Has artificial intelligence rendered language teaching obsolete?

An applied linguist with a specialization in computer-assisted language learning (CALL), I was keen to read Richard Kern's (2024, this issue) Perspectives column, "Twenty-first century technologies and language education: Charting a path forward." In this article, Kern identifies several possible directions language learning and teaching might take following the COVID-19 pandemic and recent technological advances. With an interest in how artificial intelligence (AI), specifically speech technologies, might be harnessed to facilitate language learning and teaching dating back to the first wave of AI (Handley, 2006, 2009; Handley i.e., primary and secondary schooling) long in decline and more than 10 university language departments closing over the last 20 years (Kenny so-called pedagogical content knowledge). General pedagogical knowledge comprises knowledge of classroom management and organisation, as well general theories of learning and development and associated pedagogical principles. Pedagogical content knowledge is subject-specific knowledge of how easy or difficult the subject matter is to understand, including knowledge of common misconceptions and knowledge of how the subject matter can be represented to promote understanding (Shulman, 1986). In other words, pedagogical content knowledge comprises the knowledge required to plan the curriculum and individual lessons as well as select appropriate pedagogical techniques to engage learners in individual lessons. To these three dimensions, Baumert and Kunter (2013) claimed that the effectiveness of a teacher also depends on their organisational and counselling knowledge. Organisational knowledge comprises knowledge of the broader educational system as well as knowledge of the individual institution in which they are working. Counselling knowledge refers to the knowledge required to support students and their caregivers in making decisions about their education, including decisions related to careers, learning difficulties, and behavioural issues. As such, counselling knowledge is as much a skill in interpersonal communication as it is a body of knowledge. Further to this body of knowledge, the research that Baumert and Kunter (2013) drew on suggests that effective teachers have a particular personal disposition. They are confident in their ability to deploy their knowledge and skills, enthusiastic with an intrinsic interest in their subject and/or teaching, and they can cope with the demands of the role (i.e., self-regulate). Pedagogical content knowledge, as Shulman (1986, 1987) acknowledged, differs across subjects. Language teaching, however, is distinctive not only in terms of "the nature of the subject, the content of teaching and the teaching methodology" but also in terms of "teacher-learner relationships" (Borg, 2006, p. 3). Aside from the unique cultural dimension of the language curriculum, the core subject matter is the medium of instruction and the goal is to enable learners to communicate in that medium rather than to acquire knowledge of it, though equipping learners with explicit knowledge of the subject is one route to achieving that goal. As such, specific theories are required to account for second language acquisition (SLA; see, e.g., Mitchell et al., 2013, for an overview), and dedicated pedagogies have been developed (see, e.g., Larsen-Freeman Long, 1983) and language-related episodes (i.e., dialogue about language and language use; Swain Andrews, 2003). Moreover, the curriculum normally requires engagement in topics that are often somewhat personal in nature (Borg, 2006), and speaking in another language can be particularly anxiety provoking for some learners (Horwitz et al., 1996). The effective language teacher therefore requires not only a distinctive form of pedagogical knowledge, but also particularly strong interpersonal or counselling skills. To understand the extent to which AI might replace language teachers, it is also important to have a good understanding of what AI is. Currently, the first thing that probably comes to mind is ChatGPT. Released in November 2022, ChatGPT is a chatbot, a technology that enables a computer application to engage in conversations with end-users. It was recently made possible because of advances in natural language processing—specifically, large language models (LLMs)—and generative AI, technologies that can produce novel text and other outputs in response to user-generated prompts. a machine-based system that can, for a given set of human‑defined objectives, make predictions, recommendations or decisions influencing real or virtual environments. It uses machine and/or human-based inputs to perceive real and/or virtual environments; abstract such perceptions into models (in an automated manner e.g. with ML machine learning or manually); and use model inference to formulate options for information or action. AI systems are designed to