Developing students’ feedback literacy in disciplinary academic writing through generative artificial intelligence

The incorporation of generative artificial intelligence (GenAI) has greatly transformed the feedback landscape. While there is a burgeoning scholarship on GenAI feedback, student feedback literacy in GenAI contexts receives relatively little attention. This study aimed to investigate the effects of a self-regulated learning (SRL)-based intervention on L2 university students’ feedback literacy in a GenAI-mediated writing environment. A quasi-experimental design was employed, involving an experimental group that received SRL-based instruction and a control group that received no such intervention. Data were collected from self-reported questionnaires and semi-structured interviews. The quantitative results revealed that the experimental group improved their feedback literacy in both cognitive and behavioural dimensions. Interview data further illuminated that the intervention enhanced students’ ability to interpret GenAI-generated feedback, develop more discerning evaluations of its usefulness, employ strategic prompting practices, critically appraise feedback before uptake, and refine revision strategies. In contrast, no significant differences were observed in the affective and ethical dimensions of feedback literacy, indicating the need for additional support beyond leveraging SRL. The present study advances our understanding of student feedback literacy in an increasingly GenAI-driven world. It also offers pedagogical implications for supporting students’ feedback literacy development, enabling them to meaningfully and ethically engage with GenAI feedback.

Despite the recognised importance of feedback in enhancing student learning, feedback practices in higher education have not achieved the expected effects. A primary issue lies in student disengagement, exacerbated by contextual constraints such as large classes and limited curriculum space and time. The advent of Generative Artificial Intelligence (GenAI) may help overcome these contextual constraints. However, GenAI also poses substantial challenges and ethical dilemmas during the feedback process. Meanwhile, it is essential to recognise that the feedback environment created by GenAI inevitably interacts with students’ personal factors, especially their feedback literacy, to jointly influence feedback engagement. Therefore, a question remains whether GenAI can be an effective enabler of student feedback engagement. To answer the question, based on a literature review and theoretical synthesis, we scrutinise student engagement with GenAI in three stages of the feedback process and discuss the interplay of student feedback literacy and the GenAI context. We suggest that the extent to which students are engaged with feedback depends on their degree of feedback literacy as orchestrated in the GenAI context. Finally, we propose a cyclical feedback framework consisting of feedback forethought, feedback control and feedback retrospect to enable student feedback engagement in a GenAI world.

Over the past decade, scholarly attention to how learners engage with feedback significantly grown within Second Language Acquisition (SLA) research. However, few studies have examined learner engagement with feedback in a multiple-interaction environment composed of generative artificial intelligence (GAI), peer, and teacher feedback. Grounded in the ecological affordance theory, the present study investigates the feedback engagement of eight non-English major undergraduates in a multiple-interaction environment within an EFL writing context. This study was carried out by analyzing written texts, questionnaires, stimulated recall interviews, and semi-structured interviews, with an emphasis on the behavioral, affective, and cognitive aspects of engagement. The findings reveal: (1) Three types of learner feedback engagement were identified: peer-teacher oriented type, GAI-teacher oriented type, and GAI-peer-teacher oriented type; (2) While correlations exist among the three dimensions of feedback engagement, discrepancies between cognitive engagement and affective or behavioral engagement were observed, particularly in GAI feedback stage; (3) GAI’s continuous mediation in some learners’ writing revision processes significantly influenced their engagement with the other two sources of feedback. These results offer both theoretical and practical implications for fostering students’ feedback literacy within intelligent education contexts and optimizing the design of multi-source feedback systems.

The integration of Generative Artificial Intelligence (GenAI) into L2 writing feedback has gained attention, yet its intersection with student feedback literacy development remains underexplored. This mixed-methods study addresses this gap by examining a 12-week GenAI-supported feedback practice involving 96 Chinese postgraduate students. A triadic pedagogical practice was employed, including preparatory scaffolding, diversified feedback sources (Kimi, Kimi-assisted peers, and teacher), and reflective reinforcement. Quantitative data were analyzed using repeated measures multivariate analysis of variance (RM-MANOVA), while qualitative interview data were examined through thematic analysis guided by a five-dimensional framework of feedback literacy. Three key findings emerged. First, the practice significantly enhanced students’ feedback literacy across four dimensions—appreciating feedback, acknowledging different feedback sources, managing affect, and taking action—yet showed limited impact on making judgments. Second, GenAI played multifaceted roles at two levels. At the practice level, it boosted student interest, enhanced motivation, fostered a sense of accomplishment in mastering a new tool, and strengthened responsibility in feedback participation. At the product level, Kimi and Kimi-assisted peer feedback enhanced students’ ability to recognize different feedback sources, manage emotions, and take action. Third, diminished awareness of assessment rubrics, sustained reliance on GenAI, and limited recognition of its limitations may amplify the tool’s negative effects, potentially hindering the development of evaluative judgment. This study advances understanding of technology-mediated feedback practices and contribute to pedagogical innovation in L2 writing education.

