ELT in the digital age: We have come a long way Thomas Strasser Educational Applications and Artificial Intelligence (AI) powered tools have been a source of creativity but also controversy in recent years, also in the EFL classroom. From questions of spell checkers to automated text-generating technologies, educators and researchers alike have debated the utility and limitations of AI-powered tools. This paper looks at the historical development of educational technology, mainly the role of AI in ELT and argues for a creative engineering approach to language learning that leverages the best of the analogue and digital world. Taking the example of the Midjourney bot, this article outlines how AI-powered tools can be used as an assistive catalyst for teaching, such as providing linear remedial drills, grading cloze exercises, analyzing texts, but also generating fully coherent, grammatically, and lexically sound texts. It also identifies key competencies, such as interdependency, required to effectively use AI-powered tools. These skills include being able to use language chunks produced by the AI to adapt and remix texts for new activities and contexts, a skill referred to as “creative engineering”. Drawing on examples from specific EFL teaching scenarios, this paper emphasizes the need for teachers to understand and use AI-powered tools in their teaching, pointing to their potential to elevate language learning in the 21 st century ELT classroom. 1. Fatboy Slim and the history of technology-enhanced learning. A superfast remix “You have come a long way, baby”. This is the title of Fatboy Slim’s hit album, which was released in the late 1990s. Critics praised his second album as a piece of art that combines dynamic dance music with electronic characteristics (cf. 909 Originals 2018: online). Norman Cook, the artist who took on the pseudonym Fatboy Slim, knew very well how to use the potential of his DJ equalizer and remixed famous rock music songs that fit the zeitgeist of the 90s. AAA - Arbeiten aus Anglistik und Amerikanistik Agenda: Advancing Anglophone Studies Band 48 · Heft 1 Gunter Narr Verlag Tübingen DOI 10.24053/ AAA-2023-0006 Thomas Strasser 122 The attention-grabbing album title and Cook’s DJ skills have an interesting correlation with the development of Computer Assisted Language Learning (CALL) - an approach, which was also founded and coined in the 1990s (cf. Schmidt and Strasser 2018: 220). Norman Cook’s remixing of various tunes and his musical exploration of different classical and electronic music styles during his career compares broadly to English Language Teaching (ELT) practitioners and their efforts to exploit digital technologies methodologically for their own teaching (cf. Grimm, Meyer, and Volkmann 2015; Hockly and Dudeney 2018). Indeed, technology-enhanced language learning and teaching (TELL) has come a long way. Starting in the 1990s, English as a Foreign Language (EFL) teachers used Skype, email and eTwinning to design interactive projects that connected language learners around the world (cf. Kettemann 1995). At that time, the internet was not as fast as it is today, but still language learners and teachers tried their best to interact and communicate mainly in their second language (L2), which was predominantly English at that time. At the beginning of the new millennium, the internet became more collaborative and constantly increased its social appeal. More and more social media platforms like Myspace, Facebook and Twitter became widely used, and they also started to make inroads into the work of many ELT professionals. Facebook and Twitter groups were established with the help of which learners were able to interact with their peers about language-relevant topics. However, it turned out that on these social networks, learners did not want to discuss school-related topics, they wanted to have their “safe” space among themselves, free from educational content conducted by their teachers (cf. Saylag 2013). In the mid-2000s, digital technologies became more app-based and mobile responsive (cf. Bachmair & Pachler 2014; Pachler & Turvey 2016), so an increasing number of digital language learning scenarios started to be conducted with a smartphone or tablet. Vocabulary learning apps grew in popularity. However, soon numerous ELT professionals discovered that focussing on the apps per se slowed the dynamics of the lessons since students and teachers simply needed an excessive amount of time to install the apps on different mobile phones and operating systems. Therefore, more and more educational companies developed browser-based, mobileresponsive Educational Apps (cf. Hirsh- Park 2011; Pasek et al. 2015; Strasser 2023) that can be accessed and operated without an actual installation. These tools have become ubiquitous, which means they are accessible and operable from any device at any time and any place. This temporal and spatial de-limitation of mobile technologies (Cohen 2010: 66) lead to a conceptual, performative, and paradigmatic change in the design of Technology-Enhanced Language Learning (TELL) lessons since learners acquired full access to curricular knowledge and their learning artefacts. Learners were therefore given the chance to exploit digital technologies in a far more interactive, collaborative, constructivist and co-creational way, ELT in the digital age 123 for example, using Google Docs for participatory text writing or Padlet 1 for mind mapping such as collecting topic-related word fields from the course book as a part of an exercise