Prompt Power: Teaching ESL Students to Maximize AI Responses

Introduction

Artificial intelligence (AI) is rapidly transforming the way people communicate, learn, and interact with technology. In the field of English language teaching, this shift presents exciting new opportunities and complex challenges. For English as a Second Language (ESL) learners and their teachers, one of the most promising developments lies in how AI-powered tools, particularly large language models (LLMs) such as ChatGPT, can serve as personalized language tutors, writing assistants, and conversation partners. However, while these tools hold significant potential, their effectiveness hinges largely on the quality of the input they receive. In short, the better the prompt, the better the response.

Prompt engineering, the practice of crafting clear, purposeful input to AI systems, is emerging as a critical digital literacy. This skill not only enables more relevant and accurate AI-generated responses but also promotes deeper metacognitive engagement for language learners. Students who understand how to shape their prompts intentionally can access richer, more meaningful language practice from AI, supporting grammar development, writing fluency, reading comprehension, and vocabulary acquisition. For ESL learners in particular, prompt engineering can act as a scaffold that amplifies communicative competence while encouraging learner autonomy.

Yet despite the clear importance of prompting, few English classrooms explicitly teach students how to engage with AI tools effectively. Many ESL learners struggle to phrase their queries clearly or assume that AI-generated output is always reliable. Without guidance, students may become over-reliant on the tool, use it passively, or accept unverified content without critical thinking. Therefore, educators must take an active role in teaching prompt design, treating it as both a technical and linguistic skill that is integral to modern language learning.

This article aims to support English teachers and education professionals by offering a comprehensive overview of how to introduce and integrate prompt engineering in ESL classrooms. Beginning with an explanation of prompt engineering and its relevance to language learners, the discussion will explore the cognitive and linguistic benefits of training students to write better prompts. It will also address common difficulties ESL learners face when using AI, offer strategies for teaching prompt construction, and provide practical examples of prompt types aligned with different language learning goals. The final sections will focus on how to assess prompting skills in a way that promotes growth and confidence.

In equipping ESL students with the ability to prompt AI systems effectively, educators are not only helping them interact more fruitfully with emerging technologies but are also nurturing essential skills for communication, critical thinking, and lifelong learning. As digital tools become more deeply embedded in educational environments, prompt engineering is no longer a novelty—it is a necessity.

Understanding AI and Prompting in the ESL Context

What is Prompt Engineering?

Prompt engineering is the practice of crafting precise, intentional input to direct the behavior of an artificial intelligence system, particularly large language models (LLMs) such as ChatGPT, Bard, or Claude. Unlike traditional search engines that rely on keywords, AI-driven models interpret natural language prompts to generate human-like responses. The quality and structure of these prompts significantly affect the output, influencing not only the relevance of the content but also its tone, clarity, and depth. In educational settings, particularly in language instruction, prompt engineering becomes a valuable strategy for enhancing learner interaction with AI tools.

Prompt engineering may involve specifying the format of the response, the role the AI should play (such as a teacher, peer, or editor), the tone of the message, or even the level of vocabulary to be used. For example, a simple prompt like “Explain past perfect tense” might yield a generic result, while a more tailored prompt, such as “You are an English teacher. Explain the past perfect tense to an intermediate ESL student using simple examples” produces a more pedagogically useful outcome. This act of shaping language input to elicit targeted output reinforces students’ critical thinking, pragmatics, and understanding of register skills highly applicable in language learning.

Prompt engineering, while technical in origin, is closely aligned with language awareness and communicative intention, making it uniquely relevant to ESL instruction (Kasneci et al., 2023; Zawacki-Richter et al., 2019).

Why Prompting Matters for ESL Learners

For ESL learners, particularly those at intermediate or advanced levels, the ability to construct effective prompts directly influences the usefulness of AI-generated responses. A poorly framed prompt may produce output that is too complex, off-topic, or inaccurate. Conversely, a well-phrased prompt allows learners to access content that aligns with their specific language goals, proficiency levels, and contexts of use. This alignment not only supports task-based language development but also reinforces learners’ control over their own learning processes.

Prompting can also serve as a form of communicative rehearsal. As students develop skill in framing prompts clearly and strategically, they exercise grammatical accuracy, lexical precision, and syntactic control—often without realizing it. The very act of thinking through a prompt mirrors the formulation process used in real-world communication, such as writing an email, asking for directions, or participating in a job interview.

