Open generative AI changes a lot, but not everything

Abstract

Technology is again a timely topic at this moment in our profession. The appearance of generative artificial intelligence (AI) has brought an unprecedented level of public awareness and appreciation of language—both English and other world languages. The November 2022 appearance of generative AI embodied in ChatGPT ignited a combination of curiosity, imagination, and trepidation among the public. The Economist named “ChatGPT” the word of the year for 2023, with the explanation that “nothing can stop technology from dominating this year's words” (The Johnson Column, 2023, para. 7). ChatGPT captivated the attention of the public with its language performance: “The breakthrough in particular of large language models (LLMs) has been stunning. They produce prose so human-like that they have ignited a debate about whether LLMs are actually thinking (and whether students will ever do homework without them again)” (The Johnson Column, 2023, para. 7). Similarly, The New Yorker’s Sue Halpern summed up 2023 with a column entitled “The year A.I. ate the internet: Call 2023 the year many of us learned to communicate, create, cheat, and collaborate with robots” (Halpern, 2023). These are but two of the many examples of how the public media in the United States has weighed in on the publicly accessible generative AI unleashed over the previous months. need to articulate and communicate the value of language study in a social context, identify what technology offers that is positive for language education, rethink how we organize our teaching in light of technology's affordances, and be clear about what technology cannot do. (Kern, 2024, this issue, p. XX) He opens the conversation with a discussion of the new technological capabilities offered by generative AI—specifically, machine translation and ChatGPT—and with concrete suggestions for prompting students’ “critical engagement with technology guided by human teachers” (p. XX). This inflection point suggests that it is time to grapple again with our understanding of the significance of language, language teaching, and our roles as language professionals. After all, language is at center stage. Google has been responding admirably to our queries for information for years with lists of sources, links, and more precise questions for us to explore. We already had access to copious information. ChatGPT captivated the world with its language: the grammatically idiomatic, responsively contingent turn taking in natural written conversation. It soothed our sensibilities that have been irritated for years by repetitive, irrelevant responses from robotic chatbots positioned between us and the personalized information we sought from websites built to mitigate the need for human communication. This is the time for us as language professionals to increase our own critical engagement with language and language technologies in a world never so fascinated by language. As a founding faculty member of the doctoral program in Applied Linguistics and Technology at Iowa State University, I am accustomed to watching the evolution of technology as it changes the ecology of language teaching, learning, and assessment. Over the past decades, technology has delivered a continuing stream of important issues for us to study and countless avenues for expanding learners’ access to language and culture learning while developing agency as language users. The constantly evolving technologies of the past should have prepared us well for the entrance of ChatGPT, but it is necessary to stop and consider what we have learned from the past. Taking Kern's (2024, this issue) prompt to consider our preparation for this moment, I recall three episodes where I find clues for understanding the opportunity presented by the technical accomplishment manifest as generative AI. The first episode is the 50-plus years of navigating technology in language learning through research and practice. The Modern Language Journal has documented a fitting sample of this history, which is more fully displayed in journals focusing on technology and language learning, most notably CALICO Journal, CALL Journal, Language Learning & Technology, and ReCALL Journal. These journals, as well as many books, have reported the fraught quest for AI-based tools and learning activities in language teaching. The vision of the 1970s was that tutorial programs would recognize students’ sentence-level errors and provide error-specific feedback while building “student models” from the cumulative data gathered from each student's performance (Sleeman & Brown, 1982). In the 1980s, some language teachers imagined computers interacting with learners in written playful or touristic conversations in which the computer would recognize the students’ written messages and respond accordingly, creating a conversation circumscribed by the limits of the program (Underwood, 1984). Such prototype “microworlds” were conceptualized for language learning and, in some cases, primitive ones were developed. A third strand of research sought to develop pedagogical grammar checkers to provide grammatical feedback on learners’ writing (Cotos, 2014; Heift & Schulze, 2007). A fourth area where elements of AI have played a role is in virtual gaming and virtual reality (Peterson, 2023). The visions, descriptions of artifacts, and research are documented in professional journals and books, but their contribution seems almost negligible in the face of the Internet-eating, polyvocal superstar of 2023 and beyond. The interactive grammatical discourse of ChatGPT dwarfs the linguistic production limited by yesterday's AI in a tutorial, microworld, grammar checker, or virtual reality environment. The limited accuracy of the language recognition and machine translation of the past did not earn widespread trust of teachers and learners, leaving no worries about the technology