benefiting group dynamics. However, not all the EFL practitioners were excited about these new technologies. Now students had access to many knowledge repositories and were able to theoretically check everything the teachers said and taught, a scenario that has not been extremely popular among language teachers so far. A phenomenon called the “Laempel vs. LTE paradox” (Strasser in: Anders 2020: online) means that there is a constant paradigmatic struggle between instructivist knowledge brokers, i.e. a group of teachers that believe they are supposed to know everything, thus referencing “Lehrer Laempel”, an omniscient, strict teacher from Wilhelm Busch’s “Max and Moritz: A Story of Seven Boyish Pranks” (Busch & Schmidt, 1992), and the non-linear, almost subversive nature of the internet represented by the mobile Long Term Evolution (LTE)-technology of that time. Therefore, one might assume that technology has come a long way, constantly evolving, and improving, whereas the mindsets of certain foreign language teachers have not. At the same time, technology keeps on developing, and especially in the mid-2020s increasingly adaptive AI-powered technologies have appeared. Artificial intelligence is the latest development in ELT which will definitely shape and modify the idea of how foreign language teaching and learning should be designed within the next years and decades. The following sections will try to explore and explain AI’s transformative impact. 2. What is digital transformation anyway? Digital transformation as the process of digitisation is a rather confusing term because of its different meanings ranging from a somewhat technocratic term which mainly focuses on technology per se, to how digital technologies work in the light of a concept that implies a certain co-creative, collaborative mindset of stakeholders within their institutions, up to a complete reset of societal structures using digital technologies in an agile way (cf. Margaryan, Littlejohn, and Vojt 2011; Verina and Titko 2019) . Experts agree that the term is generic and needs to be specified for ELT. Semantically speaking, transformation implies the process of entirely changing a phenomenon. In terms of ELT, however, it can be seen that from the times of Computer-Assisted-Language-Learning (CALL) in the 1990s, the era’s cutting-edge technologies did not fully transform the process of teaching and learning a language but rather enriched or adapted these processes (cf. Schmidt and Strasser 2018) with positive effects on language learning (cf. Kettemann 1995; Meurers 2020). Digital or blended (i.e. analogue and dig- 1 http: / / www.padlet.com; an interactive brainstorming application Thomas Strasser 124 ital combined) language learning often consists of project-based, collaborative, constructivist and game-based or role-play-based scenarios (e.g. using mind mapping applications like Padlet or TaskCards) (cf. Raffone 2022), which support the work skills of the 21 st century (cf. van Laar et al. 2017). The pedagogical and conceptual similarities to the communicative language teaching approach (a methodological gold standard in ELT, so to speak) are evident here. Therefore, the impact of digital technologies on ELT should be considered as additive, rather than transformative. 3. What is the impact of digital technologies on ELT? As mentioned above, digital technologies have not fully transformed the teaching of English, but they have had a substantial impact on how EFL lessons are planned. There are various approaches to elaborate on the impact of digital tools on ELT. One can discuss such impact from technical or pedagogical perspectives, such as learning theories or the multiliteracies movement (Da Lio, 2020; Keller, 2016). In this paper, I shall provide an overview of how digital tools, so-called Edu-Apps, have impacted the four central components of ELT (cf. Martín 2015: 22; Cunningham, Rashid, and Le 2019: 43) in a blended language learning world: To start with reading, there are plenty of applications that support EFL learners with their reading skills. Most of the websites have so-called immersive readers, a technology that scans the digital text and reads it aloud in the chosen language. Furthermore, these readers can label various word forms and syllables, helping the learner to better understand a certain text. To make the reading experience even more contextualized, multimodal and visualized, immersive readers suggest images to the corresponding lexical chunk and can also translate the text into a chosen language. This multichannel digital approach can be considered a useful and versatile knowledge broker in addition to classic analogue reading. Wakelet 2 or Quizlet 3 are tools that use this technology. 