Volume 2, Issue 1

Literature in the Twitterverse emerges through two cyber-species: the tweeter and the Twitterbot. Both are fighting for relevance in digital creative writing to win the reader’s attention. They tweet, read, and write to each other remotely, and they correct each other as an algorithmic sharpening symbiosis. This essay will analyse the symmetrical linguistics of Twitterbot poetry that incorporates critical code studies of its source code as a subset of technodiscursive analysis to decipher the meanings of the tweet-poems produced by Leonardo Flores’ @Protestitas. This text contextualises itself in a genre of generative electronic literature with multiple interdisciplinary approaches. Our study focuses on this Twitterbot poetry and its relation to socio-technological communication. We also seek to capture the intention of the Twitterary robopoet @Protestitas through a technodiscursive analysis that consists of what we call “Four-Dimensional Analysis (henceforth 4DAs)” of the Twitterbot poetry. How does Twitterbot produce its output as executed codes? How do we read the language of the tweet-poem? Our study seeks to demystify these phenomena in this article. The analysis deciphers Twitterbot poetry in the manner of the rebus, deconstructing the four semantic elements of the ten excerpts of @Protestitas with its source code. We have compared the source codes with the output on the screen. Therein, we discovered that both of them project the same literary spark.

The last two decades have witnessed a growing interest in developing and incorporating tasks in technology-mediated settings. Correspondingly, technology-mediated task-based language teaching (technology-mediated TBLT) has established itself as a distinct domain of TBLT research, exploring the benefits of technology in promoting task performance and outcomes of task-based learning. This research synthesis intends to contribute to this growing trend by focusing on one type of technology – immersive virtual reality (VR). We conducted a systematic search and reviewed 33 primary studies (2014–2024) that adopted VR technology to create tasks, and then we assessed learning outcomes of these tasks. In addition to reviewing study features for a general trend (e.g., target population, language, and knowledge/skill area), we evaluated the VR-based instructional tasks for common characteristics of pedagogical tasks established in the TBLT literature. Results showed that the majority of VR tasks are meaning-focused and authentic, but they are not sufficiently designed to produce non-linguistic outcomes (outcomes beyond the mere display of linguistic knowledge and skills).

This study investigates the ideological evolution in translator styles through a diachronic analysis of five English translations of The True Story of Ah Q . It aims to uncover how translators’ linguistic choices reflect their cultural, historical, and ideological contexts. A mixed-method approach was adopted, combining quantitative analysis using the Multidimensional Analysis Tagger (MAT) with qualitative interpretations based on Hatim and Mason’s ideological framework. The study focuses on six key features: modality, nominalisation, domestication, lexical recurrence, lexicalisation, and transitivity shifts, examining their role in conveying ideological priorities. Findings reveal statistically significant variations in translators’ linguistic strategies, with modality reflecting authority and agency shifts, while nominalisation and lexical recurrence highlight ideological reframing of key concepts. Domestication and transitivity shift further illustrate how translations align with target audience expectations. These findings underscore the non-neutral nature of translation, where linguistic choices serve as ideological markers shaped by sociopolitical environments and personal beliefs. This study contributes to translation studies and corpus linguistics by demonstrating how quantitative and qualitative methodologies can be combined to uncover ideological patterns in translation. It emphasizes the need to consider translators’ backgrounds and historical contexts when analysing translated texts, offering insights into the ideological dimensions of cross-cultural communication. Future research should expand this approach to larger translation corpora and multiple language pairs to deepen our understanding of how ideology shapes translation practices.

The PARSEME GRC guidelines distinguish between Light-Verb Constructions, such as to make a suggestion , and Verbal Idioms, such as to kick the bucket . Light-Verb Constructions need to contain a nominal element that is abstract and predicative. Verbal Idioms are lexically, morphologically, morphosyntactically, and syntactically inflexible. Syntactic nominalisations as the nominal element are usually disregarded especially in Light-Verb Constructions due to their structural and functional ambiguity. (Post)classical Greek has productive morpho-syntactic means for the nominalisation of any part of speech which are not restricted e.g. diatopically or distratically, whereas lexical nominalisation by means of derivational morphology is more restricted. This makes the exclusion of syntactic nominalisations seem artificial. When considering the eventiveness of the nominal component the crucial characteristic it emerges that non-deverbal and underived event nouns, including syntactic nominalisations, can in fact be predicative nouns in verbal multi-word expressions. Syntactic nominalisations can appear as predicative nouns in Light-Verb Constructions along with forming part of Verbal Idioms. Synchronically, verbal multi-word expressions containing a syntactic nominalisation can be gap fillers, indexical alternatives, or semantico-pragmatic alternatives to structures with a lexical nominalisation. The study is primarily based on the PARSEME GRC and ECF Leverhulme Sketch Engine corpora of literary classical Attic philosophical prose, historiography, and oratory.

This article provides a critical synthesis and analysis of the research on the application of generative AI (GenAI) in second language (L2) writing. It conceptualizes GenAI literacy, synthesizes the research on written feedback, establishes a framework for prompt engineering, critiques the research examining the validity of GenAI ratings in writing assessment, and summarizes empirical evidence on the differences between GenAI and human writing. Specifically, the following findings and arguments are presented and discussed. GenAI literacy consists of four components pertaining to users’ competence and knowledge of GenAI basics, effective use, output evaluation, and ethics. The research on written feedback shows that teacher feedback focuses more on content, while GenAI feedback focuses more on organization. This research also suggests a need for criteria-based feedback and feedback evaluation. Prompt engineering is discussed along three dimensions: input, task, and output, followed by snapshots of prompts used in feedback research. The studies on writing assessment reveal that GenAI ratings are more consistent with human ratings when GenAI is trained using a large number of scored essays and when the rating criteria are well-defined. Comparisons of GenAI and human writing demonstrate that GenAI writing is more formal, academic, and impersonal, while human writing is more personal, creative, and linguistically accessible. This article concludes by making sense of the research findings, identifying future directions, and proposing three principles that may guide the research, practice, and theory construction for GenAI: individualization, domain-specificity, and writer agency.

Heritage Language (HL) has been used as an umbrella definition to cover different facets of non-official/dominant languages (i.e., non-English in North America) within communities. Overall heritage language proficiency is highly related to literacy and general cognitive development. In America’s Deep South statest like Alabama, the three largest heritage languages are Spanish, Chinese, and Gujarati. Despite some parental investment and little governmental support in HL programs and schooling, most K-16 heritage language learners have a disparity between oracy, literacy, and overall HL proficiency. To remedy this discrepancy, we explore how generative AI provides Chinese Heritage Language (CHL) learners with tailored real-time feedback and observe how their literacy level changes with different social entities. By examining the influences of social-cultural factors on HL learners, we aspire to augment our comprehension regarding the efficacy of pedagogical modalities in equipping them to acquire advanced literacy. Surveys will be administered to evaluate participants’ familial, educational, and communal backgrounds, level of proficiency in HL, and attitudes toward AI models. ChatGPT3.5-4o will generate reading materials, with Prompt Engineering Techniques (PET) and Prompt-engineered Leading Protocols (PLP) designed for CHL learners at the beginning, intermediate, and advanced levels. To investigate the motivations to use AI and its impact on language learning, this study invites participants to engage in a six-month HL study by following pre-trained prompts and interacting with ChatGPT with the intensity, frequency, and duration specified by the learning protocol. Students’ and parents’ reports, periodic progress assessments, AI usage reports, and electrophysiological measurement (EEG) will be collected at the study’s initial, middle, and end times. This project will hold significant implications for AI tools’ role in bridging resource gaps in literacy and cognitive development in underserved communities.