Moreover, prompt construction reinforces awareness of genre and purpose. Students begin to see how language changes based on task demands. This metalinguistic reflection encourages ESL learners to think not just about what they want to say, but also how to say it appropriately to receive the intended response. As AI systems respond to user input with detailed, contextually relevant language, they offer learners immediate feedback on the effectiveness of their communication (Dwivedi et al., 2023).

Research suggests that engagement with LLMs through structured prompts can support second language acquisition by enabling learners to repeatedly model and receive language forms in relevant contexts (Smutny & Schreiber, 2023). Furthermore, learners develop the cognitive flexibility to rephrase prompts, troubleshoot output mismatches, and iterate toward clearer interactions—core skills for autonomous language use.

AI as a Language Partner

One of the most compelling roles that AI can play in ESL education is that of a responsive and nonjudgmental language partner. Unlike human interlocutors, LLMs are always available, never fatigued, and capable of adjusting their tone, vocabulary, and response style based on user input. This flexibility makes AI an ideal tool for language learners who may lack frequent access to native speakers or real-time conversational practice.

AI tools can simulate a range of communicative tasks, from formal essay writing to casual dialogues, allowing learners to practice varied registers and genres in a low-pressure environment. For instance, a student preparing for a speaking exam can prompt the AI with, “Ask me five questions about travel and correct my grammar after each response,” thus receiving both interactive practice and instant feedback. Similarly, writing prompts such as “Write a 150-word description of your favorite holiday, and suggest improvements to vocabulary and sentence variety” enable learners to generate and revise text with support.

The use of AI as a language partner also enhances learners’ exposure to authentic language models. While not infallible, tools like ChatGPT draw on extensive datasets to produce coherent and contextually appropriate text, offering ESL learners access to linguistic input that mirrors real-world use. This supports the development of receptive skills such as reading and listening, as well as productive skills like speaking and writing.

Additionally, when learners actively prompt AI, they become more than passive recipients of information. They engage in meaning negotiation, revise their phrasing, and evaluate the AI’s response—processes that are central to language acquisition. As learners adjust prompts to receive better output, they are, in effect, practicing both functional and strategic competence (Reich, 2023).

Nevertheless, students must approach AI-generated language critically. Teachers play a vital role in fostering analytical engagement, encouraging learners to verify information, question inaccuracies, and compare AI responses with authoritative sources. Used thoughtfully, AI tools can complement—not replace—human instruction, offering scalable, individualized practice that reinforces classroom learning.

Cognitive and Linguistic Benefits of Prompt Training

Supporting Metacognition and Autonomy

Metacognition—the awareness and regulation of one’s own learning processes—is a foundational element in language development. In the context of AI-assisted learning, crafting effective prompts demands that learners reflect on what they want to know, how they want it explained, and how they will use the information provided. This deliberate engagement enhances metacognitive awareness, prompting students to think not only about the content of their queries but also about the form and clarity of their language.

When ESL learners create prompts, they must anticipate how an AI system will interpret their words. This requires them to analyze their objectives, assess the clarity of their language, and revise accordingly. In doing so, learners begin to regulate their own learning more consciously. Prompt writing becomes a scaffolded exercise in planning, monitoring, and evaluating linguistic choices—key components of self-directed learning (Garrison, 2016).

Furthermore, learners who become skilled in prompt design often experience a sense of control over the learning process. Instead of passively receiving information, they initiate and steer interactions with the AI tool. This shift from consumer to co-constructor of knowledge aligns well with learner autonomy frameworks, where students take active roles in setting goals, selecting resources, and evaluating outcomes (Benson, 2011). Prompt training, therefore, supports the development of both metacognitive strategies and the learner’s ability to operate independently within digital environments.

AI platforms provide immediate feedback, enabling learners to test hypotheses about language use in real time. For instance, a learner might compare how the AI responds to “Explain the present perfect tense” versus “Give me five examples of the present perfect tense used in a travel story.” Reflecting on the differences in response encourages students to identify more effective input strategies, reinforcing self-awareness and refinement in both language and learning strategies (Kukulska-Hulme et al., 2021).

Improving Output through Input Control

One of the most significant linguistic advantages of prompt training lies in its capacity to improve the quality of AI-generated content through precise input. Tailored prompts guide the AI model to deliver responses that are contextually relevant, linguistically appropriate, and pedagogically useful. ESL students who learn how to control AI output by refining their input develop an essential skill: the ability to produce language that elicits desired responses, a cornerstone of effective communication.

