Self-assessment and critical thinking: An explanatory sequential study of AI implications in English language teaching - Miftah Al Farhan; Noor Aida Aflahah (University of Muhammadiyah Malang, 2026)
The rapid integration of artificial intelligence (AI) into English language learning calls for a more in-depth examination of its implications for students’ self-assessment and critical thinking skills, given the potential for both improved learning performance and the risk of cognitive dependency. This study seeks to develop deeper and contextually grounded understanding of how the integration of AI in English language learning affects students’ abilities in self-assessment and critical thinking through an explanatory sequential mixed-methods design. Quantitative data were collected via a questionnaire distributed to 200 undergraduate English education students across four Indonesian regions, followed by qualitative interviews with eight selected participants. The collected data were analyzed using an explanatory sequential approach, in which quantitative questionnaire data were processed through descriptive statistical analysis, followed by qualitative analysis involving data condensation, data display, and conclusion drawing. The quantitative results reveal that AI-based media are widely and positively perceived as effective tools for improving material comprehension, grammatical accuracy, and self-reflection through automated feedback. However, variations in utilization suggest the need for pedagogical support to optimize reflective engagement and long-term learning strategies. Qualitative findings further demonstrate that AI fosters metacognitive awareness and autonomy but simultaneously raises challenges such as overreliance on AI validation and diminished confidence in independent thinking. Overall, AI is perceived as a supportive cognitive partner rather than a substitute for human reasoning. Future research should examine how AI literacy and ethical awareness affect students’ ability to critically evaluate AI-generated content, and explore its use across disciplines and cultures to improve generalizability.