INTEGRATING AI FEEDBACK LOOPS IN ACADEMIC READING INSTRUCTION: PEDAGOGICAL OPPORTUNITIES AND ETHICAL CHALLENGES

Аннотация:

This article examines the integration of AI feedback loops into academic reading instruction, highlighting both their pedagogical benefits and ethical implications. As intelligent systems increasingly assist learners through adaptive feedback, summarization, and comprehension analysis, educators face new opportunities to enhance reading engagement, self-regulation, and critical awareness. The study investigates how AI-generated feedback—delivered through tools such as ChatGPT, Elicit, and Perplexity—can promote metacognitive reflection, formative assessment, and individualized reading support. Drawing upon socio-constructivist and feedback literacy frameworks, the research emphasizes the role of iterative AI-human interaction in fostering deeper textual comprehension. However, it also addresses ethical concerns such as data privacy, algorithmic bias, intellectual dependency, and the diminishing of authentic interpretive skills. Findings suggest that AI feedback loops, when thoughtfully embedded within pedagogically guided environments, can enhance learner autonomy, motivation, and comprehension outcomes. The paper concludes with recommendations for balancing technological innovation with ethical responsibility, proposing a reflective model for educators to critically evaluate AI’s role in academic literacy development.