2 http: / / www.wakelet.com; an interactive brainstorming and curation tool. 3 http: / / www.quizlet.com; an interactive flashcard tool. ELT in the digital age 125 Figure 1: Immersive reader Wakelet. For listening, particularly from a communicative language teaching viewpoint, using authentic materials in the EFL classroom has certain potentials and advantages, especially for intermediate to advanced learners. Digital technologies nowadays provide almost infinite access to authentic videos and audio if we think of the streaming culture of Netflix and YouTube, etc. However, this myriad of information and data needs to be methodologically curated. Tools like YouGlish 4 help learners contextualize language. The users type in a word they want to learn (either what the word means or how it is pronounced) and then the system suggests YouTube videos that include this word or phrase. The learner can then listen to the videos and learn the word in context along with a transcript. Furthermore, the user can click ‘next’ to see another video containing the searched word. Additionally, the transcript consists of a built-in dictionary and thesaurus, including pronunciation practice. Here, the learner has ubiquitous and multichannel access to a huge lexical database. Speaking skills are usually given the least practice time in the EFL classroom, often because of the high number of learners in a class. Here, the impact of digital technologies is quite noticeable. AI-powered tools (cf. chapter 4) like Otter.ai 5 automatically transcribes the learner’s speech in real time even if the learner struggles with pronunciation and accent. 4 http: / / www.youglish.com; An interactive pronunciation tool. 5 http: / / www.otter.ai; an AI-powered transcription tool. Thomas Strasser 126 Figure 2: Words in context with YouGlish. The system is designed for live-speech, which means that all the conversational gap-fillers like pauses or “erms” are identified and omitted to make the text more fluent and readable. Even if the speakers autocorrect themselves during the speech, the tool identifies that and goes for the “correct” version. Furthermore, this tool allows collaboration which means that after speaking, learners and teachers can annotate, provide feedback or highlight certain passages. Here, digital technologies offer a useful way to support learners with their presentations. It immediately provides insights into the context and lexico-grammatical features of their spoken texts (with the help of a built-in spell and grammar checker). It can also be a beneficial way for learners to speak aloud alone, as it offers a less intimidating environment to practice speaking. When it comes to supporting the writing process in a creative way, cartoon or book-creator tools are a multimodal catalyst (Bateman & Schmidt- Borcherding, 2018; Da Lio, 2020). The learner can use relevant prompts for certain text types and contextualize them in a visually creative storytelling mode. By using storytelling apps like Makebeliefscomix 6 or Book 6 http: / / www.makebeliefcomix.com; an interactive cartoon application . ELT in the digital age 127 Creator, 7 they can also practice textual coherence mechanisms in an entertaining, yet methodologically versatile way. Figure 3: Creating interactive books with book creator. All these tools provide useful means to hone specific language skills, and they can also be applied to render contents in line with curricular topics. Therefore, they can be considered as Educational Applications, so-called Edu-Apps. 4. Educational apps reloaded: When AI comes into play Educational Applications have been discussed controversially since the 2000s. These digital tools, mainly used for educational purposes, have evolved sophisticatedly. In the field of ELT, there are mind mapping and brainstorming tools that support the collaborative text production process, or co-creative graphic applications that let learners and teachers collaboratively design an infographic about classroom rules or climate change, for example. Furthermore, an increasing selection of simple and intuitive video tools can help learners produce interactive vlogs or presentations. 8 Experts agree that such digital tools should not only tackle the didactic domain which consists of the four skills, but also the pedagogic domain, which focuses on learning modes and group-dynamics, e.g. how students can selfdirect and language educators can foster learning processes (cf. Schmidt 7 http: / / www.bookcreator.com; create interactive books. 8 For a detailed list of educational applications, see Strasser (2023). Thomas Strasser 128 and Strasser 2018; Strasser 2023). Moreover, the learners should collaborate and communicate with their peers by using certain tools in the classroom. They should be given reflective feedback by their teachers so that they can modify their created digital artefacts and easily share them afterwards. The nature of digital tools is a transient one, which means certain applications come and go; however, the basic didactic and pedagogic principles (collaboration and co-creation) remain. Educational applications have been used in the EFL classroom for decades mainly for remedial drill scenarios (vocabulary quizzes, etc.), but there needs to be more to create intelligent adaptive environments that address learner heterogeneity. This is where Artificial Intelligence comes into play. In the last couple of years, an increasing number of AI-powered tools have appeared on the ELT market. A thorough review of the multifaceted implications of Artificial Intelligence would go beyond the scope of this paper. Therefore, I shall concentrate on the implications of AI in ELT contexts. As regards ELT, AI-powered tools generally share certain features. There are a number of different levels of complexity in AI, ranging from so-called “narrow AI” (less complex algorithms that are good at solving one particular goal, e.g. AI-based vocabulary learning with Quizlet or automatic explainer-video generation with Simpleshow 9 ) to algorithms reading the processed data on learners’ interactions with technology to therefore create adaptive, intelligent tutoring systems (Pandarova et al. 2019: 350). The digital age and AI provide interesting opportunities for personalized learning, and it seems like these new technologies will continue to change