This input-output relationship reflects principles of interactionist language learning theory, which emphasizes negotiation of meaning and the adaptation of language to achieve communicative success (Long, 1996). As students revise prompts to better match their intended goals—whether for grammar clarification, vocabulary expansion, or paragraph revision—they actively engage in this negotiation process. The prompt becomes not only a request but a linguistic performance that requires accuracy, conciseness, and strategic formulation.

When students fine-tune their prompts, they indirectly practice syntax, morphology, and register. For example, a vague prompt such as “What is past tense?” might yield a brief and technical answer, while a prompt like “Can you explain the simple past tense with 3 examples about a recent holiday?” generates more extended, contextually relevant content. The refinement of prompts over time fosters increased grammatical accuracy and syntactic variety, both critical for intermediate and advanced ESL learners.

This feedback loop also supports lexical development. Prompting AI to “Rewrite this paragraph with more advanced vocabulary” or “Replace basic verbs with stronger alternatives” can yield vocabulary-rich alternatives that students can analyze and internalize. The act of revising prompts based on AI responses encourages repeated exposure to target language forms, aiding retention and application (Godwin-Jones, 2023).

Ultimately, the ability to manipulate input to achieve clearer output promotes a deeper understanding of how language functions in specific contexts. Students learn to adjust for tone, formality, level of detail, and other pragmatic features of communication—skills that are not easily acquired through textbook learning alone.

ESL-Specific Learning Gains

Prompt training offers particular benefits to ESL learners at intermediate to advanced proficiency levels, who often require more nuanced language input and output than beginner learners. These students are typically capable of interacting with AI tools meaningfully but may not yet have the strategic awareness needed to maximize their use. Structured prompt practice helps bridge this gap by fostering both linguistic accuracy and communicative effectiveness.

For learners at these levels, developing confidence in language production is a key concern. Prompt training allows students to explore language without fear of negative evaluation. Because AI tools do not judge or penalize, students may feel freer to experiment with grammar, vocabulary, and discourse structures. This sense of safety can encourage more frequent and sustained language use, which supports fluency development (Yuan & Ellis, 2003).

Another advantage is the opportunity for focused, customized practice. AI tools can be prompted to simulate job interviews, role-plays, academic discussions, or email correspondence, giving learners targeted practice in domains relevant to their goals. Prompting the AI to “Act as my speaking partner in an IELTS interview about technology” allows students to engage in practice that is not only authentic but also adaptable. This kind of dynamic interaction is especially valuable in preparing students for real-world communication tasks where responsiveness and appropriateness matter.

Importantly, ESL students also gain greater control over the form and content of their practice. By writing their own prompts, they shape the language input to meet personal needs. This alignment between learner intention and linguistic output increases the relevance of learning, promoting deeper engagement and motivation. Research on language learner agency has shown that when students see a direct link between their actions and learning outcomes, they are more likely to persist and self-correct (Ushioda, 2011).

In sum, prompt training enhances language awareness, supports productive skills development, and empowers ESL learners to take active roles in their language acquisition journey. By learning how to prompt effectively, students move beyond passive consumption of language input toward strategic, purposeful interaction that builds both confidence and competence.

Common Challenges ESL Students Face When Prompting AI

As AI-powered tools increasingly become part of language education, ESL learners are encountering unique challenges when attempting to communicate effectively with these technologies. While prompt engineering holds promise as a language learning strategy, it also introduces specific obstacles that educators must address. These challenges stem from linguistic limitations, misinterpretations between input and output, and an over-reliance on AI-generated content without critical engagement.

Language-Level Barriers

One of the most immediate difficulties ESL students face when using AI tools is related to their current language proficiency. Many learners, particularly those at lower-intermediate levels, struggle with vocabulary limitations, syntactic errors, and unfamiliarity with idiomatic or culturally appropriate phrasing. These limitations can make it difficult to craft clear and effective prompts, which in turn leads to confusing or irrelevant AI responses.

For example, a learner might input, “Tell me about fast tense” when they intend to request information about the past tense. The AI might still generate an answer, but it could misinterpret the prompt or provide unrelated content. Even small lexical errors can shift the meaning or cause the tool to respond inaccurately. This situation underscores the importance of linguistic precision and highlights how gaps in grammar or vocabulary can disrupt the effectiveness of AI as a language partner (TESOL International Association, 2023).