The advent of Generative Artificial Intelligence (GenAI) tools, particularly Large Language Models (LLMs) such as ChatGPT, is reshaping the landscape of academic writing, posing both opportunities and challenges for second language (L2) disciplinary academic writing instruction. While existing research has explored student perceptions of GenAI, little is known about how teachers perceive and integrate such technologies through the lens of their Teacher Feedback Literacy (TFL). This study addresses this gap by employing Q-methodology to systematically investigate the subjective perspectives of 27 L2 university instructors on the use of GPT in disciplinary academic writing contexts. Participants sorted 40 statements reflecting key dimensions of GPT use, feedback practices, and ethical considerations, followed by semi-structured interviews. The analysis revealed three distinct types: The Pragmatic GPT Optimist; The Critical Pedagogue with Ethical Foresight; and The Strategic and Policy-Focused Integrator. These perspectives illuminate how teachers reinterpret and expand their feedback literacy competencies in response to advanced GenAI, highlighting evolving needs for professional development and institutional policy. The findings contribute to a nuanced understanding of teacher agency in navigating the pedagogical, ethical, and practical dimensions of integrating GPT into L2 academic writing education.

Generative artificial intelligence (GenAI) raises pressing pedagogical and ethical questions in higher education. We surveyed 87 UK psychology students about GenAI familiarity, study uses, attitudes, and the justification of questionable uses (neutralisation). 54% reported using GenAI to assist their studies, primarily via ChatGPT. Compared with non‐users, study users showed more positive AI attitudes and higher neutralisation scores. Across the full sample, AI attitudes modestly predicted neutralisation. The most common study uses were explaining concepts and generating ideas, and most users intended to use GenAI again. Non‐users were more likely to endorse restrictive views on GenAI in assessed work. Findings point to a tension between perceived learning value and risks of dependency and academic integrity. Students also reported a need for clearer institutional guidance. We recommend a balanced approach that supports responsible use, feedback literacy, and critical engagement with AI outputs, alongside continued student‐centred research to inform policy and assessment design.

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

Purpose This study aims to critically examine the socio-technical, economic and governance challenges emerging at the intersection of Generative artificial intelligence (AI) and Urban AI. By foregrounding the metaphor of “the moon and the ghetto” (Nelson, 1977, 2011), the issue invites contributions that interrogate the gap between technological capability and institutional justice. The purpose is to foster a multidisciplinary dialogue–spanning applied economics, public policy, AI ethics and urban governance – that can inform trustworthy, inclusive and democratically grounded AI practices. Contributors are encouraged to explore not just what GenAI can do, but for whom, how and with what consequences. Design/methodology/approach This study draws upon interdisciplinary literature from public policy, innovation studies, digital governance and urban sociology to frame the emerging governance challenges of Generative AI and Urban AI. It builds a conceptual foundation by synthesizing insights from comparative city case studies, innovation systems theory and normative policy frameworks. The approach is interpretive and exploratory, aiming to situate AI technologies within broader institutional, geopolitical and socio-economic contexts. The study invites contributions that adopt empirical, theoretical or practice-based methodologies addressing the governance of GenAI in cities and regions. Findings This study identifies a critical gap between the rapid technological advancements in Generative AI and the institutional readiness of public governance systems – particularly in urban contexts. It finds that current policy frameworks often prioritize efficiency and innovationism over democratic legitimacy, civic trust and inclusive design. Drawing on comparative global city experiences, it highlights the risk of reinforcing power asymmetries without robust accountability mechanisms. The analysis suggests that trustworthy AI is not a purely technical attribute but a political and institutional achievement, requiring participatory governance architectures and innovation systems grounded in public value and civic engagement. Research limitations/implications As an editorial introduction, this study does not present original empirical data but synthesizes key theoretical frameworks, case studies and policy debates to guide future research. Its analytical scope is conceptual and comparative, offering a foundation for submissions that further investigate Generative and Urban AI through empirical, normative and practice-based lenses. The limitations lie in its broad coverage and reliance on secondary sources. Nonetheless, it provides an agenda-setting contribution by highlighting the urgent need for interdisciplinary research into how AI reshapes public governance, institutional legitimacy and urban democratic futures. Practical implications This editorial offers a structured framework for policymakers, urban planners, technologists and public administrators to critically assess the governance of Generative and Urban AI systems. By highlighting international case studies and conceptual tools – such as public algorithmic infrastructures, civic trust frameworks and anticipatory governance – the article underscores the importance of institutional design, regulatory foresight and civic engagement. It invites practitioners to shift from techno-solutionist approaches toward inclusive, democratic and place-based AI governance. The reflections aim to support the development of trustworthy AI policies that are grounded in legitimacy, accountability and societal needs, particularly in urban and regional contexts. Social implications The editorial underscores that Generative and Urban AI systems are not socially neutral but carry significant implications for equity, representation and democratic legitimacy. These technologies risk reinforcing existing social hierarchies and systemic biases if not governed inclusively. This study calls for reimagining trust not as a technical feature but as a relational, contested dynamic between institutions and citizens. It encourages submissions that examine how AI reshapes the urban social contract, affects marginalized communities and challenges existing civic infrastructures. The goal is to promote AI governance frameworks that are pluralistic, just and reflective of diverse societal values and lived experiences. Originality/value This editorial offers a timely and conceptually grounded intervention into the emerging field of Urban AI and Generative AI governance. By framing the challenges through Richard R. Nelson’s metaphor of The Moon and the Ghetto, this study foregrounds the gap between technical capabilities and enduring societal injustices. The contribution lies in its interdisciplinary synthesis – bridging innovation systems, AI ethics, public policy and urban governance. It introduces a critical framework for assessing “trustworthy AI” not as a technical goal but as a democratic achievement and encourages research that is policy-relevant, equity-oriented and attuned to the institutional realities of AI in cities.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