the way we learn and teach, now and in the future (cf. Dodigovic 2005; Berendt, Littlejohn, and Blakemore 2020). Prominent examples for foreign language learning and teaching are AI-powered translation services like DeepL 10 or writing companions like Grammarly 11 that automatically correct written texts by providing suggestions for tone, register, syntax, grammar, and spelling. Since these technologies and learning concepts have evolved within the ELT community, also because of Artificial Intelligence, there is evident need to adapt the original Edu-App approach as it does not consider such emerging technologies like AI that have the potential to replace certain typical teacher tasks (cf. Heckmann and Strasser 2012; Eisenmann 2022: 210). More and more algorithms and sophisticated applications can now create linguistic artefacts automatically. Therefore, learners and teachers need to consider creative engineering, a concept that focuses on AI’s creative power to automatically generate images, texts, audio, and video based on certain algorithms. 9 https: / / simpleshow.com; AI-powered explainer video generator. 10 http: / / www.deepl.com; AI-powered translation website. 11 http: / / www.grammarly.com; AI-powered style checker. ELT in the digital age 129 5. Visual facilitation with AI: The next big thing in ELT? Visual facilitation is a proven strategy for language educators. For example, the use of images for language-based explanation helps learners negotiate the meanings of words more easily (cf. Orav 2021). Visual facilitation provides imagery which is organized to help learners better understand new material and collaborate with others during learning experiences. It is important to remember that visual facilitation is not just about picture drawing or using PowerPoint. As mentioned above, AI-powered tools can now produce fully coherent language artefacts. Since 2017, a stream of AI-powered language learning facilitation has been applied: visual facilitation tools like Simpleshow or Midjourney 12 . Within the language-learning context, such visual facilitation tools work like this: the learner types a sentence into the app that somehow reflects the curricular needs of his/ her learning process (e.g. similar phrases from the course book) and the AI-powered tools automatically synthesize an image that represents the input sentence. This textual-inputvisual-output approach is called prompt engineering and refers to the autoregressive language model GPT-3 (Da Lio, 2020; Oppenlaender 2022), a model that “requires context to produce relevant text as output” (Oppenlaender 2022: 5). As for text-to-image generation, Oppenlaender (ibid.: 7) developed a general taxonomy of so-called prompt modifiers, which can be applied to the field of ELT as follows: 1. Subject terms: If you want to generate an image, you type in the subject you wish to describe. For example, “a girl playing football on a sunny autumn afternoon” or “a white cat sitting on a roof in New York”. Here the subject is the lexical or curricular leitmotif, the language artefact that is at the centre of the meaning, so to say. 2. Style modifiers (quality boosters): Style modifiers can be added to a prompt to create images in a certain style. For example, the modifier in “Pop Art style” will always generate digital images resembling the work of Andy Warhol et al. Other examples of this type of modifier include the use of adjectives expressing a certain situation or setting (e.g. colourful garden, enthusiastic crowd). 3. Repetition: Entering a certain word a couple of times in a semantically appropriate way will help the system to produce more reliable results based on the “neural network’s latent space that [is] associated with the subject terms” (ibid.). Furthermore, it can be seen that “the prompt of, “a very very very very very beautiful landscape” will, for instance, produce a better image than a prompt without repetitions. Technically, this is due to likelihood-maximizing language models becoming stuck in positive feedback loops from repeated phrases” (ibid.). 12 http: / / www.midjourney.com; AI-powered visualizer tool. Thomas Strasser 130 4. Magic terms: By entering abstract lexical utterances, the AI-powered application can produce “surprising results” (ibid: 7). In language learning, the input of such magic words may introduce “an element of unpredictability and surprise to the resulting images” (ibid: 7), which to some extent may boost the creative and semantic versatility of language production. Magic terms can refer to subjects that are only distantly related to the main subject of the prompt, or they can refer to non-visual qualities, such as the sense of touch (somatosensory), sense of hearing (auditory), sense of smell (olfactory), and sense of taste (gustatory) (e.g., “feed the soul”) (ibid). The next section provides an example of how this taxonomy can be implemented in the classroom. 