JOURNAL OF COMMUNICATION, LANGUAGE AND CULTURE

Attitudes Toward AI as Communicative Partners: A Human–Machine Communication (HMC) Analysis of Undergraduate Language Learners

Language learning has been revolutionized by the incorporation of AI-mediated language communication systems (AIMLCS), which provide individualized, scalable assistance. Nevertheless, little is known about how this particular group of undergraduate language learners views AI as collaborative partners as opposed to merely tools. This study examines undergraduate language learners' perceptions of AIMLCS as relational entities using a Human-Machine Communication (HMC) lens. Four HMC-derived dimensions: Functional Value, Relational Comparison, Partnership Perception, and Attitudinal Growth, were used in an exploratory quantitative survey of 55 Malaysian university students to gauge attitudes. Descriptive statistics were used to describe learner attitudes across the specified dimensions systematically. Specifically, mean scores and standard deviations were calculated. The findings demonstrate a dualistic viewpoint: students frequently identify a disparity in affective response, pointing out that these tools provide less emotional depth than human teachers, yet greatly appreciating the functional benefits of AIMLCS. AI is not seen by learners as a replacement for human connection, but rather as a complementary partner inside the learning environment. Additionally, respondents who reported continuous use also expressed more favourable opinions; however, longer-term studies are required to verify real changes in attitudes. The findings highlight the necessity of creating AI systems that are both communicatively and instructionally successful. By linking HMC theory and language pedagogy, this study offers educators and developers preliminary exploratory insights to optimize AI integration. It addresses both functional capabilities and relational restrictions to promote more supportive and engaging language-learning experiences.

JOURNAL OF COMMUNICATION, LANGUAGE AND CULTURE

Public responses to teacherless classrooms are shaped by specific ideologies of humanity, morality, labour, and educational authority, demonstrating how public responses to teacherless classrooms are shaped by specific ideologies of humanity, morality, labour, and educational authority.

JOURNAL OF COMMUNICATION, LANGUAGE AND CULTURE

Do Language Textbooks Challenge or Reinforce Cultural Stereotypes? Perspectives of Advanced Learners of Arabic

Language textbooks do more than facilitate linguistic development; they also mediate learners’ encounters with the cultural communities represented through the target language. This role becomes particularly significant in advanced foreign-language education, where learners are expected to engage critically with complex social, political, and cultural representations. This qualitative study examines how advanced learners of Arabic perceived and negotiated cultural representations in Al-Kitaab fii Ta'allum al-'Arabiyya, Part Three, with particular attention to whether the textbook challenged or reinforced pre-existing stereotypes about Arab cultures. The study draws on focus-group data collected from eight advanced American learners of Arabic specializing in diplomacy and politics at a language center in Rabat, Morocco. Participants had extensive experience using the textbook during their advanced Arabic studies. The focus-group discussion was audio-recorded, transcribed, anonymized, and subjected to iterative qualitative content analysis. Findings indicate a complex relationship between textbook representation and learner perception. Participants acknowledged the textbook’s contribution to introducing cultural, historical, and political issues related to the Arab world, yet they also perceived some representations as insufficiently diverse or critically contextualized. Rather than consistently disrupting pre-existing assumptions, certain representations were perceived as potentially confirming simplified understandings of Arab societies. Learners particularly emphasized the need for greater regional diversity, authentic voices, contemporary perspectives, sustained self-reflection, and opportunities for direct intercultural engagement. The findings suggest that cultural representation in advanced language textbooks should move beyond exposure to cultural information toward pedagogical practices that actively encourage learners to interrogate, reassess, and complicate their prior assumptions. The study contributes to discussions of textbook evaluation, intercultural learning, and cultural representation in Arabic-as-a-foreign-language education.

Mediation Without a Mind: A Sociocultural Reconsideration of Student Feedback Literacy for Generative AI Feedback in ELT Writing

Generative artificial intelligence increasingly provides immediate feedback to English language learners, yet learners’ capacity to interpret, evaluate, and act on such feedback, known as feedback literacy, remains undertheorised in relation to this new source. This conceptual paper adopts a theory adaptation approach, using Carless and Boud’s four-dimensional model of student feedback literacy as the domain theory and Vygotsky’s sociocultural theory as the method theory. It examines how appreciating feedback, making judgements, managing affect, and taking action are reshaped when feedback is generated by general-purpose artificial intelligence rather than by teachers or peers. The paper argues that the model remains useful, but that each dimension faces additional strain because generative artificial intelligence functions as a mediating artefact and cannot be assumed to perform the pedagogical role of a more knowledgeable other. Although it can provide responsive assistance, it does not reliably identify a learner’s zone of proximal development, interpret developmental needs, or provide support that is carefully adjusted and gradually withdrawn. Learners must therefore undertake more of the evaluative, emotional, and regulatory work required to use feedback effectively. In English language writing contexts, target-language proficiency influences how strongly these limitations are experienced, creating a proficiency paradox in which learners who depend most on artificial intelligence feedback may have fewer linguistic resources to judge its accuracy, relevance, and appropriateness. The paper uses illustrative ELT writing scenarios and indicative proficiency benchmarks to anchor this argument, suggesting that the paradox may be most acute for learners at CEFR A2-B1, while recognising that such boundaries are gradual rather than fixed. It concludes that feedback literacy for generative artificial intelligence should be deliberately developed through teacher-supported mediation, guided verification, reflective revision, comparison of human and artificial intelligence feedback, and selective adaptation of suggestions.

Mapping human–AI communication in a Russian higher education context: a psychometric network analysis of AI literacy and attitudinal dispositions

As generative and chat-based artificial intelligence (AI) systems evolve into communication partners, students' attitudes toward these machine interlocutors have become a central question in communication research. These attitudes are not culturally neutral; they are shaped by the media ecology, linguistic repertoire, and social norms of the communication environment. Drawing on a humanmachine communication framework, this study reconceptualizes AI literacy as communicative competence with machine interlocutors and attitudes toward AI as culturally mediated evaluative orientations. The aim was to map their conditional architecture in the Russian cultural-communicative context. In a cross-sectional design, 668 undergraduate students from three Russian universities completed the Russian-adapted Meta Artificial Intelligence Literacy Scale and the General Attitudes towards Artificial Intelligence Scale. A standardized partial correlation network was estimated. Centrality, bridge centrality, network invariance, and community structure were examined. The strongest bridge was found between practical interaction with machines (Apply AI) and positive attitude. Exploratory network analysis placed Apply AI within the attitude cluster. Critical evaluation was located at the structural center of the network. Negative attitude remained peripheral. Persuasion and emotion regulation competencies merged into a single ESEM dimension. The network remained invariant across gender and frequency of use. This suggests that machine interaction and its evaluation form an integrated communicative-relational domain. A linguistic-cultural reading of this pattern is offered as a post hoc interpretation. The findings extend the human-machine communication framework to Russian higher education, a setting underrepresented in AI literacy research. They also support the reconceptualization of AI literacy as a communicative-cultural construct.