This paper examines how artificial intelligence (AI) tools enhance feedback practices in doctoral education by providing a supplementary source of formative assessment. It explores the use of Generative AI alongside Grainger’s Formative Assessment Criteria-Based Tool (F.A.C.T.) in a single-subject case study of a pre-confirmation doctoral candidate at an Australian university. The study employs a naturalistic and interpretive approach with a sequential design, exploring interactions between a doctoral student and ChatGPT across multiple sessions where the AI tool evaluated a pre-confirmation thesis. Data collection included deidentified summarised feedback received from AI and an independent academic reviewer. Findings revealed that AI-generated feedback, guided by Grainger’s F.A.C.T. demonstrated thematic alignment with a human reviewer in identifying areas needing improvement, particularly regarding theoretical foundation and contribution to knowledge. However, the human reviewer provided contextually nuanced and discipline-specific feedback with more actionable suggestions. The study illustrates how, when coupled with formative rubrics, generative AI may serve as a supplementary feedback mechanism that may reduce power imbalances inherent in supervisor/reviewer-student relationships while providing expedient formative feedback. This research contributes to understanding how reflective practice in doctoral education may be enhanced through AI integration, addressing gaps in feedback literacy, socialisation, and standardised assessment parameters in doctoral contexts.

Effective learning depends on effective feedback, which in turn requires a set of skills, dispositions and practices on the part of both students and teachers which have been termed feedback literacy. A previously published teacher feedback literacy competency framework has identified what is needed by teachers to implement feedback well. While this framework refers in broad terms to the potential uses of educational technologies, it does not examine in detail the new possibilities of automated feedback (AF) tools, especially those that are open by offering varying degrees of transparency and control to teachers. Using analytics and artificial intelligence, open AF tools permit automated processing and feedback with a speed, precision and scale that exceeds that of humans. This raises important questions about how human and machine feedback can be combined optimally and what is now required of teachers to use such tools skillfully. The paper addresses two research questions: Which teacher feedback competencies are necessary for the skilled use of open AF tools? and What does the skilled use of open AF tools add to our conceptions of teacher feedback competencies? We conduct an analysis of published evidence concerning teachers’ use of open AF tools through the lens of teacher feedback literacy, which produces summary matrices revealing relative strengths and weaknesses in the literature, and the relevance of the feedback literacy framework. We conclude firstly, that when used effectively, open AF tools exercise a range of teacher feedback competencies. The paper thus offers a detailed account of the nature of teachers’ feedback literacy practices within this context. Secondly, this analysis reveals gaps in the literature, signalling opportunities for future work. Thirdly, we propose several examples of automated feedback literacy, that is, distinctive teacher competencies linked to the skilled use of open AF tools.

