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All Journal JOLLT Journal of Languages and Language Teaching Journal of Modern Approaches to Communication, Humanities, and Academia
Polim, Hidayat
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Humanities Education Languange, Linguistic, Communication & Media Other

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AI Writing Assistants in English Language Learning: Evaluating Feedback Quality and Learner Autonomy Triwibowo, Febri Dhany; Polim, Hidayat
MATCHA: Journal of Modern Approaches to Communication, Humanities, and Academia Vol. 1 No. 2 (2025): MATCHA: Journal of Modern Approaches to Communication, Humanities, and Academia
Publisher : CV. Akademi Merdeka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70152/matcha.v1i2.195

Abstract

As artificial intelligence (AI) writing assistants become increasingly integrated into English language learning (ELL), their influence on feedback quality and learner autonomy warrants critical evaluation. This mixed-methods study investigates how AI-generated feedback compares to teacher feedback in terms of accuracy, clarity, usefulness, and its impact on learner autonomy. Forty university-level ELLs completed writing tasks using either AI tools or instructor input. Results showed that while AI feedback was effective for correcting surface-level errors, it lacked the pedagogical depth necessary to foster meaningful learning. Teacher feedback, by contrast, encouraged reflective revision, metacognitive engagement, and greater writing independence. Despite AI tools’ convenience and immediacy, learners often accepted suggestions passively, which hindered the development of critical evaluation skills and self-regulated learning. The study concludes that AI writing assistants can serve as useful supplements in writing instruction but should not replace human feedback. Instead, a hybrid model that combines technological efficiency with pedagogical insight may offer the most effective support for developing autonomous, reflective writers.
Task-Based Learning Reimagined: How Gemini AI Enhances EFL Students’ Speaking Fluency in Self-Directed Learning Widodo, Anang; Vivianti, Vivianti; Karim, Sayit Abdul; Polim, Hidayat
JOLLT Journal of Languages and Language Teaching Vol. 14 No. 2 (2026): April
Publisher : Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jollt.v14i2.18207

Abstract

In self-directed learning, EFL learners face persistent fluency challenges, particularly when access to native speakers or teachers is limited. Recent advancements in generative AI, such as Gemini AI, offer transformative potential by simulating human-like interactions and adapting to individual learner needs. This study investigates how Gemini AI uniquely facilitates speaking fluency through Task-Based Learning in self-directed learning and its impact on students’ speaking performance. This study involved 19 participants enrolled in an English Discussion class at the Universitas Teknologi Yogyakarta who engaged in a four-week intervention program. Utilizing a mixed-methods design, the study combined quantitative analysis of speaking fluency metrics with qualitative examination of student reflection reports. Participants completed weekly speaking tasks using Gemini AI as a conversational partner, documenting their experiences in a structured report. Quantitative results showed a 13% average improvement in speaking fluency, particularly in lexical diversity and reduced hesitation. Qualitative analysis revealed five key themes: increased confidence in spontaneous speech, appreciation for 24/7 accessible practice, effective feedback to improve students' speaking skills, enhancing vocabulary, and fostering a non-judgmental learning environment. The findings suggest that Gemini AI can effectively supplement classroom instruction for speaking skill development. However, the generalizability of findings is constrained by the small number of participants and the short intervention period. Future research should employ longitudinal designs with larger cohorts to substantiate the long-term efficacy of AI-assisted task-based language teaching. Subsequent studies should also systematically investigate strategies to mitigate Gemini shortcomings, including optimizing feedback relevance and turn-taking mechanics for educational dialogue.
Co-Authors Anang Widodo Sayit Abdul Karim Triwibowo, Febri Dhany Vivianti