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All Journal EGALITA Dinamisia: Jurnal Pengabdian Kepada Masyarakat Infotekmesin JISA (Jurnal Informatika dan Sains) Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Journal of English Language and Education TIERS Information Technology Journal Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat Jurnal Penelitian Ilmu Pendidikan Indonesia Journal of Artificial Intelligence and Digital Business UNNES International Conference on ELTLT Pengabdian Jurnal Abdimas ⁠International Journal of Sustainable Social Culture, Science Technology, Management, and Law Humanities ⁠International Journal of Asian Pacific Community Service Journal of Information Technology and its Utilization Wanastra: Jurnal Bahasa dan Sastra
Annisa Romadloni
Politeknik Negeri Cilacap
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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Gendered Self-Perceptions, Inclusive Classroom Climate, and Responsible Generative-AI Use in English for Specific Purposes Romadloni, Annisa; Wanti, Linda Perdana; Sari, Laura
Jurnal Penelitian Ilmu Pendidikan Indonesia Vol. 5 No. 1 (2026)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat, Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jpion.v5i1.1071

Abstract

Gendered perceptions shape participation and belonging in higher education, and the rapid uptake of generative AI adds new equity and academic-integrity risks in English for Specific Purposes (ESP). This study examined how communal/agentic self-perceptions and perceived gender-inclusive classroom climate relate to responsible generative-AI orientations among Indonesian vocational students. A cross-sectional quantitative secondary analysis was conducted using an end-of-course survey (N=90) with reliability, descriptive, correlational, and regression analyses. Results indicated high communal and moderate agentic self-perceptions, generally positive inclusion perceptions with lingering stereotype signals in group tasks, and high perceived AI utility alongside strong concerns about inaccurate and biased outputs. Inclusion climate and perceived AI utility jointly predicted stronger governance-oriented norms (e.g., disclosure, citation, fairness). Scenario judgments rated AI most acceptable for summarizing, translating, and language correction when students revised/verified outputs, and least acceptable for generating whole reports or slide decks without meaningful authorship.
Politeness and Indirectness: When Sexism Hides Behind Advice in Workplace Statements Romadloni, Annisa; Wanti, Linda Perdana; Sari, Laura
Wanastra: Jurnal Bahasa dan Sastra Vol. 18 No. 1 (2026): March
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/wanastra.v18i1.12140

Abstract

Sexism in workplaces is often framed as ordinary guidance or evaluation rather than as overt hostility, which can cause sentiment and toxicity filters to miss it. A corpus analysis was conducted using the Sexist Workplace Statements dataset (1,142 statements; 627 labeled sexist). A pragmatics-informed operationalization was applied to classify sexist statements as benevolent or hostile and to label each statement’s primary speech act as advice, evaluation, insult, joke, or complaint. Benevolent sexism was estimated to constitute 73.8% of sexist statements, while hostile sexism constituted 26.2%. Benevolent sexism was concentrated in evaluation and advice, whereas hostile sexism was concentrated in insults. A sentiment-or-profanity toxicity proxy achieved high precision but low recall for sexism, capturing most hostile sexism while missing most benevolent sexism. A supervised baseline (TF–IDF plus logistic regression) performed well on the binary label but still showed false negatives dominated by benevolent evaluations. The findings were interpreted through ambivalent sexism theory, speech act theory, and politeness theory, highlighting how indirectness and face-work enable discriminatory norms to be advanced under the guise of help.  These results make explicit that sexism detection systems should incorporate pragmatics- and speech-act-aware features to reliably identify benevolent, “helpful”-framed workplace sexism that standard sentiment/toxicity signals systematically overlook.
Co-Authors Abdul Rohman Supriyono Andesita Prihantara Annas Setiawan Prabowo Cahya Vikasari, Cahya Eka Tripustikasari, Eka Fadhilla Hazrina Fadillah Fadillah Fadillah Fadillah Ganjar Ndaru Ikhtiagung Hastuti, Hety Dwi Hazrina, Fadhilla Hety Dwi Hastuti Ilham Aditya Pangestu Isa Bahroni, Isa Johanna, Anne Kukuh Muhammad Laura Sari Lina Puspitasari Linda Perdana Wanti Listyaningrum, Rostika Lutfi Syafirullah Muhammad Nur Faiz Muhammad, Kukuh Musawarman, Mr. Nur Wachid Adi Prasetya Nur Wahyu Rahadi Oman Somantri Pramita, Ayu Rahadi, Nur Wahyu Ratih Hafsarah Maharrani Ricak Agus Setiawan Rostika Listyaningrum Rostika Lityaningrum Sari, Laura Supriyono, Abdul Rohman Wanti, Linda Perdana Wati, Linda Perdana Widianingsih, Betti