https://oaj.jurnalhst.com/index.php/jhr id-ID Jurnal Humaniora Revolusioner https://oaj.jurnalhst.com/index.php/jhr/article/view/28308 <p><em>The development of generative artificial intelligence has brought significant changes to communication and educational practices, while also raising fundamental questions about the nature of language, particularly whether expressions generated by AI can possess meaning and intentionality in the same way as human language. This issue has become increasingly relevant as students, educators, and the wider public use technologies such as ChatGPT for learning, communication, and knowledge production. This study aims to critically analyze the concept of intentionality in AI-generated language from the perspective of the philosophy of language, focusing on the relationship between linguistic form, meaning, and communicative intention. The study employs a qualitative approach with an interpretive case study design. The participants consist of 12 individuals, including English language students and lecturers who have experience interacting with generative AI. Data were collected through semi-structured interviews and an analysis of human–AI conversation documents, and were then analyzed thematically to identify patterns of meaning-making and attribution of intentionality. The findings reveal three main themes: the tendency of humans to attribute intentionality based on the coherence of AI responses, the distinction between apparent meaning and human communicative intention, and the role of context in shaping interpretations of AI-generated language. The study contributes theoretically to expanding discussions in the philosophy of language concerning intentionality in machine-based communication and practically emphasizes the importance of critical AI literacy in English language education.</em></p> Annisa Humairah Meliala Natasya Sabila Nur Izza Assya'bana Hak Cipta (c) 2026 Jurnal Humaniora Revolusioner 2026-10-31 2026-10-31 10 10 https://oaj.jurnalhst.com/index.php/jhr/article/view/28318 <p>Kecerdasan buatan generatif mempersulit pemahaman konvensional tentang kebenaran, kepercayaan, dan agensi komunikatif, terutama ketika respons yang fasih dapat memuat informasi karangan yang lazim disebut halusinasi AI. Penelitian ini menelaah apakah keluaran AI yang keliru dapat dipahami secara bermakna sebagai kebohongan dan syarat apa yang harus terpenuhi untuk atribusi tersebut. Dengan analisis kasus konseptual kualitatif berlandaskan filsafat bahasa, penelitian ini memadukan konsep makna penutur dan maksud komunikatif Grice dengan pembedaan filosofis antara kekeliruan, penipuan, dan kebohongan. Serangkaian prompt verifikasi diajukan kepada satu sistem AI generatif, dan jawabannya diperiksa terhadap catatan bibliografis. Referensi dan keterangan bibliografis yang diberikan sistem tersebut akurat, dan sistem menolak mengarang kutipan, volume jurnal, serta isi artikel rekaan; ketika ditanya apakah ia berbohong, sistem menyangkal memiliki maksud demikian. Kasus-kasus ini menunjukkan bahwa kefasihan saja tidak menandakan kekeliruan dan bahwa pernyataan AI tentang maksudnya sendiri merupakan keluaran linguistik yang perlu ditafsirkan. Penelitian ini mengusulkan pembedaan antara kekeliruan proposisional, penipuan yang dialami, dan kebohongan yang disengaja, serta menekankan pentingnya literasi AI yang kritis dan verifikasi epistemik.</p> <p><em>Generative artificial intelligence complicates conventional understandings of truth, trust, and communicative agency, particularly when fluent responses may contain fabricated information commonly called AI hallucinations. This study asks whether false outputs of AI can meaningfully be understood as lies and what conditions would have to hold for such an attribution. Using a qualitative conceptual case analysis informed by philosophy of language, it combines Gricean accounts of speaker meaning and communicative intention with philosophical distinctions among falsehood, deception, and lying. A series of verification prompts was posed to one generative AI system, and its responses were checked against bibliographic records. The references and bibliographic details the system supplied were accurate, and it declined to fabricate a quotation, a journal volume, and the contents of an invented article; asked whether it had lied, it denied intending to. These cases show that fluency alone does not signal falsehood and that an AI's statements about its own intention are linguistic outputs requiring interpretation. The study proposes a distinction among propositional falsehood, experienced deception, and intentional lying, and highlights the importance of critical AI literacy and epistemic verification.</em></p> Suhailah Rizki Anggun Misnawati Haura Khansa Windy Aulia Hak Cipta (c) 2026 Jurnal Humaniora Revolusioner 2026-10-31 2026-10-31 10 10