Syntactic issues are another common challenge. Learners who are unfamiliar with standard sentence structures might create overly complex or fragmented prompts. A sentence like “Explain grammar how can I use it good?” may confuse the AI and lead to output that lacks clarity or usefulness. These errors often reflect learners’ underlying grammatical struggles and indicate areas where direct instruction and scaffolded support are still necessary.

Moreover, ESL learners may not fully grasp the cultural conventions or pragmatic nuances embedded in language use. AI tools trained on large datasets often reflect dominant patterns of interaction rooted in native-speaker contexts. As a result, prompts that are culturally ambiguous or linguistically vague may yield responses that do not align with the learner’s intentions. Without a clear understanding of how tone, politeness, or indirectness function in prompts, students may inadvertently receive outputs that feel unnatural or difficult to interpret (Liu et al., 2023).

Misalignment in Output

Even when prompts are grammatically accurate, there can be a significant disconnect between what the learner expects and what the AI produces. Misalignment in output occurs when the response does not match the task’s intent or fails to support the learner’s objective due to insufficient detail or vague input. This problem is particularly pronounced when students use general or underspecified prompts such as “Help me write better,” which could generate anything from grammar advice to motivational tips.

AI tools interpret prompts literally and cannot infer deeper learner goals unless those are explicitly stated. Consequently, learners who are unfamiliar with how to contextualize their prompts may receive responses that are off-topic, overly broad, or irrelevant to their learning needs. This mismatch can be frustrating and may lead students to doubt their own abilities or the usefulness of the tool.

Additionally, AI models often generate responses based on probability rather than understanding. While the language may appear fluent and grammatically correct, it may lack coherence, factual accuracy, or instructional value. For instance, a learner asking for an example of the past perfect might receive an incorrect sentence if the AI miscalculates context. Because these tools simulate understanding rather than actually possessing it, the risk of producing content that looks polished but is pedagogically weak remains significant (Marcus & Davis, 2020).

In many cases, learners do not immediately recognize these flaws. This highlights the need for explicit instruction in evaluating AI output critically and for developing strategies to refine or revise prompts to correct for misalignment.

Over-Reliance and Misinformation

Another key concern is the risk of students relying too heavily on AI tools without critically assessing the responses they receive. AI-generated content can seem authoritative due to its fluency and speed, but it may include subtle inaccuracies, invented information, or contextually inappropriate suggestions. ESL learners, particularly those with limited experience in digital literacy or critical thinking, may struggle to detect such errors.

This issue is compounded by the fact that AI models, including ChatGPT, are not search engines and do not verify information against up-to-date databases unless specifically integrated with external tools. As a result, they may generate convincing but outdated or incorrect information. For example, an AI might provide a grammar rule that is no longer considered standard or offer examples that reinforce incorrect usage patterns. Learners who lack the ability to cross-check these responses may internalize faulty language models.

Teachers, therefore, play a crucial role in guiding students on how to evaluate AI responses. This includes modeling how to revise prompts, compare AI output with trusted sources such as grammar references or dictionaries, and reflect on the appropriateness of the language produced. Educators should also emphasize that AI is a supplementary tool, not a replacement for human feedback and peer collaboration (Zou et al., 2023).

Promoting a critical mindset toward AI use in language learning is essential. Learners need to be taught to ask: Is this response accurate? Does it match my task? Is the vocabulary appropriate for my level? Such questions not only help avoid misinformation but also reinforce higher-order thinking skills that contribute to long-term language development.

In addition, overuse of AI tools for writing or idea generation may reduce learners’ willingness to struggle through productive language tasks. If students rely on AI to write essays, correct grammar, or generate conversation topics without participating in the underlying cognitive and linguistic processes, their development may plateau. Teachers should balance AI usage with activities that encourage original thought, peer interaction, and creative expression.

Teaching Prompt Design in the Classroom

As artificial intelligence tools become increasingly integrated into educational environments, ESL instructors are in a strategic position to teach students how to engage with these technologies effectively. Teaching prompt design is not simply a technical task—it is a pedagogical opportunity to deepen students’ understanding of language structure, clarity of expression, and communicative purpose. This section outlines practical strategies for introducing prompt design to ESL students, including models for prompt structure, scaffolded classroom practice, and reflective techniques that strengthen the relationship between input and output.

