دانشگاه تربیت مدرس جستارهای زبانی 2322-3081 مقالات آماده انتشار 2026 01 05 From Feedback to Feedforward: AI-Enhanced Formative Assessment for Self-Evaluation in EFL Non-English Majors’ Listening–Speaking Development From Feedback to Feedforward: AI-Enhanced Formative Assessment for Self-Evaluation in EFL Non-English Majors’ Listening–Speaking Development 28063 10.48311/lrr.2026.117091.82932 FA Dodi Mulyadi Universitas Muhammadiyah Semarang Ratu Dinny Fauziah Faculty of Islamic Religious Education Study Program, STIT Insan Kamil Bogor Joko Slamet Department of English, Faculty of Letters, Universitas Negeri Malang, Malang, Indonesia 0000-0002-8676-8525 Yusuf Hidayat Department of Early Childhood Education and Care, Faculty of Education, Sekolah Tinggi Agama Islam Putra Galuh Ciamis, Indonesia Journal Article 2025 10 17 Research on formative assessment in English as a Foreign Language (EFL) contexts has emphasized its potential to enhance learner autonomy, reflection, and sustained improvement; however, limited research has explored how Artificial Intelligence (AI) can transform formative feedback into feedforward practices that strengthen learners’ self-evaluation and skill development, particularly in listening and speaking. This study examined the effects of AI-enhanced formative assessment on learners’ self-evaluation and their listening–speaking performance. Adopting a mixed-methods quasi-experimental design, the study involved 72 non-English majors enrolled in an “English for Communication” course at a private university in West Java, Indonesia. Participants were assigned to an experimental group (n = 36), which received AI-supported formative feedback, and a control group (n = 36), which received conventional feedback, over an eight-week intervention. Quantitative data were collected through pre- and post-tests of listening and speaking, while qualitative data were obtained from a closed-ended questionnaire incorporating AI feedback log items and semi-structured interviews with six selected participants. The results indicated that the AI-supported group achieved significantly greater gains in listening comprehension, pronunciation accuracy, fluency, and discourse organization than the control group. Qualitative findings further revealed enhanced self-monitoring, reflective awareness, and feedforward-oriented learning behaviors among learners using AI feedback. However, some participants reported challenges in interpreting nuanced AI-generated comments and translating them into actionable improvement strategies. Overall, the findings demonstrate that AI-enhanced formative assessment can effectively support self-evaluation and listening–speaking development, provided that pedagogical guidance is integrated to facilitate meaningful use of automated feedback. Research on formative assessment in English as a Foreign Language (EFL) contexts has emphasized its potential to enhance learner autonomy, reflection, and sustained improvement; however, limited research has explored how Artificial Intelligence (AI) can transform formative feedback into feedforward practices that strengthen learners’ self-evaluation and skill development, particularly in listening and speaking. This study examined the effects of AI-enhanced formative assessment on learners’ self-evaluation and their listening–speaking performance. Adopting a mixed-methods quasi-experimental design, the study involved 72 non-English majors enrolled in an “English for Communication” course at a private university in West Java, Indonesia. Participants were assigned to an experimental group (n = 36), which received AI-supported formative feedback, and a control group (n = 36), which received conventional feedback, over an eight-week intervention. Quantitative data were collected through pre- and post-tests of listening and speaking, while qualitative data were obtained from a closed-ended questionnaire incorporating AI feedback log items and semi-structured interviews with six selected participants. The results indicated that the AI-supported group achieved significantly greater gains in listening comprehension, pronunciation accuracy, fluency, and discourse organization than the control group. Qualitative findings further revealed enhanced self-monitoring, reflective awareness, and feedforward-oriented learning behaviors among learners using AI feedback. However, some participants reported challenges in interpreting nuanced AI-generated comments and translating them into actionable improvement strategies. Overall, the findings demonstrate that AI-enhanced formative assessment can effectively support self-evaluation and listening–speaking development, provided that pedagogical guidance is integrated to facilitate meaningful use of automated feedback.