Simplifying AI Prompt-Writing: Strategies for ESL/EFL Learners

Introduction

In recent years, the integration of artificial intelligence (AI) into educational settings has become increasingly common, transforming how learners and educators interact with information. Among the most prominent applications of this trend is the use of generative AI tools such as ChatGPT, Claude, and Bard. These systems, powered by advanced language models, respond to written instructions, referred to as prompts, to generate meaningful, often human-like text. In the context of English as a Second Language (ESL) and English as a Foreign Language (EFL) education, the ability to write effective AI prompts has emerged as a significant new skill for both teachers and students.

For many ESL/EFL learners, AI represents a promising tool to support language development. These platforms can offer vocabulary explanations, correct grammar mistakes, summarize texts, and simulate conversations. However, to access these benefits meaningfully, users must know how to communicate clearly and effectively with the AI. This means crafting prompts that are comprehensible, concise, and aligned with the learner’s objective. Without these elements, the responses generated by AI may be vague, irrelevant, or too complex, resulting in confusion rather than support.

Prompt-writing has become a form of digital literacy, an essential competence in navigating the modern learning landscape. In much the same way that writing an effective email or search query requires precision and clarity, AI prompt-writing demands thoughtful composition, especially for non-native English speakers. While native speakers may rely on implicit linguistic knowledge to generate functional prompts, ESL/EFL learners often struggle with vocabulary selection, grammar, and syntax—all of which impact the quality of AI-generated outputs.

The challenge is twofold. On one hand, ESL/EFL learners must manage their existing language limitations while formulating input for an AI system. On the other hand, they must interpret AI-generated responses that may contain advanced structures or nuanced expressions beyond their current proficiency. This dynamic can be discouraging if not approached with guided instruction and simplified strategies. Consequently, there is a growing need for English educators to equip learners with the tools and techniques necessary to write better prompts and make sense of the outputs they receive.

This article explores the principles and practices of simplifying AI prompt-writing specifically for ESL/EFL learners. It offers an accessible and pedagogically sound roadmap for both educators and learners, balancing academic insights with practical applications. The following sections will outline the fundamentals of AI prompt-writing, identify the most common challenges faced by non-native English users, and introduce a range of strategies and tools for developing effective prompts. This approach enhances students’ language learning outcomes and encourages independent, reflective engagement with digital technologies in language education.

What is AI Prompt-Writing? Understanding the Basics

What are AI prompts?

In the context of generative artificial intelligence, a prompt is the written instruction or query provided by a user to elicit a response from an AI model. Prompts can be simple commands, such as “Write a short paragraph about London,” or more complex, multi-layered tasks like “Summarize this article using B1-level vocabulary and identify three main ideas.” The quality and specificity of a prompt significantly influence the accuracy, usefulness, and appropriateness of the AI-generated output.

For ESL/EFL learners, prompts are not only a means of communicating with the AI system but also serve as a form of written language production. The process of constructing a prompt involves linguistic choices related to grammar, vocabulary, and sentence structure. As such, prompt-writing becomes a skill that reinforces core components of language learning while serving as a gateway to personalized educational support.

How AI tools interpret user input

Generative AI tools such as ChatGPT, Claude, and Bard operate by predicting the most likely continuation of a given sequence of words. When a user inputs a prompt, the AI processes it based on patterns in the vast datasets on which it was trained. These models do not “understand” language in the human sense; rather, they rely on probabilities to generate responses that appear coherent and relevant (Brown et al., 2020).

This probability-based reasoning means that AI is highly sensitive to the wording, structure, and clarity of the prompt. For instance, vague or grammatically incorrect inputs can confuse the AI, causing it to misinterpret the user’s intention. Moreover, the inclusion of unfamiliar idioms, ambiguous terms, or complex syntax can further complicate the response generation process, especially for learners who rely on consistency and clarity in feedback.

For ESL/EFL learners, this sensitivity highlights the importance of clear, direct language when writing prompts. By learning to construct prompts that avoid ambiguity and minimize syntactic complexity, learners are more likely to receive outputs that support their language development effectively.

Examples of clear versus confusing prompts

To illustrate the effect of prompt clarity, consider the following two examples intended to help a learner practice writing introductions:

1. Confusing prompt: “Tell me how to write an intro, but in a way that’s not too much but not too little, and also keep it like academic but not really hard.”
2. Clear prompt: “Please explain how to write a short academic introduction for an essay, using simple English and examples.”

While both prompts express similar objectives, the first one contains vague modifiers (“not too much,” “not really hard”) and informal phrasing. The second example uses precise language and a clearly defined goal, resulting in a more appropriate AI response that aligns with the learner’s needs.

Another example relevant to vocabulary development:

1. Confusing: “Can you tell me stuff about hard English words I should know?”
2. Clear: “Please give me a list of ten advanced English vocabulary words with definitions and example sentences.”

