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Back-to-School Special Issue: August 2023
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Deciphering AI Speak: A Primer for English Language Educators
by Joshua M. Paiz and Ilka Kostka

If you are like us, AI (artificial intelligence) has come to play some role in a growing number of professional conversations—either at conferences, in meetings, or during breaks with fellow teachers. In these conversations, you have likely heard an almost dizzying array of jargon that might get in the way of clear communication. In this article, we offer a simple guide to some of the more common words and ideas surrounding AI so that you can better participate in these conversations and continue to advocate for yourself and for your multilingual learners. From there, we offer advice for gaining familiarity with AI tools.

AI Jargon

The field of AI is a subfield of computer sciences that is focused on creating intelligent computer systems. To begin, we draw your attention to some fundamental terms that come up in many conversations about AI and AI-based systems. In Table 1, to help you more comfortably engage in AI-focused conversations, we list the most common AI-related terms, working definitions, and, where relevant, examples of AI platforms.

  • Large language model (LLM): An AI system trained to understand and generate human-like text/speech, aiding in tasks like translation, summarization, and conversation.

  • Machine learning (ML): A field of AI that enables computers to learn from data and make predictions or decisions without explicit programming.

  • Natural language processing (NLP): An interdisciplinary field at the intersection of AI and machine learning that focuses on teaching machines to understand and interpret human language(s).

  • Transformers: AI technology that can consider a whole text, being aware of relationships between ideas across the entire text and how this creates unique context for meaning making, resulting in more accurate and coherent text generation.

  • Generative Pre-trained Transformer (GPT): Advanced AI model that uses the transformer architecture to generate human-like text based on a given prompt. It was trained on a set of data containing more than 400 billion tokens of text and code.

  • Pathways Language Model 2 (PaLM2): Large language model developed by Google that was trained on more than 4.6 trillion tokens of text and code and is seen as a more capable model than the most recent model GPT (i.e., GPT-3).

Table 1. AI Jargon for English Language Teaching Educators


Working Definition

Example Platforms

Generative AI (GAI)

Branch of AI that takes user input and produces original and meaningful output through the use of sophisticated algorithms and language models.

    OpenAI’s ChatGPT (text/code)
      Google’s Bard (text/code)
        Grammarly GO (text)
          OpenAI’s DALL-E (Images)
            Stability AI’s Stable Diffusion (images)
              StockImg.AI (images)
                Soundful’s AI Music Generator (music)
                  Twee (English language and literature lesson planning)

                  Constructive AI

                  Category of AI systems designed to enhance and improve existing user-generated content by applying modifications to address potential issues in that input.

                    Grammarly Basic/Pro (text)

                    Hemingway APP (readability)

                    Krisp (voice)

                    Adobe’s Audition (voice/sound)

                    Video2x (images/videos)

                      Assistive AI (AAI)

                      Agents that support users with some form of impairment, temporary or permanent, by enhancing abilities and improving accessibility to empower users and promote inclusivity.

                        Orcam (vision)

                        eSight (vision)

                        Proloquo (nonverbal-to-verbal)

                        Braina (voice control)

                        Ava (hearing)

                        AI agent(s)

                        Platform powered by AI to perform a certain function for the user.

                        See Generative, Constructive, and Assistive AI, above.

                        Prompt engineering

                        Process of crafting effective instructions or queries to guide AI models’ behavior by optimizing prompts to improve output quality, align with user objectives, and account for model capabilities.

                          Learn Prompting’s Prompt Engineering Guide (beginner to advanced users)

                          DAIR AI’s Prompt Engineering Guide (advanced user)

                          Getting Started With Generative AI

                          The use of generative AI in English language teaching (ELT) is relatively unexplored; yet, as technology continues to rapidly develop, educators will need to keep up with the latest developments. Following, we offer suggestions for building AI literacy skills and better understanding of how they work. Because of its ease of use and because it is currently freely available, we take ChatGPT as an example here of a path to skilling up. However, our advice can be generalized to many chat-based AI tools.

                          1. Begin With Free Tools

                          With the availability of free versions of many AI programs, teachers should not have to pay for most of these tools. For instance, ChatGPT 3.5 is currently freely available; users simply need to create an account and log in with their username and password. If a teacher decides to use a tool more regularly or extensively, they might opt to pay for a more powerful version to maximize its benefits through more capable models with expanded abilities.