Introducing Prompt Structure to Students

One of the most effective ways to initiate students into prompt engineering is by teaching a clear, repeatable framework. A widely useful model is the context–task–instruction structure, which breaks a prompt into three core elements:

1. Context: Provides background information or sets a scenario.
2. Task: Specifies the action or output required.
3. Instruction: Adds constraints or preferences (such as tone, vocabulary level, or format).

This framework helps ESL learners understand how to build complete and targeted prompts that AI can interpret accurately. For example, instead of saying “Tell me about grammar,” students can be guided to write:

“You are an English teacher helping intermediate ESL students (context). Explain how to use the past perfect tense in storytelling (task). Use simple examples and short sentences (instruction).”

By modeling and practicing this structure, learners begin to internalize the principles of effective communication. This framework not only clarifies expectations for the AI but also reinforces elements of genre, audience, and purpose—critical skills in both spoken and written communication (Webb et al., 2023).

Teachers may first present the structure explicitly, using guided examples, and then challenge students to deconstruct or reframe prompts they encounter in real-world tasks. Over time, learners begin to grasp how subtle changes in wording affect the outcome and how their language choices shape the response they receive.

Scaffolded Prompt Practice

Scaffolding is essential when introducing prompt design to ESL students, especially for those unfamiliar with AI tools or advanced writing tasks. Teachers can begin with structured activities that gradually shift responsibility from the teacher to the learner, in line with Vygotsky’s concept of the Zone of Proximal Development (Vygotsky, 1978).

At the early stages, instructors might provide fixed prompts with minor gaps for students to fill in. For instance:

“You are a __________. Explain __________ using __________ examples.”

This controlled format allows students to focus on vocabulary and syntax while still engaging in meaningful input creation. Teachers can offer example completions and encourage learners to compare results generated from different versions of the same base prompt.

As students progress, activities can become more open-ended. One effective technique is prompt revision exercises, in which students are given an AI-generated response and asked to reverse-engineer the prompt that likely produced it. They then rewrite the prompt to make it clearer, more specific, or better aligned with a particular learning goal.

Another useful practice is prompt comparison, where students write two different prompts aimed at achieving the same learning objective, such as generating a paragraph using five new vocabulary words, and evaluate which one yields a more helpful output. This helps students notice how prompt length, specificity, and instruction influence response quality.

Role-play exercises can also be adapted to involve prompt design. For example, one student acts as the AI, and another must write and revise a prompt to elicit a specific response. The student in the AI role responds according to the clarity of the prompt. These activities not only encourage language production but also deepen learners’ awareness of how their input affects communication.

Teachers may also consider creating prompt portfolios, where students collect and reflect on their prompts over time, alongside the responses they generated. This serves as both a formative assessment tool and a tangible record of progress in clarity, complexity, and accuracy.

Encouraging Reflective Prompting

While creating effective prompts is essential, equally important is the ability to reflect on the quality of both prompts and the resulting AI output. Reflective prompting fosters critical thinking, error analysis, and transfer of learning, all of which contribute to long-term language development.

One method for promoting reflective practice is to use output comparison tasks. After receiving an AI-generated response, students are asked to evaluate it based on specific criteria: accuracy, relevance, level of formality, vocabulary range, and clarity. Teachers can introduce simple checklists or rubrics that guide this analysis. For example:

• Does the response answer my question clearly?
• Is the vocabulary appropriate for my level?
• Are the examples useful for my learning goal?

After reflection, students are encouraged to revise their original prompt and resubmit it to the AI, comparing the new response to the first. This iterative process helps them see how small adjustments can lead to significantly improved outcomes, reinforcing a feedback mindset.

Another reflective tool is the prompt journal, where students document their interactions with AI tools. Each entry might include the prompt used, the AI’s response, the student’s evaluation of the result, and any changes they would make next time. Such metacognitive tracking helps students notice patterns in their own language use and begin to anticipate how to improve future prompts (Lee & Gillespie, 2022).

Peer feedback can also enhance reflection. In pairs or small groups, students share their prompts and resulting AI outputs, offering suggestions for improvement. Teachers can provide sentence starters to support this feedback process, such as:

• “Your prompt is clear, but maybe you could add more detail about…”
• “The response is a little off-topic—perhaps changing the instruction to say…”

These discussions not only build collaborative skills but also reinforce the idea that language clarity is a skill that can be observed, discussed, and refined.

