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The degree to which generative artificial intelligence (GenAI) has rapidly infiltrated education is unparalleled. Language education has been particularly impacted because GenAI tools process and generate the learning objective of that education, i.e., human language. Language teacher education programs have been faced with addressing GenAI since the public release of ChatGPT in November 2022, and we anticipate that many recent and future graduates will have had some formal education that includes it. Moorhouse & Kohnke (2024) provide initial insights from a group of language teacher educators on this topic. But what about those who have already completed their formal education and are in the language teaching workforce, the millions of individuals across the world actively teaching languages at all levels?
UNESCO (2024) has recognized the immediate need for AI competency across the board in education and why it should be addressed.
Al can pose significant risks to students, the teaching community, education systems and society at large…In education, Al can reduce teaching and learning processes to calculations and automated tasks in ways that devalue the role and influence of teachers and weaken their relationships with learners. It can narrow education to only that which Al can process, model and deliver. Finally, it can also exacerbate the worldwide shortage of qualified teachers through disproportionate spending on technology at the expense of investment in human capacity development (p. 13).
So, given this litany of dangers, what do we think language teachers need to know and be able to do to achieve a functional level of expertise so that they can safely leverage the affordances of GenAI to improve rather than degrade language learning processes and outcomes? How can language teachers and language programs support them in accomplishing this goal?
In this position paper, we first address these questions by focusing on the importance of understanding the fundamentals of AI and its subset GenAI, as recognized in a number of AI competency and literacy frameworks. For example, the UNESCO (2024) AI Competency Framework for Teachers states that at the lowest of their three levels of AI competency, “Teachers are expected to acquire basic conceptual knowledge on AI, including: the definition of AI, basic knowledge of how AI models are trained, and associated knowledge on data and algorithms” (p. 30). The Educause (2024) Durable AI Literacy Framework, targeted at tertiary institutions, goes further: “Faculty must grasp the core principles of AI, including machine learning, natural language processing, and neural networks. This foundational knowledge is crucial for understanding how AI operates and what its potential applications are in various academic disciplines.” Other frameworks we discuss below, such as those from the International Society for Technology in Education (ISTE) and Paradox Learning, echo this need for teacher understanding.
In the first part of this paper, we sketch the computational (mathematical) underpinnings and approaches of GenAI, the basics of human conversation with a chatbot (a machine), and the context of intelligent tutoring systems and machine translation. We then review the preceding four AI competency/literacy frameworks and draw from those and other sources 10 areas for teachers to first familiarize themselves with and then engage in to become competent with GenAI as it exists today. Finally, we offer guidance for what we refer to as GenAI sustained integrated professional development (GenAI-SIPD), providing principles and strategies to help language teachers thrive in a rapidly evolving and unpredictable future.