Perceptions of ai-based language learning tools: a case study on student adaptability and learning strategies

Abstract

The rapid integration of artificial intelligence (AI) into higher education has transformed how students approach language learning tasks. This study investigates university students’ perceptions of AI-based language learning tools, focusing on how these technologies influence their adaptability and learning strategies. Employing a qualitative case study design, data were collected from semi-structured interviews and reflective journals involving sixteen undergraduate students from various disciplines at an Indonesian public university. Thematic analysis revealed three key themes: perceived benefits and limitations of AI tools, diverse adaptive responses to automated feedback, and varying degrees of strategic engagement. Findings show that while student   ts generally appreciate the efficiency and support offered by AI applications, their level of critical engagement and adaptability varies significantly. Some students actively cross-check AI feedback and adjust their strategies thoughtfully, whereas others tend to accept suggestions passively, which may hinder deeper language development. The study highlights the need for explicit AI literacy training and pedagogical frameworks that foster critical and reflective use of AI tools in language learning contexts. These insights contribute to ongoing discourse on responsible AI integration in education and offer practical implications for educators and policymakers aiming to balance technological affordances with the cultivation of independent learning skills.