots in language learning has evolved significantly since the 1960s, becoming more sophisticated platforms as generative AI emerged. These tools now simulate natural conversations, adapting to individual learners' needs, including those studying Chinese. Our study explores how learners can use specific prompts to engage Large Language Models (LLM) as personalized chatbots, aiming to target their language level based on the Common European Framework of Reference for Languages (CEFR) and the European Benchmarking Chinese Language (EBCL) project. Focusing on A1, A1+ and A2 levels, we examine the teaching of Chinese, which presents unique challenges due to its logographic writing system. Our goal is to develop prompts that integrate oral and written skills, using high-frequency character lists and controlling oral lexical productions. These tools, powered by generative AI, aim to enhance language practice by crossing lexical and sinographic recurrence. While generative AI shows potential as a personalized tutor, further evaluation is needed to assess its effectiveness. We conducted a systematic series of experiments using ChatGPT models to evaluate their adherence to constraints specified in the prompts. The results indicate that incorporating level A1 and A1+ characters, along with the associated reference list, significantly enhances compliance with the EBCL character set. Properly prompted, LLMs can increase exposure to the target language and offer interactive exchanges to develop language skills. |
| title | Prompting ChatGPT for Chinese Learning as L2: A CEFR and EBCL Level Study |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2501.15247 |