Finally, reflection should include guidance on recognizing when AI output is inaccurate or unhelpful. Teachers can model critical engagement with AI responses by highlighting incorrect grammar suggestions, unnatural phrasing, or factual errors. This helps students move beyond passive consumption and toward evaluative literacy—an essential competency in technology-enhanced language learning (Cotos, 2014).

Practical Prompt Types for ESL Learning Goals

Artificial intelligence tools such as ChatGPT and other large language models offer wide-ranging opportunities for language learners to engage in personalized, responsive, and context-driven practice. However, for these tools to effectively support language acquisition, the prompts used to guide them must be structured purposefully. Teachers who equip ESL students with practical, goal-oriented prompt types can significantly enhance learning across key language skills. This section outlines several categories of prompts designed to support vocabulary and grammar development, writing and speaking practice, and reading comprehension with contextual and cultural insights.

Prompts for Vocabulary and Grammar Development

AI tools can be particularly effective in helping ESL learners consolidate grammar structures and expand vocabulary. One of the most accessible uses of prompt-based interaction is to generate example sentences, rules, and word explanations. However, learners must be guided to structure these prompts clearly and specifically.

Prompt structures such as “Create five example sentences using the present perfect tense with time expressions like ‘already’ and ‘yet’” allow students to receive targeted input. This type of prompt ensures that the AI provides examples situated within a grammatical and lexical focus that matches the student’s current learning objective. It also enables immediate analysis and correction.

Another useful variation is the comparative grammar prompt: “Explain the difference between the past simple and present perfect, and give two examples for each.” This not only promotes contrastive analysis but also engages students in applying rules rather than memorizing them.

Vocabulary-focused prompts can also be adapted to suit thematic or situational needs. For example, “List 10 advanced adjectives to describe emotions, and use each in a sentence about a stressful day at work” helps learners see new words in context, increasing both retention and appropriate usage. Teachers can also guide learners to request explanations or synonyms using prompts like “What does the word ‘resilient’ mean? Give three synonyms and example sentences.”

AI models are especially valuable for exploring word collocations and formality. Learners can use prompts such as “What verbs commonly go with the noun ‘decision’? Use them in short examples” to study language chunks. This supports the development of formulaic sequences, which are essential for fluency and idiomatic usage (Nation & Webb, 2011).

When supported with instruction on evaluating and questioning the responses, these prompt types become a productive way to deepen grammar knowledge and increase lexical richness.

Prompts for Writing and Speaking Practice

AI-powered tools can simulate conversation partners, writing coaches, and peer reviewers, making them useful for practicing productive language skills. When used with intention, prompt-based tasks can assist learners in developing fluency, coherence, and communicative range.

For speaking practice, role-play and simulation prompts are particularly effective. Learners might use the prompt, “Pretend you are a hiring manager. Ask me five interview questions for an entry-level customer service job. After I answer each question, give me feedback on grammar and vocabulary.” This type of interaction replicates real-world speaking tasks while providing instant language-focused support.

Alternatively, conversation-based prompts such as “You are my speaking partner. Ask me travel questions, then ask follow-up questions based on my answers” encourage extended dialogue and spontaneous language production. Learners can adjust these prompts according to topic preferences, helping maintain motivation while reinforcing interactional competence (Bygate, 2021).

Writing prompts can serve multiple purposes depending on learners’ proficiency levels. Basic prompts such as “Write a paragraph about your favorite meal. Then suggest five ways to make the paragraph more interesting” encourage descriptive writing and self-editing. At more advanced levels, prompts like “Give me feedback on this IELTS Task 2 essay. Focus on grammar and coherence” allow learners to use AI tools for evaluation and revision guidance.

AI tools can also be used to model genre-specific writing. Prompts such as “Write a formal email to a teacher asking for an extension on a deadline. Use polite expressions and correct format” enable students to explore register, tone, and discourse conventions. Students may then edit the output or use it as a model for their own drafts.

Peer collaboration around prompt writing for writing and speaking tasks can further increase engagement. For example, learners could design prompts for each other and compare the AI-generated responses, discussing which prompts produced the most accurate or useful language. This promotes awareness of task design and encourages strategic language use (Lee, 2021).

Prompts for Reading Comprehension and Cultural Awareness

AI tools can also support receptive skills development, particularly reading comprehension. When paired with well-structured prompts, these tools can help learners summarize, paraphrase, or question written content, strengthening overall reading ability.