The clearer prompt allows the AI to focus on specific output parameters—list format, quantity, and explanation—while the ambiguous one lacks structure and definition, leading to less useful results.

Why does prompt quality affect language learning outcomes?

Prompt quality directly affects the usefulness of AI-generated responses, particularly for learners with limited language proficiency. A well-structured prompt serves as a scaffold, guiding the AI to produce output that is aligned with the learner’s objectives and current ability. In contrast, poorly structured prompts can lead to answers that are overly complex, off-topic, or grammatically inconsistent.

Effective prompts can support various aspects of language learning, including grammar correction, vocabulary acquisition, reading comprehension, and writing practice. For example, prompts such as “Please correct the grammar in the following paragraph and explain your changes” or “Give me synonyms of ‘important’ that are suitable for formal writing” produce outputs that directly contribute to learner growth (Zou et al., 2023). Furthermore, clear prompts help learners engage with AI-generated content more confidently, fostering independent learning and motivation.

On a broader level, prompt-writing can enhance digital literacy and critical thinking. As learners refine their prompts based on AI feedback, they develop metalinguistic awareness—recognizing how language functions in different contexts and how meaning can shift based on word choice and sentence structure. This iterative process promotes self-directed learning and greater precision in language use (Savin-Baden et al., 2021).

Educators, too, benefit from understanding the mechanics of AI prompt-writing. With this knowledge, they can guide students in developing prompts that lead to effective practice activities and personalized feedback. In classroom settings, teachers may incorporate prompt-writing exercises into writing or speaking lessons, helping students to explore the potential of AI tools while reinforcing foundational skills.

In summary, AI prompt-writing is both a technical and linguistic task that plays a vital role in how learners interact with AI systems. Clarity, specificity, and simplicity are key factors in generating effective responses from AI models. As ESL/EFL learners begin to explore AI as a learning companion, understanding how prompts work—and how to improve them—can significantly enhance their educational experience. By learning to formulate well-structured prompts, students not only improve their communication with AI but also reinforce critical elements of English language learning.

Challenges Faced by ESL/EFL Learners When Writing Prompts

Artificial intelligence tools such as ChatGPT offer a wide range of support for language learning, but the quality of this support depends heavily on the user’s ability to write clear and purposeful prompts. For ESL/EFL learners, this task can be especially challenging due to a combination of linguistic limitations, cognitive demands, and psychological barriers. Understanding these challenges is essential for educators who aim to integrate AI effectively into the language classroom and for learners who wish to engage with these tools productively.

Linguistic limitations: vocabulary, grammar, sentence construction

One of the primary difficulties ESL/EFL learners face when writing prompts lies in their limited control over vocabulary and grammar. Unlike native speakers, who often possess an intuitive sense of word choice and sentence structure, second-language learners must carefully consider how to construct a question or command in English that is both accurate and understandable.

Learners at lower proficiency levels may struggle to express their intentions clearly due to a restricted vocabulary. For instance, instead of requesting a “summary of a news article,” a learner might write, “Tell me about this thing,” which lacks the specificity required for the AI to produce a meaningful response. In addition, grammatical errors—such as incorrect verb forms, misplaced modifiers, or missing articles—can distort the intended meaning of a prompt. A request like “Explain what is the important of the climate change” may still yield a relevant answer from the AI, but the ambiguity caused by incorrect grammar increases the risk of an unhelpful or misaligned response.

According to Ellis (2003), language production in a second language often involves a slower and more deliberate process due to the need for lexical retrieval and syntactic construction. This cognitive load makes it harder for learners to produce effective prompts under time constraints or without guidance. Errors in structure may also reduce the learner’s confidence in using AI tools regularly, further limiting their engagement and progress.

Misunderstanding AI-generated content

Even when a prompt is clear and well-formed, learners may have difficulty interpreting the AI’s response. Generative AI tools often produce outputs that include advanced vocabulary, idiomatic language, and nuanced phrasing that are beyond the learner’s current proficiency level. For instance, a B1-level learner might ask for an explanation of passive voice and receive a definition that includes terms like “agentless constructions” or “transitive verbs,” which may not be fully understood.

This disconnect between the language level of the learner and the AI’s output can lead to confusion and frustration. Learners may perceive the tool as unhelpful or too difficult to work with, especially if they are unfamiliar with strategies for simplifying or clarifying AI responses. As highlighted by Vandergrift and Goh (2012), comprehension difficulties in language learning are not solely a matter of vocabulary but also involve working memory, background knowledge, and the ability to infer meaning from context.

Educators should be aware of this challenge and teach learners how to modify their prompts to request simpler language, such as by including phrases like “Use A2-level words” or “Explain this in easy English.” When students are not taught to specify the language level in their prompts, they may disengage from the tool due to repeated experiences of not understanding the answers provided.

