The rapid expansion of generative artificial intelligence is transforming writing practices across educational systems worldwide. In higher education, students increasingly encounter feedback mediated through AI-based tools alongside established peer and teacher feedback practices. These developments have introduced new pedagogical tensions, particularly regarding how feedback is interpreted, evaluated and integrated into revision decisions. While much existing research has examined the technical features or perceived usefulness of AI tools, less attention has been paid to how feedback practices are experienced and negotiated as part of learners’ ongoing educational development.

These challenges are especially salient in second language (L2) writing contexts. L2 writers often rely heavily on feedback to support linguistic accuracy, academic conventions and genre expectations, making feedback a central site of learning. As AI-mediated feedback becomes embedded within L2 writing environments, learners must navigate competing sources of evaluative input, raising questions about authorship, responsibility and the formation of judgement. Such issues are not confined to a single educational system but reflect globally shared concerns in language education.

For pre-service language teachers, engagement with feedback carries additional significance. As learners, they are required to make revision decisions within AI-mediated writing tasks; as future teachers, they are simultaneously developing professional understandings of assessment, evaluation and ethical practice. Learning to use feedback therefore becomes intertwined with learning to judge. Understanding how pre-service teachers interpret and negotiate multiple feedback sources is thus central to contemporary debates on teacher professional learning in digitally mediated contexts.

This study examines these issues through an interpretive inquiry conducted with pre-service English teachers in a Chinese university setting. China represents one of the largest second language education systems globally, where academic writing is shaped by strong assessment norms and expectations of linguistic accuracy. These characteristics render the context analytically valuable for examining how emerging feedback practices are negotiated under conditions of high evaluative pressure. Rather than positioning the site as exceptional, the study treats it as an illustrative case through which globally shared pedagogical tensions can be explored.

Conceptually, the study draws on literature on feedback literacy, evaluative judgement and professional learning. Feedback is understood not as information transmission but as a situated practice through which learners interpret criteria, negotiate meanings and construct responsibility for their learning decisions. From this perspective, AI functions as a mediating artefact within a broader multi-source feedback ecology, interacting with peer and teacher feedback across drafting stages. Professional learning is conceptualised as an interpretive process shaped by engagement with tools, relationships and institutional expectations.From this perspective, learning to judge is not merely an individual cognitive skill but a socially and institutionally situated professional practice.

Guided by this framework, the study addresses the following research questions:

1. How do pre-service English teachers engage with multiple feedback sources, including AI-mediated, peer and teacher feedback, across iterative writing tasks?

2. How do pre-service teachers interpret, compare and exercise judgement in prioritising feedback when making revision decisions in AI-mediated writing contexts?

3. What conditions shape pre-service teachers’ developing professional judgement and sense of responsibility in feedback-mediated learning environments?

By foregrounding professional learning processes rather than technological effectiveness, this study contributes to international discussions on how emerging technologies reshape educational practices. The findings aim to offer insights for teacher education by highlighting the importance of pedagogical designs that support judgement formation, reflective comparison and ethical deliberation in AI-mediated writing contexts.

This study adopts an interpretivist qualitative approach, viewing professional learning as a situated and meaning-making process shaped through participants’ engagement with feedback practices. Rather than examining the effectiveness of AI tools, the study focuses on how pre-service teachers interpret, compare and prioritise feedback as part of their developing professional judgement. The research was conducted within an academic writing module for pre-service English teachers at a Chinese university. The module required students to engage with multiple feedback sources, including AI-mediated feedback, peer comments and teacher input, across successive drafting tasks. This setting provided an analytically rich context for examining how revision decisions were negotiated under conditions of competing evaluative perspectives. Five pre-service teachers participated in the study. All participants were preparing for future teaching roles and had prior experience with English academic writing. Their dual positioning as learners and prospective teachers enabled exploration of feedback engagement as both a learning activity and an emerging professional practice. Data were generated through two complementary sources: reflective journals and semi-structured interviews. Reflective journals were completed throughout the module and captured participants’ ongoing reflections on feedback use, challenges encountered during revision and evolving understandings of responsibility and authorship. Semi-structured interviews were designed to foreground decision-making processes during revision. This design allowed direct alignment with the study’s focus on how evaluative judgement is formed and enacted in feedback-mediated learning. During the interviews, participants revisited selected excerpts from their written drafts alongside feedback received from different sources. These textual artefacts were used to support participants’ reflection on how they interpreted feedback, compared alternative suggestions and explained why particular feedback was accepted, adapted or rejected. This artefact-supported reflective approach enabled access to participants’ evaluative reasoning, moving beyond general attitudes toward feedback. Data analysis followed reflexive thematic analysis. Initial coding focused on participants’ accounts of feedback interpretation, comparison and prioritisation. Through iterative cycles of coding, theme development and analytic memo-writing, patterns were identified in how professional judgement was enacted and how contextual conditions shaped these processes. The analysis aimed to develop a theoretically informed and context-sensitive understanding of professional learning within AI-mediated feedback environments rather than to produce generalisable claims.

This study is expected to generate nuanced insights into how pre-service teachers engage with multiple feedback sources within AI-mediated writing environments. Rather than treating feedback use as a linear or tool-driven process, the findings are anticipated to illuminate how participants actively interpret, compare and prioritise feedback suggestions across drafting stages. Such insights may deepen understanding of feedback engagement as a dynamic and judgement-oriented learning process. At a conceptual level, the study aims to contribute to discussions of professional learning by foregrounding evaluative judgement as an emergent practice developed through engagement with competing perspectives. By examining how pre-service teachers negotiate responsibility, authorship and decision-making in revision, the research is expected to extend existing work on feedback literacy beyond reception and uptake toward the formation of professional reasoning. This perspective aligns with broader debates in teacher education concerning how pedagogical judgement develops prior to formal classroom practice. The study also seeks to inform current discussions surrounding artificial intelligence in education. Instead of positioning AI as an instructional solution or as a threat to academic integrity, the findings are expected to highlight its role as a mediating artefact within a broader feedback ecology. This framing may offer a more balanced understanding of AI integration by emphasising learners’ agency and interpretive work rather than technological capability alone. From a pedagogical perspective, the study is expected to offer implications for the design of teacher education programmes. In particular, it may suggest the importance of creating structured opportunities for reflective comparison between feedback sources, supporting learners in articulating evaluative criteria and fostering ethical awareness in AI-mediated writing practices. These insights speak to broader international debates in teacher education concerning how emerging technologies can be integrated without undermining professional judgement, responsibility and pedagogical agency. They may therefore support teacher educators in designing learning environments that prioritise the development of judgement and responsible engagement with emerging technologies.

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