For instance, students might input a prompt such as “Summarize this article in 100 words and list five important vocabulary words with definitions.” This allows learners to practice identifying main ideas and key terms, two crucial reading subskills. Follow-up prompts like “Write five comprehension questions about the summary you just gave” can be used to promote deeper engagement and test understanding.

AI can also assist with paraphrasing, which is an important skill for both academic and general English learners. A prompt like “Paraphrase this paragraph in simpler English suitable for an intermediate learner” enables students to see multiple versions of the same content, helping them build confidence with rewording and restructuring texts.

In the realm of cultural awareness, AI tools can be prompted to explain language use across regions or contexts. For example, “Explain how people greet each other formally in Canada compared to Japan” can yield comparative insights, helping students understand the contextual use of language without relying on generalizations or stereotypes.

Additionally, learners can be encouraged to explore idiomatic expressions through prompts such as “List five common English idioms related to work and explain their meanings with examples.” This supports understanding of figurative language and builds pragmatic competence (Taguchi, 2015). Teachers should, however, remind students that AI-generated cultural information may not always be accurate or nuanced. Follow-up discussion and verification through trusted sources are necessary.

Finally, learners can use AI to generate adapted reading texts based on proficiency. Prompts like “Rewrite this news article for an intermediate English learner and add a glossary for difficult words” help make authentic materials accessible while reinforcing vocabulary acquisition and text structure awareness.

AI-generated reading tasks must be supported with critical discussion. Teachers should facilitate reflection by asking students to compare summaries or paraphrases across different prompts and evaluate which ones support better understanding. In this way, students gain insight into how language can be shaped for clarity, tone, and accuracy.

Evaluating and Assessing Prompting Skills

Teaching ESL learners how to write effective prompts for AI tools is only part of the instructional process. Equally important is developing methods to evaluate and assess their prompting skills in meaningful ways. Assessment in this area should not only verify student understanding but also promote reflection, refinement, and long-term growth. Because prompt writing is both a linguistic and strategic task, evaluating it requires attention to language accuracy, task clarity, and the alignment between input and AI-generated output.

Formative Assessment of Prompting

Formative assessment plays a central role in developing learners’ prompt design skills. This type of assessment focuses on ongoing feedback that guides student learning rather than assigning a final grade. In the context of prompt training, formative assessment can take the form of peer feedback, teacher commentary, and self-reflection, all of which encourage learners to evaluate how effectively their prompts influence AI output.

Peer feedback activities help students understand the features of a strong prompt through collaborative evaluation. In small groups, students can exchange prompts and corresponding AI responses, using guiding questions such as:

• Does the prompt clearly state what the AI should do?
• Is the language appropriate for the learner’s level?
• Does the response match the learner’s intended outcome?

These discussions expose students to multiple prompt styles and offer insight into different ways of achieving task clarity. Teachers can provide checklists or sentence starters to scaffold peer discussions and ensure that feedback remains constructive and focused (Brookhart, 2017).

Self-reflection is another essential element. Prompt journals or digital learning logs allow students to document the prompts they used, describe the responses they received, and evaluate whether the output met their expectations. Learners can then reflect on how they might revise their prompt to improve results. This process strengthens metacognitive skills and reinforces the importance of precision and clarity in language use (Andrade & Brookhart, 2020).

Teacher feedback remains vital, particularly in the early stages of instruction. Comments can focus on the structural features of prompts (e.g., whether context, task, and instruction are clearly defined), as well as on language accuracy and appropriateness. Teachers might also highlight how small adjustments in word choice or sentence structure can lead to better AI responses. Timely, targeted feedback helps students make immediate improvements while reinforcing core learning objectives.

Rubrics for Prompt Effectiveness

To ensure consistency in assessment and provide learners with clear expectations, educators can develop rubrics specifically designed for evaluating prompt effectiveness. A well-constructed rubric outlines key criteria that reflect both linguistic and functional aspects of prompt writing.

A typical prompt evaluation rubric may include the following categories:

1. Clarity of Purpose – Does the prompt clearly express the task or question?
2. Language Accuracy – Is the grammar and vocabulary appropriate and correct?
3. Completeness of Structure – Does the prompt include necessary elements such as context, task, and instruction?
4. Relevance of AI Output – Does the resulting response accurately address the learner’s objective?
5. Adaptability and Revision – Has the learner made effective adjustments to improve the prompt based on feedback?