Pragmatic and contextual confusion

Another common issue lies in the area of pragmatics—the social and contextual use of language. ESL/EFL learners often have difficulty using language appropriately for specific situations, especially when trying to interact with an AI system that simulates human-like communication. For example, a learner might use overly polite or indirect language, such as “Would you mind giving me some information if that’s okay with you?” when a clearer and more effective prompt would be “Please give me five facts about climate change.”

In a study by Taguchi (2011), pragmatic competence was shown to be one of the more difficult aspects of language learning, particularly for learners with limited exposure to authentic communication. When learners do not fully grasp the norms of directness, formality, or purpose in prompt-writing, they may inadvertently confuse the AI model, leading to off-topic or irrelevant outputs. Additionally, learners may interpret polite responses from AI as ambiguous or evasive, misreading the intent of the message due to a lack of contextual awareness.

To address this challenge, learners need to be explicitly taught how to structure prompts based on communicative purpose, such as requesting information, asking for clarification, or generating examples. Providing sample prompts and practicing their formulation in different learning scenarios can help learners build pragmatic awareness and reduce confusion when working with AI.

Emotional barriers, such as fear of making mistakes

Finally, emotional and psychological factors play a significant role in learners’ ability to write effective prompts. Many ESL/EFL learners experience anxiety when producing written language, especially when they are uncertain about correctness or appropriateness. This anxiety can be intensified when interacting with AI tools, as learners may fear being judged or evaluated, even though the interaction is with a machine.

Krashen’s Affective Filter Hypothesis (1982) suggests that emotional factors such as anxiety, self-confidence, and motivation directly impact the acquisition and use of language. When learners are afraid of making mistakes in their prompts, they may simplify their language too much, avoid using the AI altogether, or rely excessively on translation tools rather than producing original English sentences.

This fear of error can be counterproductive, as prompt-writing is an iterative skill that improves through practice and feedback. Learners need to be encouraged to view the AI as a learning partner rather than a judge. Educators can support this mindset by creating a respectful classroom culture where experimentation is welcomed and mistakes are viewed as part of the learning process.

Moreover, scaffolding strategies—such as providing prompt frames or sentence starters—can help reduce the fear of error by giving learners a foundation upon which to build their own prompts. Over time, this approach can lead to greater learner autonomy and confidence in navigating AI tools.

The challenges faced by ESL/EFL learners in AI prompt-writing are multifaceted, involving linguistic, cognitive, pragmatic, and emotional dimensions. Vocabulary gaps, grammatical errors, and misinterpretation of AI responses are common obstacles, as are cultural misunderstandings and performance anxiety. By identifying these challenges, educators can design instructional approaches that provide targeted support, helping learners to write clearer prompts and engage more confidently with AI tools. With guidance and practice, prompt-writing can become a powerful skill that enhances both language proficiency and digital competence.

Principles for Simplifying Prompts in the ESL/EFL Context

As generative AI tools become more prevalent in language learning, it is essential for educators to guide ESL/EFL learners in writing prompts that are both effective and accessible. Clear, well-structured prompts allow learners to interact with AI confidently and receive responses that support their linguistic development. This section outlines four core principles for simplifying AI prompts in the ESL/EFL context, along with illustrative examples based on learner proficiency levels.

Use of plain language and high-frequency vocabulary

Plain language refers to clear, straightforward English that avoids unnecessary complexity. For language learners, especially those at A2 or B1 levels on the CEFR scale, using plain English ensures that the AI can interpret the prompt accurately and produce an understandable output. According to Nation (2013), frequent exposure to high-frequency vocabulary supports reading comprehension and productive language use, which directly translates into more effective prompt-writing.

For example, instead of a prompt like:

“Could you provide an elaborate explanation on the ramifications of climate policy shifts in developing nations?”

A simplified version using plain language would be:

“Please explain how changes in climate laws affect poor countries, using simple words.”

The revised prompt uses high-frequency words such as “changes,” “laws,” and “poor countries,” making it easier for both the learner to write and the AI to interpret correctly. Educators can support this principle by encouraging learners to use graded vocabulary lists, such as the New General Service List (Browne et al., 2013), when crafting prompts.

Structuring prompts with clear intentions

Another essential principle is ensuring that prompts reflect a clear and direct intention. ESL/EFL learners may sometimes write vague or multi-layered prompts that confuse the AI and lead to irrelevant or overly complex responses. Clarity in structure helps both the AI and the learner understand what is being asked and what kind of output is expected.

A clear prompt typically contains:

1. A task (e.g., explain, list, summarize)
2. A topic (e.g., present perfect tense, environmental issues)
3. A condition (e.g., using simple language, for B1-level learners)

Compare the following:

Vague prompt:
“Tell me something about tenses.”

