Vira Wulandari
Universitas Muhammadiyah Banjarmasin

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

Towards an Evidence-Based Institutional AI Policy: Phenomenological Insights from English Language Teaching in Indonesian Higher Education Miftah Al Farhan; Soraya Djamilah; Vira Wulandari
Ahmad Dahlan Journal of English Studies Vol. 13 No. 2 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/adjes.v13i2.2355

Abstract

The rapid adoption of artificial intelligence (AI) in higher education has created new opportunities for English Language Teaching (ELT) while simultaneously raising concerns regarding learner autonomy, academic ethics, and institutional governance. Although Indonesia has introduced national guidelines for the responsible use of generative AI in higher education, evidence on how AI is experienced and interpreted within specific educational contexts remains limited. This study explores the lived experiences of AI use among lecturers, students, and institutional leaders in English Language Teaching within Indonesian higher education. Employing an Interpretative Phenomenological Analysis (IPA), the study analyzed data collected from 46 participants across nine English Language Education programs in South Kalimantan through semi-structured interviews, qualitative questionnaires, and institutional document analysis. Three superordinate themes emerged: (1) making sense of AI as an accessible learning partner, (2) negotiating learner autonomy and academic ethics in AI-assisted learning, and (3) translating lived experiences into evidence-based institutional AI policy. The findings indicate that participants perceived AI not merely as a technological tool but as a learning partner whose educational value depends on critical judgment, ethical responsibility, and contextual pedagogical practices. The study contributes to AI and ELT scholarship by providing a phenomenological understanding of AI-assisted learning and demonstrating how lived experiences can inform context-sensitive institutional AI policies that complement broader national policy directions. These findings offer practical insights for higher education institutions seeking to develop responsible, evidence-based AI governance.