writing papers for students. The anticipated results of the Athena project at the Massachusetts Institute of Technology, intended to create pedagogical tools for learners of four European languages using natural language processing (NLP), instead discovered that, in Felshin's (1995) words, “NLP is hard” (p. 271). These strands of development of AI tools never made international headlines. Few made it into classrooms. Chatbots offering conversation on limited topics were not sufficiently interesting or accurate to be widely accepted; grammar checkers left errors unchecked or unresolved. They did not demand language teachers to fundamentally change how they teach. But they did contribute to the professional zeitgeist animating the study of the computer-assisted language learning of the time. This era left me with relevant takeaways including that (a) descriptive research is essential to document the contingencies in computer-mediated interactions, (b) students need guidance to learn effective strategies for using technology for language learning despite their ascribed identities as digital natives, (c) research can show benefits of technology-mediated pedagogies when they are carefully designed, appropriate to the needs of learners, engaged with over a sufficient span of time, and investigated with an appropriate methodology, and (d) perseverance is required to appreciate the unique contributions of each generation of technologies in language learning. During this period, the NLP capabilities in language learning tools improved incrementally, but the appearance of ChatGPT in November 2022 marked the beginning of a new era. A second episode that today's inflection point brings to mind was connected to the events of 9/11 in 2001. The Modern Language Association (MLA) Ad Hoc Committee on Foreign Languages referred to a “sense of crisis” at this time about the nation's “language deficit.” In the 2007 MLA report, Foreign Languages and Higher Education: New Structures for a Changed World, the ad hoc committee described the crisis in much different terms than those Kern (2024, this issue) used to characterize the circumstances giving rise to today's inflection point. The authors wrote, “In the context of globalization and in the post-9/11 environment, the usefulness of studying languages other than English is no longer contested” (Modern Language Association of America, 2007, para. 4). The challenge was seen as the need to stitch together the instrumental with the humanistic goals of world language teaching to build students’ “translingual and transcultural competence” (Modern Language Association of America, 2007, para. 4). The proposed solution was for language courses to “incorporate cultural inquiry at all levels” (Modern Language Association of America, 2007, para. 11) and the study of more subject areas in advanced courses. Cultural inquiry in language study would require that courses “situate language study in cultural, historical, geographic, and cross-cultural frames within the context of humanistic learning” (Modern Language Association of America, 2007, para. 11), which helps students to grasp their own subjectivity. Any movement toward curricula and materials targeting these goals has undoubtedly been useful for addressing today's challenges, which Kern (2024, this issue) sees as an opportunity for promoting critical engagement with technology, language, content, and culture. However, barriers to promoting cultural inquiry informed by cultural, historical, geographic, and cross-cultural frames may remain and perhaps have grown even stronger today. Kramsch (2012) identified a range of challenges including the following: a division of labor exists in language departments, where faculty teach culture only in the upper levels; some faculty do not support the goal of helping students understand their American identity; some language teachers are unprepared to discuss historical events; students’ lack of knowledge of history in general provides an insufficient basis for learning cultural histories; and textbooks do not support the goals. These issues remain significant for the field even as Kern sees the need to use AI as a means for supporting learners’ cultural inquiry at all levels. Despite the scholarship in world language teaching that has been supportive of efforts to promote critical cultural inquiry across all levels of language instruction, Kern (2024, this issue) points to some countervailing indicators. First, folk wisdom about language promotes claims that language instruction can be adequately obtained by technology-delivered drills and tutorials and that language performance does not require language knowledge in view of intelligent language generation and translation tools. Second, enrollments in language courses other than Korean and Hawaiian are decreasing—a trend highlighted by the closure of one university language department in the United States. Third, the view of some in higher education is that language teaching is too basic for research universities. The decreasing enrollments in world language courses may be part of the larger observed downward trends across humanities courses, possibly unrelated to generative AI. What Kern (2024, this issue) refers to as folk wisdom about language learning might be seen as a consequential component of “folk linguistics,” the knowledge and beliefs about language held by people who are not linguists. Folk linguistic beliefs affect what people think and say about language as well as their actions pertaining to language (Niedzielski & Preston, 2000). Folk linguistic beliefs about language learning are presumably shaped by students’ experience in language classes, but the large majority of language study in US higher education is in first- and second-year classes. These language classes provide the opportunity to demonstrate to students the breadth and depth of knowledge and understanding conveyed through