Each category can be scored on a simple scale (e.g., 1–4 or 1–5), and descriptors should be detailed enough to guide both self-assessment and teacher evaluation. For example, a “4” in the Clarity of Purpose category might be defined as “Prompt clearly states a specific task and is easy to understand without ambiguity.”

Rubrics can also include space for qualitative feedback, allowing teachers to note specific strengths and suggest areas for improvement. Over time, students become familiar with the evaluation criteria and begin internalizing these expectations, improving their prompt quality even in unassessed contexts.

Importantly, rubrics can be adjusted to match the proficiency level of the learners. For beginner students, the focus might be on sentence-level accuracy and basic task formulation. For more advanced learners, criteria may emphasize precision, nuance, and the ability to elicit detailed or context-rich responses.

Fostering Continuous Improvement

Assessment should not be viewed as a final checkpoint but as part of a cycle of growth. Fostering continuous improvement in prompting skills involves building habits of reflection, encouraging iteration, and connecting prompt design to broader language goals.

One strategy for long-term improvement is to have students maintain a prompt portfolio. This digital or paper-based collection might include original prompts, the AI’s responses, notes on prompt revisions, and personal reflections. Reviewing the portfolio at intervals allows students to track their development and observe how small changes in their language produce better results.

Teachers can also incorporate revision tasks into classroom routines. For example, learners might return to a previous prompt and attempt to improve it based on past feedback or new learning. This reinforces the idea that prompt writing, like writing itself, is a process of drafting, refining, and editing.

Linking prompt practice to specific language objectives also helps students see its relevance. When learners are preparing for speaking exams, writing essays, or learning new vocabulary, teachers can show them how targeted prompts can support those goals. For instance, a student working on academic vocabulary might prompt the AI with, “Give me five academic words related to climate change, and use each in a sentence that could appear in an IELTS essay.”

Teachers may also introduce periodic reflection tasks, asking students to consider questions such as:

• What kind of prompts give me the best responses?
• What mistakes do I often make in writing prompts?
• How has my ability to guide AI responses improved?

Embedding these practices into regular instruction promotes a mindset of self-regulation and ownership of learning. Rather than viewing AI as a passive tool, learners begin to understand it as a resource they can control and refine through deliberate language use.

The goal is to move students from trial-and-error prompting to strategic prompting, where every interaction with an AI tool reinforces grammar, vocabulary, coherence, and communicative intent. With thoughtful assessment, students can achieve this progression while building essential skills for modern language learning environments.

Conclusion

Prompting is emerging as one of the most valuable language and digital skills ESL learners can acquire in the modern classroom. As AI tools become more embedded in educational practice, the ability to communicate effectively with these systems will increasingly influence how learners access, interpret, and produce language. Prompt engineering, once considered a niche technical practice, now sits at the intersection of communication, critical thinking, and applied linguistics. It is both a practical tool and a cognitive strategy, shaping how students learn and how they interact with the digital world around them.

Throughout this article, we have explored how prompt training supports metacognitive development, linguistic accuracy, learner autonomy, and engagement across all four language skills. We have also examined common challenges faced by ESL learners—such as language-level barriers, misalignment of output, and over-reliance on AI-generated responses—and proposed practical classroom solutions. Structured prompt models, scaffolded practice, and reflective analysis help students move beyond surface-level interactions toward meaningful, learner-driven exchanges with AI systems.

For English educators, this represents a timely opportunity. With their knowledge of language pedagogy, instructional scaffolding, and learner needs, ESL teachers are well-equipped to introduce prompt design as part of a broader communicative approach to language learning. Rather than seeing AI as a threat to traditional instruction, educators can reframe it as a dynamic resource that, when used critically and purposefully, supports personalized learning and language mastery.

The next steps for teachers involve experimentation and professional growth. Educators can begin by integrating prompt activities into existing lessons, starting with basic structures and gradually increasing complexity. Collaboration among colleagues, participation in workshops, and the development of shared classroom resources can also strengthen implementation. In time, prompt design may become a standard component of ESL curricula—taught not as a stand-alone topic but as an integrated part of how learners read, write, speak, and think in English.

Ultimately, teaching students to write effective prompts is about more than mastering a new technology. It is about fostering clarity of thought, precision in communication, and adaptability in a changing world. When students learn to prompt with intention, they learn to learn more effectively. In doing so, they gain not only language proficiency but also confidence, control, and readiness for the demands of 21st-century communication.

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