                          2. Experiment and Explore

                          ChatGPT is simple to use. Users type a prompt into a box that resembles a chat box, and ChatGPT immediately produces human-like responses to the prompt. Considering this, we suggest appreciating the conversational nature. For instance, you can ask ChatGPT for clarity or you can continually refine your prompt until the AI produces a sufficient response. When using ChatGPT, for instance, creating effective prompts is critically important for getting useful output. This process is referred to as prompt engineering, and it is developing into its own field of scholarship and practice.

                          When teachers input effective prompts, they can use ChatGPT to create a lesson, generate ideas for in-class activities, and streamline materials creation. Table 2 shows a prompt that has been revised to push the AI to produce more meaningful output for an imagined academic writing class. You are welcome to try it for yourself, but because of the nature of generative AI, you will receive unique output each time you enter the same prompt. The Appendix (PDF) includes screenshots of these prompts to show how they appear in ChatGPT, as well as an explanation generated by ChatGPT about when to use idioms in academic writing and ideas for creating a handout for students.

                          Table 2. Generative AI Prompt: Before and After Engineering

                          Before Prompt Engineering

                          Create a handout on academic idioms.

                          After Prompt Engineering

                          I’m teaching an EAP class for first-year college students, and I want to teach my students about how we can use idiomatic expressions in academic writing. I already have the class planned and will introduce the fact that we typically avoid idioms in academic writing, but we do use idiomatic expressions like “shed light on” and “a double-edged sword”. Can you help me come up with a handout of 15-20 common idiomatic expressions with definitions and examples?

                          3. Upskill, Learn, Connect, and “Play”

                          Short of getting a master’s degree in computer engineering, you can learn more about AI and how it works a number of ways:

                          With the increasing amount of literature and resources about AI in education, finding materials is relatively easy.

                          Final Thoughts

                          The release of large language models like ChatGPT was certainly a shock for many because of its profound implications for teaching, learning, and work. We would like to emphasize that AI is never a replacement for classroom teaching and learning, and it is most certainly not a replacement for the expertise of the English language teacher. Instead, given the realities of much ELT labor, we see AI as a potentially powerful tool for the educator, allowing them to leverage their expertise and save time preparing for instruction.

                          Our students are also entering an AI-rich world—one in which AI literacy will permeate the workplace (McKinsey, 2023) and in which the risks of AI are unknown. As such, preparing them to critically engage with AI tools and products will become increasingly important. Perhaps more worrying is the potential for AI abuses, further creating the need for an educated citizenry who can critically engage with AI-generated material (Vincent, 2023). For these reasons, ELT practitioners who engage with AI tools can help equip their students with the skills needed to safely and effectively navigate a new world in which AI plays a prominent role.


                          McKinsey. (2023). The economic potential of generative AI: The next productivity frontier [White Paper]. McKinsey & Company.

                          Vincent, J. (2023, Apr 25). Republicans respond to Biden reelection announcement with AI-generated attack ad. The Verge.

                          Download this article (PDF)


                          Joshua M. Paiz, PhD, is a teaching assistant professor of EAP at George Washington University, Washington, DC, USA, and a teacher educator at Montgomery College and the Community College of Baltimore County. He holds a PhD in TESOL/applied linguistics from Purdue University. His scholarly work focuses on LGBTQ+-inclusive pedagogy and the application of AI and natural language processing to ELT.

                          Ilka Kostka, PhD, is a teaching professor at Northeastern University in Boston, Massachusetts, USA, where she teaches English language courses to undergraduate and graduate international students. Her primary interests include technology-facilitated language learning, flipped learning, and source-based writing instruction. She is the secretary of Northern New England TESOL, an affiliate of TESOL International Association.

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                          Table of Contents
                          TC Homepage
                          Deciphering AI Speak: A Primer for English Language Educators
                          10 ELT Must-Haves for the First Day of School
                          4 Strategies to Make Your Push-In Model Effective
                          Rotating Reading Stations for Building Literacy
                          Ask a TESOL Leader: Advice for New Teachers
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