MATCHA: Journal of Modern Approaches to Communication, Humanities, and Academia

Exploring Students’ Use of AI Translation and Paraphrasing Tools during Academic Reading

Endang Siti Nurkholidah, Shafiq ur Rehman — Mon, 01 Jun 2026 00:00:00 +0700

The increasing availability of AI translation and paraphrasing tools offers new possibilities for supporting English as a Foreign Language (EFL) students’ academic reading. This issue is particularly relevant in university contexts where students are required to engage with complex English academic texts yet often encounter linguistic and conceptual difficulties. This study aims to investigate how university students use AI translation and paraphrasing tools across the before-reading, during-reading, and after-reading stages of academic reading and to examine how these stage-specific uses support students’ meaning-making processes. Using a qualitative exploratory design, data were collected from 20 university students who were familiar with AI-assisted reading practices through think-aloud protocols, reading journals, and semi-structured interviews, and were analyzed thematically. Findings reveal that students engage with AI tools in a strategic and reflective manner, employing translation for lexical support, paraphrasing for structural simplification, and both tools for reflective consolidation. Students’ use of AI was stage-specific: pre-reading translation enabled previewing and activation of prior knowledge; during reading, paraphrasing supported comprehension monitoring and verification; after reading, students consolidated understanding through summarization and clarification. The study highlights that AI tools function as both cognitive and metacognitive scaffolds, extending learners’ capacity to navigate complex academic texts while promoting learner agency and self-directed reading. These insights offer practical implications for integrating AI tools into academic literacy instruction and suggest avenues for future research on longitudinal effects, disciplinary differences, and adaptive strategies for diverse proficiency levels.

Supporting Spoken Interaction in Academic Settings: Students’ Use of Real-Time AI Transcription in EFL Classrooms

Irma Ratna Ningsih, Ken Paul M. Espinosa — Mon, 01 Jun 2026 00:00:00 +0700

Abstract: This study explores EFL students’ use of real-time AI transcription to support spoken interaction in academic classroom settings and examines their perceptions of its role during oral activities. Adopting an exploratory mixed-methods design, the study integrates qualitative data from classroom observations and semi-structured interviews with quantitative data from a self-report questionnaire. The qualitative findings reveal that students employ AI transcription in self-directed and interaction-sensitive ways, such as monitoring speech accuracy, managing communication breakdowns, supporting turn-taking, and reducing anxiety during real-time interaction. These practices indicate that AI transcription functions not merely as a corrective aid but as an interactional resource embedded within ongoing classroom discourse. Quantitative results further show generally positive student perceptions regarding the usefulness of real-time AI transcription for enhancing clarity, confidence, and engagement in spoken interaction. By foregrounding students’ situated practices and experiences, this study contributes to emerging research on AI-mediated spoken communication in EFL contexts. The findings suggest that real-time AI transcription holds pedagogical potential when integrated flexibly and responsively to learners’ communicative needs, offering insights for educators seeking to support spoken interaction through AI-enabled technologies.

Investigating Students’ Informal Use of AI Tools during University English Classes

Reni Apriani, Shibani Basu Dubey — Mon, 01 Jun 2026 00:00:00 +0700

The rapid proliferation of artificial intelligence (AI) tools has transformed how university students engage with English learning, often beyond formal instructional designs. This qualitative study investigates students’ informal use of AI tools during university English classes and examines how they interpret the role of these tools in shaping their learning experiences and classroom engagement. Drawing on classroom observations and semi-structured interviews, the study explores the ways students independently integrate AI tools such as translation, paraphrasing, and generative assistance to support comprehension, participation, and task completion in real time. The findings reveal that students employ AI tools strategically to manage linguistic challenges, maintain engagement, and enhance confidence, while simultaneously negotiating concerns related to overreliance and academic norms. Students generally perceive AI as a supportive, complementary resource rather than a substitute for instruction or effort. By foregrounding informal and learner-initiated AI use, this study contributes to current discussions on AI integration in English language education, highlighting the importance of recognizing students’ everyday practices and perspectives when developing pedagogical and policy responses to AI-enhanced learning environments.

Enhancing Oral Communication in Real Time: University Students’ Self-Directed Use of AI-Powered Speech Recognition in English Classrooms

Masrurotul Ajiza, Ali Said Al Matari , Muthmainnah Muthmainnah — Mon, 01 Jun 2026 00:00:00 +0700

The increasing availability of AI-powered speech recognition has created new possibilities for supporting oral communication in English classrooms. This study explores how university students use AI-powered speech recognition tools in a self-directed manner during real-time speaking activities and how they perceive the usefulness of these tools for enhancing oral communication. Adopting a convergent mixed-methods design, the study integrates qualitative data from classroom observations and semi-structured interviews with quantitative data from a descriptive Likert-scale questionnaire. The qualitative findings reveal that students use speech recognition strategically to monitor intelligibility, rehearse spoken output, and manage speaking-related anxiety during communicative tasks. Rather than replacing interaction, the tool functions as a flexible support that learners draw on selectively according to situational needs. The quantitative results indicate generally positive perceptions of the tool’s usefulness, particularly in relation to confidence, fluency awareness, and ease of use. By combining observed practices with learners’ reported perceptions, this study offers a classroom-grounded account of AI-powered speech recognition use in real-time speaking contexts. The findings contribute to ongoing discussions on AI-assisted language learning by highlighting the pedagogical potential of speech recognition as a learner-oriented resource for supporting oral communication.

From L1 Thinking to L2 Text: Exploring AI-Mediated Translanguaging in University EFL Writing Tasks

Nur Ifadloh, Busayo Oluwabukola Alao, Eka Pujiastuti — Mon, 01 Jun 2026 00:00:00 +0700

The increasing availability of artificial intelligence (AI) tools has reshaped how university students engage in academic writing, particularly in English as a Foreign Language (EFL) context where multilingual resources play a central role. This qualitative study explores how EFL students use AI tools to mediate translanguaging practices during academic writing tasks and how they interpret AI’s role in moving from L1-based thinking to L2 written production. Drawing on think-aloud protocols, reflective writing journals, writing artifacts, and semi-structured interviews, the study examines writing as a process-oriented and mediated activity. The findings show that students strategically employ AI to externalize ideas, negotiate meaning, and refine language while maintaining agency and authorship through critical evaluation and revision of AI-generated text. AI tools were perceived not only as linguistic support but also as cognitive and emotional scaffolding that reduced writing anxiety and facilitated engagement with complex academic tasks. By foregrounding students’ practices and interpretations, this study contributes to growing discussions on AI, translanguaging, and writing pedagogy, highlighting the need for process-oriented and reflective approaches to AI use in EFL writing instruction.