operate with varying levels of autonomy. (OECD, 2019) In other words, AI refers to a range of technologies that enable computers to perceive, learn, abstract, and reason (Launchbury, 2017). This includes technologies such as face and voice identification, voice control, autocomplete, autofill, and smart reply that already proliferate in our daily lives (Devlin et al., 2023). AI is also far from new to language education. Grammar checkers and dictation software have long been widely available to language learners through productivity applications such as Microsoft Office (Figueredo each topic introduces some grammar and cultural concepts with very limited explanations but the lessons themselves focus mostly on introducing new vocabulary and drills. The exercises offered include translation, multiple-choice word recognition questions, and spelling. Incorrect answers are handled in two ways. Some users have a 'heart' system, in which a certain number of mistakes leads to losing one out of 5 hearts (…) When all 5 hearts are lost, the user cannot practise until they recover at least some hearts. On some devices, mistakes trigger more repetition and drills and a slightly lower amount of experience or achievement gained upon completing the lesson. (Shortt et al., 2023, pp. 520–521) Despite the apparent simplicity of this core lesson system, Duolingo is more than a first-generation rule-based intelligent tutoring system or intelligent computer-assisted language learning (ICALL; Yazdani, 1986). Some speculate that it may be supplementing examples supplied by its community of volunteers (Verweij, 2020) with automatic exercise generation (Burstein Settles Baumert Shortt et al., 2023). Some learners in Loewen et al.'s (2019) study, for example, reported that they found it hard to maintain their motivation due to the repetitive nature of the activities, which focus almost exclusively on receptive language skills. In other words, Duolingo is limited by its method of language teaching and the range of techniques it uses to deliver instruction (Shulman, 1986). Some learners in Marques-Schafer and da Silva Orlando's (2018) study reported wanting more explicit grammatical feedback. In other words, there are also limitations to the way in which Duolingo represents content to learners to facilitate learning (Shulman, 1986). Expanding the techniques and methods of language instruction beyond the behaviouristic drills and techniques associated with grammar translation seen in Duolingo and Memrise, other commercial language courses attempt to harness AI to provide automated feedback on writing and speaking and engage learners in simulated conversations. With an estimated 30 million daily users (grammarly.com), Grammarly is perhaps the most well-known example of an automated writing evaluation (AWE) system that can be used to provide learners opportunities for repeated writing practice and feedback (Koltovskaia, 2020; Warschauer e.g., Duolingo), feedback on speaking (e.g., Speechrater from ETS and Speechace), and interactive conversational practice (e.g., ELSA Speak and Speak Ngo et al., 2023). With respect to learners' experiences using these systems, it is notable that early work highlighted that the value of AI tutors equipped with automatic speech recognition might lie in the fact that they are not human and are untiring and nonjudgemental (Chiu et al., 2008). In other words, CAPT systems might complement good language teachers by enabling them to provide the intensive individualized practice required to develop speaking proficiency that they simply cannot offer themselves in their regular classes. Moreover, Computer-Assisted Pronunciation Training (CAPT) systems may be perceived to have a personal disposition (Baumert & Kunter, 2013) that is particularly well suited to counselling learners to overcome the anxiety often experienced with speaking in a foreign language (Horwitz et al., 1986). In conclusion, AI-enabled language learning and teaching is not new. Examples of AI-powered language tutors have been available on the market since the late 1990s. Despite recent advances in AI and the speech and language technologies fundamental to enabling the development of automated language tutors, the capacities of these automated tutors remain limited compared to those of an expert language tutor. Current intelligent language tutors are not only lacking in the affective dimension and their ability to engage and counsel learners but also in the pedagogical dimension. The range of pedagogical techniques that they can employ is limited, as is their ability to represent language in a way that supports learners' understanding. Current AI language tutors at best complement expert human language tutors by offering unlimited repetitive practice and feedback on linguistic form that makes time for expert tutors to engage with knowledge and meaning, provide more creative opportunities to use language, and engage with the whole learner at a human level.