Clear prompt:
“Explain the present perfect tense with three example sentences in simple English.”

The second example specifies the grammatical focus, the quantity of examples, and the desired complexity of language, increasing the likelihood of a helpful response. Harmer (2015) notes that giving learners clear models for task design encourages focused language production and fosters autonomy.

Avoiding idiomatic or culturally specific phrases

Many ESL/EFL learners unintentionally include idiomatic language or culturally bound expressions in their prompts, especially if they are modeled on informal spoken English or translated from their first language. While native speakers might use phrases like “hit the nail on the head” or “give me the lowdown,” these expressions can obscure the meaning and confuse AI systems that are more literal in their language processing.

In addition to affecting AI interpretation, idiomatic expressions can produce outputs that themselves contain culturally specific content, making comprehension more difficult for learners. To avoid this issue, learners should be guided to write prompts that use literal and globally understood English.

For example:

Less effective prompt:
“Can you spill the beans about the American Revolution?”

More effective prompt:
“Can you give a short and clear summary of the American Revolution?”

The second version avoids informal idiomatic language and replaces it with a direct request. Teachers can support learners by reviewing common idioms and offering literal equivalents suitable for prompt-writing. The British Council (2020) emphasizes the value of teaching learners to identify and rephrase idiomatic language in more universally accessible terms.

Teaching prompt scaffolding: from simple to compound instructions

Prompt scaffolding is a strategy that helps learners gradually build more complex prompts as their language skills improve. Starting with basic tasks and slowly integrating conditions, qualifiers, and constraints allows learners to develop clarity and control in prompt-writing over time. This approach also aligns with task-based learning methodology, where language is acquired through progressive practice in real-world tasks (Willis & Willis, 2007).

The process can begin with basic commands:

A2 Level Prompt:
“Make a list of five animals and write one sentence about each.”

Then move to structured multi-part tasks:

B1 Level Prompt:
“Write a paragraph about your favorite holiday. Use past simple tense and at least five adjectives.”

Eventually, advanced learners can compose compound prompts:

C1 Level Prompt:
“Compare two different forms of renewable energy. Explain the benefits and challenges of each, using formal language and organizing your answer into two paragraphs.”

By building prompts in this structured way, learners can focus on manageable tasks while progressively developing their ability to formulate complex instructions. Teachers may use prompt templates or sentence starters to facilitate this development, gradually reducing support as learners become more confident.

This approach is supported by research into cognitive load and language development, which indicates that reducing complexity at the initial stages helps learners process new language more effectively (Sweller et al., 2011). By managing the linguistic demands of prompt-writing, learners can better concentrate on the functional outcomes of their interactions with AI tools.

Sample prompt progressions by proficiency level

Below is a set of sample prompts to illustrate how complexity can be adapted according to learner level:

A2 (Elementary):
“Write five sentences about your daily routine. Use the present simple.”

B1 (Intermediate):
“Describe your favorite restaurant. Use linking words like ‘because,’ ‘but,’ and ‘so.’”

B2 (Upper-Intermediate):
“Write a short email to your boss explaining why you cannot come to work tomorrow.”

C1 (Advanced):
“Write an essay discussing whether social media has a positive or negative effect on communication. Include an introduction, two body paragraphs, and a conclusion.”

By differentiating the level of complexity, educators can tailor prompt-writing practice to suit each learner’s developmental stage and support their gradual progression.

Simplifying prompts for ESL/EFL learners requires attention to linguistic clarity, structural coherence, and the progressive development of complexity. Through the use of plain language, high-frequency vocabulary, well-defined instructional goals, and scaffolded practice, educators can support learners in developing the confidence and competence to engage meaningfully with AI tools. These principles not only improve the quality of AI interactions but also strengthen core language skills in vocabulary, grammar, and writing.

Teaching Prompt-Writing in the Classroom: Strategies for Educators

Teaching prompt-writing as a classroom skill offers a new and dynamic way to enhance language development, digital literacy, and learner autonomy. As generative AI becomes increasingly relevant in language education, it is important for educators to introduce students to this tool in a structured, pedagogically sound manner. This section outlines practical strategies for integrating AI prompt-writing into ESL/EFL classrooms, addressing lesson planning, learner levels, feedback cycles, and collaborative practices.

Integrating AI prompt-writing into lesson plans

To effectively teach prompt-writing, educators should treat it as an integrated skill, rather than a standalone activity. Prompt-writing can be included in reading, writing, speaking, or listening lessons as a means of generating content, checking understanding, or providing personalized feedback. For example, in a writing class focused on opinion essays, students might be asked to generate a list of arguments with the help of an AI tool. The prompt might be: “Give me three arguments for and against banning cars in city centers.”