BackgroundCourses in engineering often use peer evaluation to monitor teamwork behaviors and team dynamics. The qualitative peer comments written for peer evaluations hold potential as a valuable source of formative feedback for students, yet little is known about their content and quality.PurposeThis study uses a large language model (LLM) to apply a previously tested feedback quality rubric to peer feedback comments. Our research questions interrogate the reliability of LLMs for qualitative analysis with a rubric and use Bandura's self‐regulated learning theory to assess peer feedback quality of first‐year engineering students' comments.MethodAn open‐source, local LLM was used to score each comment according to four rubric criteria. Inter‐rater reliability (IRR) with human raters using Cohen's quadratic weighted kappa was the primary metric of reliability. Our assessment of peer feedback quality utilized descriptive statistics.ResultsThe LLM achieved lower IRR than human raters, but the model's challenges mimic those of human raters. The model did achieve an excellent quadratic weighted kappa of 0.80 for one rubric criterion, which shows promise for LLM capability. For feedback quality, students generally wrote low‐ to medium‐quality comments that were infrequently grounded in specific teamwork behaviors. We identified five types of peer feedback that inform how students perceive the feedback process.ConclusionsOur implementation of GAI suggests that LLMs can be helpful for rapid iteration of research designs, but consistent and reliable analysis with generative artificial intelligence (GAI) requires significant effort and testing. To develop feedback literacy, students must understand how to provide high‐quality feedback.

Feedback seeking research envisages pro-active student roles in feedback processes but students seem to hesitate to seek feedback from their teachers despite the potential benefits it offers. Appreciating variation in students’ experiences of feedback seeking is crucial for understanding this issue. This phenomenographic interview-based research investigated variation in the experiences of 24 undergraduate students regarding feedback seeking. An outcome space of five categories was developed: (1) feedback seeking as unnecessary, (2) feedback seeking through monitoring, (3) feedback seeking as impression management, (4) feedback seeking for academic achievement and (5) feedback seeking for broader learning. Broader significance emerges through charting interplay between the mutually reinforcing concepts of feedback seeking and feedback literacy, suggesting benefits of enabling students to appreciate the value of feedback seeking when transitioning to higher education. Surfacing some of the negative views of feedback seeking expressed by students enables us to propose some teaching and learning approaches to reduce their concerns. These implications for practice include developing curriculum-wide opportunities for sustained feedback seeking; establishing psychologically safe environments for feedback seeking to flourish; and designing complex iterative assessments that encourage feedback seeking and uptake. Future possibilities for students to seek feedback from generative artificial intelligence are briefly sketched.

Feedback during formative assessment is a powerful driver of student learning and educational change, yet its potential is often underutilised in higher education (HE) practice.The level of feedback literacies among students remains a barrier (Carless and Boud, 2018).The role of the teacher in the feedback process and the development of their feedback literacy has been underexplored (Carless and Winstone, 2020).Formative assessment and feedback in post-digital learning environments offers some insightful contributions to our understanding about formative assessment and feedback practice, and the changing educational landscape provides the editors with a compelling justification to revisit this topic.The focus on the postdigital learning environment is fitting given the impact digital technologies, including generative artificial intelligence (GenAI), have had on HE and the complexities and contradictions they bring as both enablers and barriers.The postdigital framing offers a useful lens for this international collection of case studies that explore formative assessment and feedback from a range of disciplinary perspectives.This edited collection will be of interest to educators in HE who want to better understand the ways digital technologies might influence assessment and feedback practices.The editors provide a helpful introduction to postdigital education citing authors who have contributed to postdigital thinking to date.

The integration of generative artificial intelligence (AI) into writing instruction has fundamentally reconfigured feedback practices in higher education. This change is particularly obvious in Business English writing since it is highly dependent on context, norms and phramatic constraints. While existing research has largely emphasized technological affordances and efficiency gains of AI-assisted feedback, it often overlooks the epistemic limits of AI and the pedagogical mechanisms necessary for its effective use. This paper develops a teacher-scaffolded framework of AI feedback literacy in Business English writing. Drawing on feedback literacy theory and sociocultural scaffolding theory, it believes that AI feedback literacy is not a single technical ability, but a multi-dimensional evaluation capacity, including epistemic literacy, pragmatic literacy, and strategic literacy. At the same time, teacher scaffolding is regarded as a key mediating mechanism to guide learners to critically use AI feedback instead of accepting it as an absolute authority. In addition, a process model is proposed featuring a recursive sequence of epistemic positioning, critical mediation, and pragmatic adaptation. By reconceptualizing the relationship among teachers, learners, and AI, this paper extends feedback literacy theory in the AI environment and provides a theoretical basis for future empirical investigation in professional discourse.