ILMIY TADQIQOTLAR VA ULARNING YECHIMLARI JURNALI
JOURNAL OF SCIENTIFIC RESEARCH AND THEIR SOLUTIONS
VOLUME 6, ISSUE 01, IYUL 2025
WORLDLY KNOWLEDGE NASHRIYOTI
worldlyjournals.com
REAL-TIME AI FEEDBACK VS. TEACHER FEEDBACK ON LISTENING TASKS: A
COMPARATIVE STUDY
Baxramova Malika Muzaffarovna
Urgench State Pedagogical Institute
Abstract:
This study explores the comparative impact of real-time Artificial Intelligence (AI)
feedback and traditional teacher feedback on learners’ performance in English as a Foreign
Language (EFL) listening tasks. As AI technologies gain ground in educational environments, their
capacity to offer immediate, data-driven, and personalized feedback has positioned them as potential
complements or even alternatives to human instruction. The research draws on a six-week
intervention with Uzbek EFL learners, comparing two groups: one using AI-integrated listening
platforms and the other receiving direct teacher feedback. Through quantitative assessments and
qualitative observations, the study investigates comprehension gains, learner engagement, and
affective responses. Findings reveal that AI feedback supports faster self-correction and increases
student autonomy, while teacher feedback fosters deeper comprehension and emotional connection.
Both feedback types contribute positively to learning, albeit in different ways. The paper concludes
that a blended approach—integrating AI precision with human empathy—offers the most
pedagogically effective solution for developing listening skills in EFL contexts. Implications for
classroom practice, teacher training, and AI tool design are discussed.
Keywords :
Artificial Intelligence, real-time feedback, teacher feedback, listening comprehension,
EFL learning, educational technology, learner engagement, formative assessment, feedback
effectiveness, Uzbekistan.
As language education continues to evolve in the digital era, the integration of Artificial Intelligence
(AI) into classroom practices is reshaping how students engage with language skills, especially
listening. One of the most discussed innovations is the use of real-time AI-generated feedback
during listening tasks. Unlike traditional teacher feedback, which is typically delayed and context-
bound, AI systems can instantly evaluate learners’ responses, offer corrections, and provide
performance analytics. This comparative study examines the effectiveness of real-time AI feedback
versus teacher-provided feedback in the context of English as a Foreign Language (EFL) listening
instruction. Drawing on classroom experiments and learner reflections, the study explores the
pedagogical implications, learner preferences, and outcome differences between the two modes of
feedback.
AI-based feedback systems utilize speech recognition and natural language processing to evaluate
pronunciation, comprehension accuracy, and task completion. In listening tasks, these systems can
track how many times a student listens to an audio, where they pause or replay, and how accurately
they respond to questions. Immediate feedback is then generated in the form of scores, suggestions,
or prompts for improvement. This immediacy encourages students to reflect on their performance
while the task is still cognitively active, promoting self-correction and deeper learning. Moreover,
AI tools are consistent, tireless, and available outside class time, giving students autonomy and
flexibility in practice.
In contrast, teacher feedback carries the advantage of human nuance, empathy, and pedagogical
intuition. Teachers can tailor their comments to individual learning styles, emotional states, and
contextual factors. They are able to clarify misunderstandings, use simplified explanations, and
build a supportive learning atmosphere. Moreover, teacher feedback often incorporates motivational
elements and a relational dimension, which AI lacks. However, teacher feedback is generally
ILMIY TADQIQOTLAR VA ULARNING YECHIMLARI JURNALI
JOURNAL OF SCIENTIFIC RESEARCH AND THEIR SOLUTIONS
VOLUME 6, ISSUE 01, IYUL 2025
WORLDLY KNOWLEDGE NASHRIYOTI
worldlyjournals.com
delayed, limited by classroom time constraints, and varies in consistency depending on the teacher’s
workload and observation skills.
Data from a sample of 100 Uzbek EFL learners aged 13 to 17 was analyzed over a six-week period.
One group used AI-integrated listening apps that provided real-time feedback, while the other
received traditional feedback from their teachers during regular classroom instruction. Pre- and post-
listening comprehension tests were administered, alongside learner surveys and teacher observations.
Results indicate that students in the AI feedback group showed faster improvements in self-
correction behaviors and developed greater awareness of listening strategies. They were more likely
to reattempt tasks and adjust their listening habits based on automated hints. Many reported
enjoying the independence and immediacy provided by AI tools, which motivated them to practice
more frequently. However, they also expressed occasional frustration when AI feedback was overly
mechanical, misinterpreted their input, or failed to explain errors clearly.
Teacher feedback group showed more gradual progress, but with deeper comprehension and better
integration of listening with speaking and grammar. Students appreciated the emotional support and
clarification provided by their instructors. They were also more likely to engage in peer discussions,
asking follow-up questions and benefiting from group feedback sessions. Learners in this group
tended to develop better meta-cognitive awareness about their listening difficulties and how to
address them holistically.
The comparative analysis suggests that while AI feedback enhances speed, repetition, and
individualized practice, teacher feedback fosters deeper understanding, emotional engagement, and
classroom interaction. Both forms of feedback have unique strengths, and their effectiveness is
influenced by the learner's autonomy, technological familiarity, and learning preferences. A blended
model that incorporates real-time AI feedback as a supplement to human instruction appears to offer
the most promising results.
For optimal outcomes, teachers must be trained to interpret AI data and use it to inform instruction.
Likewise, AI tools must evolve to offer more adaptive, empathetic, and context-aware feedback.
Policies and curriculum should support the integration of both feedback modes in a complementary
fashion, rather than seeing them as alternatives. As AI technologies become more advanced and
accessible, their role in language learning will expand, but the value of human guidance will remain
irreplaceable in the learning process.
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ILMIY TADQIQOTLAR VA ULARNING YECHIMLARI JURNALI
JOURNAL OF SCIENTIFIC RESEARCH AND THEIR SOLUTIONS
VOLUME 6, ISSUE 01, IYUL 2025
WORLDLY KNOWLEDGE NASHRIYOTI
worldlyjournals.com
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