Lesson plans should include clear objectives for prompt-writing tasks. These might involve identifying appropriate prompt structures, editing unclear prompts, or comparing different outputs in terms of relevance and language level. According to Hockly (2023), effective use of digital tools in the classroom depends on clearly linking the tool to pedagogical goals rather than using it for novelty.

Teachers can also build AI-related learning outcomes into their weekly planning, such as:

• Students will be able to write a prompt that generates relevant vocabulary on a chosen topic.
• Students will be able to revise a prompt to improve clarity and specificity.
• Students will be able to evaluate the usefulness of an AI response.

These outcomes help ensure that AI use is tied to language learning targets and is assessed in a structured way.

Age- and level-appropriate instructional techniques

Prompt-writing instruction should be tailored to the age and proficiency level of the learners. Young learners may benefit from highly scaffolded tasks and visual aids, while adult learners can handle more independent, goal-oriented exercises. Similarly, the complexity of prompts and AI responses must be adapted to CEFR levels.

For A2 learners, prompt-writing might focus on sentence patterns and vocabulary control. A task might be: “Write a sentence asking ChatGPT to give you five fruits.” Teachers can provide prompt stems such as “Can you give me…?” or “Please tell me five…,” allowing learners to practice both grammar and functional expressions.

B1 and B2 learners can be introduced to multi-step prompts. For instance, “Summarize this paragraph using simple words. Then give three new words and their meanings.” These learners benefit from seeing how prompts can include multiple tasks in one sentence and how AI tools can be used to practice vocabulary or comprehension.

At the C1 level, learners can explore conditional prompts and complex structures. A C1 student might write: “Compare two education systems and explain which is better, using formal language and examples from research.” These types of tasks align with academic writing goals and allow learners to use AI for brainstorming, planning, and lexical development.

As suggested by Reinders and White (2016), adapting digital tasks to learner profiles is essential for engagement and successful language acquisition. Teachers must be mindful of cognitive demands and provide clear models and expectations at every level.

Feedback loops using AI-generated responses

Feedback plays a central role in language learning, and AI can enhance this process by generating immediate, accessible responses. However, learners must be taught how to interpret, evaluate, and revise based on the feedback they receive. AI-generated outputs should not be accepted uncritically but examined as a source of reflection and discussion.

A simple activity involves students writing prompts and reviewing the AI’s response in pairs. For example, a student might write: “Correct this sentence: She go to school every day.” The AI’s correction and explanation are then discussed with a partner: “She goes to school every day. ‘She’ takes the -s ending in the present simple.”

Teachers can guide students in assessing whether the AI’s explanation is understandable and whether the correction matches classroom instruction. Learners should also be encouraged to revise their original prompt to improve clarity. For example, changing the prompt to “Please fix this sentence and explain why: She go to school every day.”

This cycle of writing, reviewing, and revising promotes metalinguistic awareness and can be tied to formative assessment. According to Burstein et al. (2022), technology-based writing tools can foster language development when used as part of guided learning activities that include teacher intervention and peer discussion.

Teachers may also introduce response rubrics or checklists for evaluating AI output. These might include:

• Is the AI’s response related to the prompt?
• Is the language level appropriate?
• Are there any confusing parts?
• What could I change in my prompt to improve the answer?

Such tools help shift students from passive to active engagement with AI and reinforce analytical thinking.

Collaborative writing exercises and peer support

Collaborative learning enhances both language acquisition and confidence. Prompt-writing lends itself well to group work, as students can brainstorm, evaluate, and refine prompts together. Group tasks also expose learners to different ways of phrasing instructions and allow for immediate peer feedback.

One activity involves having small groups create three different prompts to ask about the same topic, then comparing the AI responses. For example, the topic might be the environment. Group A writes: “Tell me about the environment.” Group B writes: “Explain why pollution is a problem in cities.” Group C writes: “List three ways to protect the environment, using simple words.” Students analyze which response was most useful and why, then revise their prompts accordingly.

Another collaborative approach is peer coaching, where more advanced students support less experienced learners in refining their prompts. This can take the form of “prompt clinics” where students review and improve one another’s prompts before submitting them to AI tools. These activities support language development, promote peer interaction, and build classroom respect.

TeachingEnglish (British Council, 2022) recommends using group-based language production tasks to foster engagement and critical thinking. Incorporating collaborative AI use into task-based learning projects allows prompt-writing to function not just as a linguistic exercise but as a communicative and creative one.

Practical examples and classroom integration

Below are some examples of how prompt-writing can be integrated into regular lesson themes:

Writing class (B1 level):
Objective: Practice paragraph structure
Activity: Students write a prompt asking AI to create a sample paragraph about their hometown.
Prompt example: “Write one paragraph about a small town. Include a topic sentence and three supporting ideas.”

