Middle School EFL Students' Attitudes Towards Artificial Intelligence: A Mixed-Methods Analysis
Year: 2026
Type: article
Study design: Observational Study
Abstract: This research investigates Turkish middle school students' attitudes towards the use of AI in English language learning, drawing on an explanatory-sequential mixed-methods design. It involved 165 public-school students in Istanbul during the 2025-2026 academic year. During the quantitative phase, the Artificial Intelligence Attitude Scale was administered; to extend those findings, researchers conducted semi-structured written interviews with 24 students. From this study, it has emerged that while the students generally held a positive view of AI, with low resistance to it, their eagerness to be trained in AI remained fairly modest. In this process, experience with AI appeared as a crucial factor affecting these attitudes, while boys seemed to hold a higher belief in AI's potential. Neither academic achievement nor the level of parental education significantly influenced attitudes. Qualitative results reveal that such optimism is based on expected pragmatic benefits: improved speaking practice, better pronunciation, and vocabulary enhancement. On the other hand, students also mentioned several concerns: the reduction of human contact, ethical hazards, overdependence on AI, and a redefinition of the teacher's role. This study emphasizes that the approach should be human-centered and critical in order to preserve and support the pedagogical role of the teacher during the integration of AI into learning. Besides, it underlines that embedding AI literacy within the framework of educational curricula is vitally important. Eventually, it is indicated that successful use of AI in education, especially in language teaching, depends more on the ethical awareness and responsible participation of students than on simple access to technology.
Source: DergiPark (Istanbul University)
Author: Güneş, Gökhan
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7136338838
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