6. Using AI-powered visual facilitation tools in the EFLclassroom As discussed above, such visual facilitation tools help learners contextualize language using a highly creative visual framework of reference. One scenario of how such technologies can be used in the EFL classroom would be the following: students are required to write an adventure story. In the subsequent lessons, they learn and practice topic-related prompts and lexical items (time forms, conjunctions, etc.). Before they write their story as an assignment, the teacher discusses the stylistic features of an adventure story, considering the taxonomy discussed before. Students have to think about the subject term (e.g. the protagonist, antagonist, etc.), the quality boosters/ style modifiers (e.g. the description of the setting, use of adjectives to describe a climax) and also magic words (e.g. students have to think of adjectives emotionally representing their story or words that create a visual twist). By using repetition words, students think of certain scenes they want to stylistically emphasize. After the students draft their first version of their adventure stories, they type selected phrases they think can be best represented visually into the apps. By doing so, students develop a skill of writing in an expressive language. In addition, Midjourney provides a visual representation of the students’ typed phrases, therefore, providing a picture dictionary which helps them with the vocabulary learning process. If students are not satisfied with certain AI-generated images (e.g. the text does not fully meet the demands of the author, the protagonist is not represented in a way the students like, the climax scenes need to be represented in a more visually detailed way, etc.), they can adapt and re-render the images so that they will meet their creative and linguistic expectations. This retains the students’ agency over their own creative work. In terms of the AI’s creative ELT in the digital age 131 potential, the visualizer application draws its data from an extended linguistic corpus combined with almost infinite visual layers so that the chances are quite high that the produced image represents the lexical item correctly. This incentivizes the students to construct a highly creative media product with the texts they produce. In the following lesson, students can read their adventure story to their peers and the teacher. After listening to the story, the teacher can ask everyone what pictures they have in mind, focusing on aspects like setting, characters, key scenes, and expressions to recapitulate the actual curricular intention (writing an adventure story using proper words, phrases, grammar, and styles). Figure 4: AI-powered prompt engineering applications visualizing an adventure story. 7. Educational apps reloaded: The domain of creative engineering As mentioned earlier, Educational Applications cover essential skills and literacies for the modern language learning classroom in the digital age. However, due to the highly automatized power of AI-tools, the six domains (creation, collaboration, communication, reflection, multiplication, modification) need to be extended with the domain of creative engineering - a domain that discusses the role of the learner and teacher and how they will Thomas Strasser 132 pedagogically and methodologically deal with the fact that more and more AI tools create language artefacts almost autonomously. In the case of AI-powered visualizer tools, we can see that these devices do not fully replace the creative skills of the learners. Instead, they act to some extent as a creative catalyst or scaffolding device. In our scenario mentioned above, the teacher has to apply the proper methodological and curricular skills to design a coherent lesson scenario that makes use of the tool. Without the appropriate methodological implementation, these tools would not be of any sustainable use for the learners. Based on the lesson plan framework and the teacher’s curricular input, the learners can then use an AI-visualizer application like Midjourney as a tool that visually and semantically supports them with their required language outcome (in this case writing an adventure story). However, the teachers and learners still need to decide to what extent AI-tools should support their teaching and learning processes. The domain of creative engineering is thus not only a creative catalyst supporting language production, but it also highlights that AI-powered technologies have enormous potential in supporting learners and teachers in their way of experiencing and teaching a language in the digital age. The domain of creative engineering does not focus on a “computer does the human work” narrative but rather captures the fact that the computer can be a creative partner in co-constructing a text and provide stimuli that aid the language acquisition process. AI-powered visual tools have a big community now, which means that learnerproduced images (based on provided input written by themselves) will be shared, discussed and remixed in so-called Jam-Sessions (Oppenlaender 2022: 7). AI tools are not likely to replace EFL-teachers, at least in distinct teaching scenarios. Since AI-powered visualizer tools generate images from various corpora, layers and datasets, chances are high that the production of a visual artefact will end in a machine bias, which means that the algorithm produces images that are highly stereotypical, culturally inappropriate or politically incorrect (Strasser 2021: 99). Therefore, the persona of a competent and interculturally-aware language teacher is necessary in order to discuss potentially biased images with their learners. This enriches language learning as it goes beyond vocabulary and grammar learning and creates a place for controversial discourse which focusses on the fluency and cultural competence of the learners. AI-powered tools have undergone drastic technological and methodological development. However, the role of the teacher in language teaching contexts is still pivotal, particularly with regard to his or her role as an interculturally-aware language professional who considers language