Speaking class (A2 level):
Objective: Practice asking questions
Activity: Students write prompts to generate five conversation questions on a chosen topic.
Prompt example: “Give me five easy questions to ask about travel.”

Reading class (B2 level):
Objective: Practice summarizing texts
Activity: After reading a news article, students write prompts to generate a summary.
Prompt example: “Summarize this article in 50 words. Use B2-level vocabulary.”

In each of these cases, the prompt-writing task is directly linked to the skill focus of the lesson, reinforcing both AI literacy and core language outcomes.

Teaching prompt-writing in the ESL/EFL classroom supports a broad range of instructional goals, from vocabulary acquisition and grammar practice to digital literacy and learner independence. By embedding prompt-writing into lesson plans, adapting instruction to learner profiles, using feedback loops, and encouraging collaboration, educators can make AI a valuable and respectful tool in language education. Structured and intentional use of AI can transform passive learning into an active, exploratory process that strengthens linguistic competence and classroom engagement.

Digital Tools and Resources for ESL/EFL Prompt-Writing Support

Incorporating digital tools into ESL/EFL instruction offers valuable opportunities for learners to engage with language in interactive and adaptive ways. When it comes to teaching AI prompt-writing, a range of online tools can provide practical support by assisting learners in structuring clear prompts, checking grammar and vocabulary, and interacting with generative AI in real time. This section reviews useful platforms for both learners and educators, while also highlighting essential considerations related to safe and responsible use of AI technologies in the classroom.

AI writing assistants: ChatGPT, Grammarly, and QuillBot

Several AI-powered writing assistants are now widely available to support learners in constructing and refining English prompts. Among the most notable is ChatGPT, a generative AI developed by OpenAI that produces context-sensitive responses based on user input. ChatGPT is particularly useful for learners who want to experiment with different ways of phrasing prompts and observe how wording influences responses. For example, a learner can compare how the AI responds to “Tell me about pollution” versus “List three effects of air pollution in cities using simple English.”

Grammarly, although not a generative AI in the same way as ChatGPT, serves a complementary role by providing real-time grammar and vocabulary suggestions. Learners can use Grammarly to polish prompts before submitting them to AI models, ensuring greater clarity and grammatical correctness. Grammarly also explains errors, helping learners understand the rules behind the corrections (Common Sense Education, 2023).

QuillBot is another valuable tool that specializes in paraphrasing and sentence restructuring. ESL/EFL students can use it to reformulate vague or grammatically incorrect prompts into clearer alternatives. For example, a learner writing “Say how people don’t like noise” might use QuillBot to generate “Explain why people dislike loud environments,” resulting in a more precise and appropriate prompt.

These tools support different stages of prompt development, from planning and editing to execution, and can be used individually or in combination to improve learner outcomes.

Prompt-building platforms and examples

Some platforms are specifically designed to support users in learning how to craft effective prompts for AI systems. These prompt-building tools offer templates, examples, and interactive guidance to help users refine their input. For instance, LearnPrompting.org provides tutorials and structured exercises that teach users how to design prompts for tasks such as summarizing texts, generating questions, or drafting essays (LearnPrompting, 2024).

OpenAI also maintains a library of sample prompts within its documentation, which educators can adapt for classroom use. These samples are especially helpful when introducing learners to various types of prompts, such as instructional, comparative, or explanatory queries. For example, learners might be given the task of modifying the sample prompt “Translate this text into French” into a version suitable for English learning purposes, such as “Explain this sentence using B1-level English.”

Teachers can also create a class prompt bank, categorizing prompts by topic or skill area. This allows students to refer to and learn from effective examples while building their own prompts. Engaging with real examples helps reduce the cognitive load associated with task initiation and supports learners in recognizing the elements of successful prompts (VanLehn, 2011).

Vocabulary and grammar support tools

Prompt-writing is not only a digital task but also a linguistic one, requiring a solid foundation in grammar and vocabulary. In addition to AI assistants, learners benefit from tools designed to support lexical and structural accuracy. One such tool is Ludwig.guru, a search engine for contextualized sentence examples. Ludwig allows learners to compare their sentence with authentic uses of similar structures, helping them refine grammar and word choice before finalizing their prompt.

Rewordify.com is another useful tool that simplifies English text, allowing learners to see easier versions of complex sentences. Students who receive a difficult AI response can paste it into Rewordify for simplified output, thus closing the comprehension gap and reinforcing vocabulary development. Teachers can also encourage students to use learner dictionaries such as the Cambridge Dictionary or Longman Dictionary of Contemporary English, which include example sentences and collocations tailored for language learners.

