Artificial intelligence (AI) language models, such as ChatGPT, offer exciting new possibilities for foreign language education. However, effectively utilizing these tools requires more than just casual conversation. This article focuses on prompt engineering, the skill of creating specific and effective instructions to guide AI responses. By mastering prompt engineering, learners can maximize the potential of AI for language acquisition. We explore practical strategies, including providing clear instructions, offering context, breaking down complex tasks, and integrating external tools. These techniques empower learners to achieve greater fluency and a deeper understanding of the target language, transforming AI into a valuable personalized language learning partner
Artificial intelligence (AI) language models, such as ChatGPT, offer exciting new possibilities for foreign language education. However, effectively utilizing these tools requires more than just casual conversation. This article focuses on prompt engineering, the skill of creating specific and effective instructions to guide AI responses. By mastering prompt engineering, learners can maximize the potential of AI for language acquisition. We explore practical strategies, including providing clear instructions, offering context, breaking down complex tasks, and integrating external tools. These techniques empower learners to achieve greater fluency and a deeper understanding of the target language, transforming AI into a valuable personalized language learning partner
| № | Имя автора | Должность | Наименование организации |
|---|---|---|---|
| 1 | Kasimova M.A. | PhD researcher | SSS ”Temurbeklar maktabi” |
| № | Название ссылки |
|---|---|
| 1 | 1.Denk, T. (2023). Prompt engineering for text-based generative AI. O'Reilly Media.2.Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903.6https://doi.org/10.48550/arXiv.2201.119033.Reynolds, L., & McDonell, K. (2021). Prompt programming for large language models: Beyond the few-shot paradigm. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, 1-7. https://doi.org/10.1145/3411763.3451760 |