as a dynamic vehicle that cannot be plainly learned or acquired with linear algorithms. Yet, AI-powered tools have an assistive function as they help teachers with certain forms of teaching such as linear remedial drills. AItools do all the automated tasks like grading cloze exercises or even analysing and improving texts written by students. Of course, there are many ELT in the digital age 133 AI-powered tools on the market that generate fully coherent, grammatically and lexically sound texts. This is why teachers should be inspired to go beyond and look for new text genres, exercises and assessment formats when applying creative engineering in order to allow for a sustainable interaction between technology, the learners and the teaching context. It is about time that the ELT world understands that we should use “AI as a tool, and consciously deal with data” (Olari & Romeike 2021: 1). When I talk about AI in a language learning context, I predominantly refer to narrow AIs, i.e. an AI “that is intelligent within a particular domain” (Long and Magerko 2020: 5) such as the Midjourney bot as a visualizer tool. Following Long and Magerko (ibid.), as regards these literacies, the following competencies are of relevance for the creative engineering domain: Competency Description Relevance for the EFLclassroom Explainability Use graphic visalizations, simulations of “agent decision-making processes” (Long and Magerko 2020: 5) to support learning. Use AI-visualizer tools (like Midjourney) to support lexical contextualizations and creative story writing. Detecting/ analysing the human role in AI Humans should interact with AIs and fine-tune the outcome. Language learners and teachers use AI features to produce digital artefacts that meet their learning/ teaching needs (render function AIvisualizer tools) Critically interpreting data/ Critical thinking The production of digital artefacts produced by AI “cannot be taken at facevalue and requires interpretation” (ibid.) Language learners and teachers should critically reflect on AI results using their intercultural skills. Table 1: AI-literacies. A selection for the EFL-classroom. The skill of interdependency within the domain of creative engineering should be added here. This term means that teachers create task and assessment formats that cannot be plainly copied or replaced by AI but are a methodologically-interdependent addendum to the textual products generated by AI. A practical example would be that learners use Grammarly and Copymatic to prepare a complaint as a text type (as a base text by omitting the most evident linguistic infelicities). Then the teacher designs a role play where students have to remix and rephrase the machine’s produced phrases in a conversational context showing turn-taking and ad hoc negotiation skills. In this case, the written complaint automatically generated by the Thomas Strasser 134 AI just serves as discursive base (use of prompts, linkers and discursive fragments). This base needs to be adapted and coherently applied to the new activity (role play) in ways that cannot be anticipated by the machine. In order to do that, the language learner has to synchronously use and remix the AI-generated language chunks or texts. 8. A long way to go? It is not a matter of whether AI-powered tools will be an omnipresent part of the ELT-world, but rather a question of how teachers and learners deal with it methodologically and ethically. The competencies discussed above offer at least a generic starting point in which new literacies within the domain of creative engineering could be developed and trained. What started decades ago with the controversial discussion on whether spell and grammar checks in text-processing software should be allowed in the EFL-classroom, which then elevated to the dispute in the mid-2010s on whether to use translation (DeepL/ DeepL Write) and proofreading tools (like Grammarly) in order to write curriculum-based text types, like a blog entry, CV or a letter to the editor during an exam or revision, has now found its latest controversial peak, namely whether teachers will be replaced by AI-powered tools like Jasper.ai or copymatic.ai - AI-technologies that generate flawless texts in a foreign language. In the spirit of the communicative language teaching approach of fluency before accuracy, a range of key elements are crucial such as conversational strategies like turn-taking, intercultural negotiation, and contextualization of humour and pragmatic skills like using language in a proper context. We can consider AI-powered tools as a digital catalyst for personalized learning that requires the interculturallycompetent language teacher, who is not a linear knowledge broker of grammar rules and language prompts, to develop new skills in times of digital technologies. When a learner uses AI technology as a creative scaffolding tool that provides solid language fragments which he or she can process, remix and adapt to the interdependent task designed by the teacher, then this does not translate into a simple copy and paste procedure but into sophisticated use of assistive technologies that elevate the language-learning process. In the same way Norman Cook, aka Fatboy Slim, used his remixing skills to continuously develop or produce his traditional playlist by adding some new and emerging beats, teachers have to understand that AI-powered tools can be their DJ equalizer to adapt their teaching to the needs of the 21 st century ELT classroom. 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