For grammar practice, websites like Perfect English Grammar and EnglishPage provide explanations and interactive quizzes, which can serve as warm-up activities before prompt-writing sessions. These resources help learners build the accuracy needed to create syntactically correct and semantically clear prompts.

Safety, privacy, and usage guidelines for students

While AI tools offer many benefits, their classroom use should be accompanied by clear safety, privacy, and usage guidelines. Learners must understand that AI tools are not infallible and that generated responses should be critically evaluated rather than accepted without question. As outlined in EdSurge’s (2023) guidelines for AI in education, teachers should help students develop awareness of the limitations of AI, such as occasional factual errors, lack of source attribution, and the potential for biased or inappropriate content.

Students should also be informed about data privacy when using online tools. Many AI platforms store user interactions to improve performance, which may raise concerns about personal information and educational data. Teachers are encouraged to review the privacy policies of tools like ChatGPT or Grammarly before assigning them for classroom use. In many settings, it is advisable to use institutional accounts or guest modes that limit data tracking.

To support safe usage, educators can introduce responsible AI use as a topic within digital literacy modules. Lessons might include discussions about the differences between AI-generated content and human writing, the importance of verifying information, and ethical considerations such as plagiarism and authorship. The TeachingEnglish platform from the British Council (2023) offers resources on digital safety that can be adapted for these purposes.

Digital tools can significantly enhance ESL/EFL learners’ ability to write clear and effective AI prompts, especially when used with proper instructional support. Writing assistants like ChatGPT, Grammarly, and QuillBot help at various stages of prompt formulation and editing, while vocabulary and grammar tools ensure linguistic accuracy. Prompt-building platforms and curated examples further support learners in developing skills and confidence. However, to use these tools responsibly, educators must also address privacy, safety, and ethical considerations. With thoughtful integration, digital tools can serve as valuable assets in teaching prompt-writing and promoting informed engagement with AI technologies.

Encouraging Metacognition and Reflective Prompt Use

As ESL/EFL learners increasingly engage with AI tools such as ChatGPT and Bard, developing reflective skills around prompt-writing becomes essential. Metacognition—the ability to think about one’s own thinking—plays a critical role in language development and supports learners in becoming more independent, strategic, and effective in their use of digital tools. This section explores why reflective practices matter in AI prompt-writing, and how educators can foster critical thinking and revision habits through structured classroom activities.

Why reflection matters in prompt-writing

Metacognitive strategies in language learning have been shown to enhance self-regulation, comprehension, and transfer of knowledge across tasks (Anderson, 2002). When applied to AI prompt-writing, reflection allows learners to identify what makes a prompt effective or ineffective, recognize patterns in their own errors, and refine their approach based on outcomes.

Prompt-writing is not only a linguistic task but also a problem-solving activity. Learners must assess their communication goals, anticipate how the AI might respond, and revise when results do not meet expectations. Without reflective awareness, learners may repeatedly make the same errors, accept irrelevant outputs, or misunderstand why a response was unhelpful.

Encouraging metacognition helps learners become more intentional in their use of AI. For instance, a student who notices that the AI often misunderstands vague requests may begin to write more specific, structured prompts. This iterative process of questioning and adjusting fosters a deeper understanding of both language and digital interaction (Oxford, 2017).

Encouraging learners to evaluate AI outputs

Teaching students to critically evaluate AI-generated responses is a vital step in developing effective prompt-writing habits. Many learners may accept AI answers at face value, assuming their correctness and relevance without analysis. However, generative AI can produce inaccurate, confusing, or overly complex content, particularly when prompted by unclear or ambiguous instructions.

Educators can support evaluation skills by introducing reflection checklists. These may include questions such as:

• Did the AI understand my prompt?
• Was the response on-topic and clear?
• Did the vocabulary level match my needs?
• What part of my prompt could I change to improve the answer?

Using these questions, learners can practice identifying mismatches between input and output. For example, a student might write, “Write a short story about animals,” and receive a response that is too long or contains advanced vocabulary. Upon reflection, they may revise the prompt to: “Write a story for children about three animals. Use simple words and keep it under 100 words.”

According to Lai and Teng (2011), the development of evaluative judgment is a key element in fostering learner autonomy. By guiding students to analyze the cause-and-effect relationship between prompts and AI responses, teachers promote more effective interaction and decision-making.

Strategies for prompt revision and refinement

Just as writers revise drafts to improve clarity and impact, ESL/EFL learners should be encouraged to revise their prompts based on feedback and results. Prompt revision involves identifying ineffective wording, adding detail, simplifying complex sentences, or adjusting the focus of the request.

A classroom activity might involve presenting learners with an AI response that lacks relevance or clarity, followed by a discussion of how the original prompt might be improved. For example:

Initial prompt: “Tell me about food.”
AI response: A long and general explanation about food from different cultures.

Improved prompt: “List five traditional foods from Japan. Explain each one in one sentence.”