language study, but in view of the barriers to teaching cultural inquiry noted above, it is not clear how well the opportunity is exploited. This is the issue raised in the 2007 MLA report. It is the challenge explored in the 2012 special issue of L2 Journal on history and memory in foreign language study (Kramsch, 2012). It is the goal targeted by many scholars in applied linguistics over the past 20 years. This challenge remains a high-priority topic for understanding how generative AI can improve language learners’ experience with critical cultural inquiry beginning in their first language course. The near future will undoubtedly reveal fascinating new ideas for increasing beginning-level learners’ engagement in critical cultural inquiry while conveying a more sophisticated understanding of language learning than what is currently expressed by current folk wisdom. In other words, the current inflection point brings tools that could help to address the previous crisis. Generative AI supplies beginning language students with an unprecedented degree of power over their target language by supplying them with tools for multimodal translanguaging through translation, text to speech synthesis, synchronous interaction, composition of text, and multimodal forms of cultural expression. These tools can unlock barriers to language understanding and interaction that frustrate beginners. They can empower learners with language for expressing their own meaning and creativity. They can produce artifacts for critical analysis that requires gaining knowledge of historical frames of reference. In other words, AI tools provide concrete, accessible devices that can contribute to solutions to the crisis, but they may also remind us that the crisis was and is bigger than the most powerful AI tools. The crisis identified in 2007 was created from institutional structures, knowledge deficits, and pedagogical beliefs about the methods and goals of language instruction. Recalling the inflection point marked by recognition of the need for critical cultural inquiry in the first years of language study, I expect that the opportunities made feasible by generative AI will contribute to defining a 2020s version of the aughts crisis in world language teaching. The third episode related to today's inflection point took place as a hybrid event originating at Iowa State University in October 2023. Our annual Technology for Second Language Learning Conference (TSLL) provided a glimpse of the types of questions and issues that may be on the agenda for applied linguistics going forward. We invited abstracts describing research investigating the potentials, uses, and implications of AI technologies for language teaching, learning, assessment, and research. The conference theme was “Advancing Technologies—Expanding Research,” signifying the multiple strands of research and practice in applied linguistics affected by current and future AI technologies. Applied linguists throughout the world responded with a range of fascinating studies that offered a glimpse into potential directions. Distinct categories were difficult to discern in the array of research directions represented by the presentations at the conference, but they generally fell into four groups. One group followed a tradition of descriptive research on technology for language learning with investigations of learners’ generative AI use primarily, but not only, for their writing. A second group continued the strand of research on technology for language assessment with studies of automated evaluation of learners’ writing and speaking and on language assessment literacy, but also included investigations of AI-generated language test items and test preparation. A third group explored a range of generative AI capabilities for applied linguistics, including automated assessment of accuracy for L2 writing research, detecting pragmatic competence for L2 Chinese teaching, promoting learner–computer interaction with question–answer technologies, and a corpus-based study of the discourse styles of ChatGPT. A fourth group investigated implications of generative AI for language teacher education to begin to understand the new knowledge that language teachers need to develop and the methods for teacher education. The conference program, which includes the abstracts and videos of some keynotes, is at https://apling.engl.iastate.edu/conferences/technology-for-second-language-learning-conference/tsll-2023/ the experience and that will the study of AI in language learning in the presented a vision of a new of learning with a glimpse into the in writing instruction that will be part of the new and provided examples of current research on generative AI for and each described how generative AI would be in for assessment and learning within an pedagogical that AI should be the of critical in view of the ecology in which generative AI is and highlighted the significance of this moment of public to the view that language is The by these in this new generation of technology for language learning. 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We will need to these with concrete strategies for using AI as a to help learn about language and culture. previous of technology, generative AI will require us to learn from descriptive research investigating how it can contribute to language learning goals and to provide students with guidance about how to use it to build their own language competence without it their opportunity to learn from their language I have that we will be to together and with our across to the opportunities and presented by generative AI. Our has the to do so through years of professional engagement with technology for language learning as well as our recognition of the need for teaching critical cultural awareness across all levels of language access provided by the Iowa State University

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