The CATESOL Journal

Artificial Intelligence technologies are becoming ubiquitous, transforming the workforce by altering or creating jobs and influencing decisions that affect minority communities. The necessity of AI literacy, comprising knowledge and skills for critical interaction with AI, is increasingly important. Multilingual learners, engaging with both every day and domain-specific vocabulary and syntax, must acquire AI literacy, necessitating tailored pedagogical practices. This article outlines three strategies implemented for multilingual middle school students during a summer camp in Southern California. We argue for a mutually beneficial relationship between acquiring second language proficiency and AI literacy. Supporting these processes, we present three pedagogical strategies: (1) storytelling to model AI decision-making, (2) role-playing as an AI to demonstrate programmability and learning from data, and (3) programming text-to-speech-to-text AI to illustrate sensor functionality and action-reaction concepts. Additionally, we discuss their alignment with AI competencies. These strategies potentially foster linguistic scaffolding and translanguaging, aiding multilingual learners in acquiring new literacies.

Over the last decade, there has been an explosion of national interest in computer science (CS) education. In response to this, several organizations and initiatives have emerged in recent years to expand the CS pipeline. However, within these broad and laudable efforts, one important area has been largely overlooked—the instruction of CS to multilingual students, including the large and growing number of students designated as English learners in K-12 schools. These are one of the most underserved and understudied groups in CS education. In this article, we draw on existing research, as well as our own and others’ theoretical and empirical work to date, to put forth both a framework and curriculum for teaching CS to multilingual students.

Engaging second language (L2) students in multimodal academic writing that leverages multiple semiotic resources has the potential to foster their awareness of audience, purpose, and other rhetorical features. This case study explores L2 students’ engagement in a multimodal digital storytelling (DST) project. The study reports on how DST was integrated into an undergraduate writing curriculum and how students’ engagement in multimodal composition fostered their rhetorical awareness and influenced their perceptions of academic writing. The findings suggest that the process of transmediation (a process of translating meaning from one sign system into another) facilitated students’ revision, directing their attention to rhetorical features of writing. Creating digital stories enabled students to make sophisticated and deliberate rhetorical choices to bolster their arguments and transmit their messages effectively. Students exhibited positive attitudes toward the integration of multimedia. The results suggest that thoughtful integration of DST and multimodality have unique affordances for L2 students’ writing development.

ChatGPT has been at the center of media coverage since its public release at the end of 2022. Given ChatGPT’s capacity for generating human-like text on a wide range of subjects, it is not surprising that educators, especially those who teach writing, have raised concerns regarding the implications of generative AI tools on issues of plagiarism and academic integrity. How do we navigate the already complex discourse around what constitutes plagiarism and how much assistance is acceptable within the bounds of academic integrity? As we contemplate these theoretical questions, a more practical approach is to assess what these tools can do to facilitate students’ learning of existing academic integrity codes. In this short piece, we share our exploratory interactions with ChatGPT relevant to issues of plagiarism and academic integrity, hoping to shed light on how writing instructors can use the tool to facilitate the teaching and learning of ethics in academic writing.

The COVID-19 pandemic impacted traditional pedagogies and modalities for facilitating instruction to all students, including those enrolled in higher education courses. Given the disruptions to in-person learning and the growing interest in distance education, higher education institutions are increasing the number of asynchronous online and blended courses in educational programs. This increase also coincides with the growing numbers of diverse students, including those from multilingual backgrounds, across college campuses in the United States. Student diversity calls for more inclusive instructional delivery modes. This article describes Universal Design for Learning (UDL), a framework designed to enhance teaching and learning for all students. It also explains how university faculty can implement UDL in online courses in higher education and in preservice teacher preparation courses. Finally, the article discusses the implications of UDL implementation for multilingual students enrolled in higher education courses.

Although the Master of Arts in Teaching English to Speakers of Other Languages (MA TESOL) practicum course is meant to initiate preservice teachers (PSTs) into the TESOL profession, it is not uncommon for PSTs to feel a high level of stress as they prepare to teach their first solo lesson. This article proposes the implementation of mixed-reality simulations, in which PSTs teach student avatars- computer-generated 3D representations of real-life students displayed on a computer or large screen—as a precursor of the first solo lesson. In contrast to other fields, in which mixed-reality simulations have been used for some time, their use in MA TESOL teacher preparation is more recent (Kamhi-Stein et al., 2020). This article describes Mursion, a mixed-reality simulation platform, identifies the principles supporting the integration of mixed-reality simulations in MA TESOL teacher preparation, and discusses the benefits and challenges arising from their implementation.

Choosing relevant vocabulary to teach in ESL composition courses can be a difficult task for instructors. They often rely on intuition rather than empirical methods for vocabulary selection. This paper presents two case studies where corpora were used either to select vocabulary items for instruction or to scaffold student learning of vocabulary through exposure to corpora. Corpora and language analysis tools were used to complement vocabulary selection and instruction. This vocabulary selection process complements perceptual approaches as it provides empirical measures for the selection and a data driven approach to learning as instructors teach and students learn contextually relevant words.

The emergence of AI writing assistants has raised concerns about their potential impact on language diversity, preservation, and education. This paper examines the capabilities and limitations of AI writing assistants in generating dialectic text in response to academic and professional writing prompts. The study uses a concordance tool to conduct N-gram and keyword analyses on texts generated by AI writing assistants to examine the collocational patterns and linguistic conventions in AI-generated text productions. The results suggest that AI writing assistants rely heavily on Standard American English (SAE) conventions. Pedagogical implications include integrating language technology to promote language diversity and preservation and utilizing register-diversified corpora to enhance students’ understanding of language beyond SAE. This study emphasizes the importance of critically evaluating and revising AI-generated content and contributes to a better comprehension of the potential role of AI writing assistants in academic and professional writing.