By comparing the results, students learn how specificity and structure affect the outcome. Teachers can also implement peer feedback sessions where learners share prompts, evaluate each other’s inputs and outputs, and make joint revisions.

An effective teaching strategy is to ask learners to submit two or three versions of the same prompt, highlighting how each change affects the result. This reinforces the concept that prompt-writing is not a one-step process, but an iterative one that improves with practice and attention to detail (Poehner & Infante, 2017).

Teachers should also model the revision process through live demonstrations, showing how even small modifications can significantly influence AI behavior. These examples help demystify the AI’s functioning and empower learners to take control of the interaction.

Building a digital prompt portfolio for learner growth

To track progress and support long-term development, educators can encourage learners to maintain a digital prompt portfolio. This portfolio serves as a space for collecting, reviewing, and reflecting on prompt-writing attempts and AI responses over time. It can include:

• Original prompts and AI-generated outputs
• Revised versions of prompts
• Reflection notes on what worked and what didn’t
• Vocabulary or grammar notes from AI feedback
• Screenshots of helpful interactions

The act of compiling and revisiting prompts encourages learners to identify patterns in their progress and challenges. For example, a learner may notice that they consistently forget to specify the desired length of a response or neglect to define the topic clearly. By keeping a prompt journal, these trends become visible, and improvement becomes measurable.

Teachers can assess these portfolios periodically to provide formative feedback. They can also use them as a basis for one-on-one conferences to set individualized learning goals. As Benson (2011) notes, reflection and self-assessment are central to developing learner responsibility and agency in the classroom.

Digital portfolios can be created using tools such as Google Docs, Padlet, or even classroom learning management systems like Moodle or Microsoft Teams. The key is to ensure that the portfolio is accessible, organized, and used regularly as part of the learning process.

Encouraging reflection in AI prompt-writing equips ESL/EFL learners with the metacognitive skills needed to become more accurate, efficient, and independent in their language use. Through evaluating AI responses, revising prompts, and maintaining a digital portfolio, learners build awareness of their own learning strategies and become more adept at using AI as a language-learning resource. These reflective practices not only enhance prompt-writing but also foster habits of critical thinking, responsibility, and sustained engagement—qualities essential for long-term language development in a digitally mediated world.

Conclusion

As the use of generative artificial intelligence becomes increasingly integrated into educational contexts, the ability to craft effective AI prompts is emerging as a vital digital and linguistic skill. For ESL/EFL learners, prompt-writing is not simply a technical task; it is a communicative process that reinforces essential language competencies such as vocabulary selection, sentence structure, and clarity of expression. At the same time, it offers unique opportunities for learners to access personalized feedback, engage in meaningful practice, and experiment with language in a low-risk environment.

This article has highlighted the multifaceted nature of AI prompt-writing, beginning with an overview of how generative AI models interpret user input and continuing through the challenges that ESL/EFL learners often face when writing prompts. These include linguistic limitations, pragmatic misunderstandings, and emotional barriers. In response to these challenges, educators can support learners through clear instructional strategies such as using plain language, structuring prompts with purpose, avoiding idiomatic phrases, and gradually building complexity through scaffolding.

In the classroom, prompt-writing can be effectively incorporated into a variety of lesson formats and skill areas. It provides a dynamic platform for integrating writing, reading, speaking, and listening tasks with real-time feedback. When aligned with learner levels and supported by guided reflection, AI becomes a tool for growth, not substitution. Teachers can further support student engagement through collaborative tasks, feedback loops, and prompt-revision activities, all of which encourage deeper learning and critical thinking.

The role of metacognition is particularly important in this process. By teaching students to reflect on the quality of their prompts and evaluate the usefulness of AI-generated responses, educators help foster learner autonomy and digital responsibility. Strategies such as maintaining prompt portfolios, journaling interactions, and using evaluation checklists promote self-awareness and long-term improvement.

It is equally important to approach AI integration with care and responsibility. While these tools offer significant benefits, they are not without limitations. Teachers and students must be mindful of the quality and accuracy of AI outputs, as well as privacy, safety, and ethical considerations. AI should be treated not as a replacement for authentic language instruction but as a support mechanism—one that complements pedagogical goals and reinforces respectful, learner-centered environments.

In summary, prompt literacy is a skill that will continue to gain relevance in the years ahead. By equipping ESL/EFL learners with the ability to write clear, purposeful prompts, educators are not only supporting language acquisition but also preparing students for future academic, professional, and digital communication contexts. The integration of AI into language learning holds great potential, but its impact depends on thoughtful implementation, learner reflection, and sustained pedagogical support. With the right strategies, prompt-writing becomes a bridge between traditional instruction and the evolving world of intelligent technologies—one that empowers learners and enriches language education.

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