Stop perfecting the feedback, start supporting the uptake: rethinking AI in writing instruction

This article discusses the use of text-generating AI applications for providing feedback on students' texts and to help them revise their writing. While feedback through applications based on generative AI (for example, ChatGPT or specific tools such as Writing Coach, Writeable, and others) is often evaluated in terms of quality, even in comparison to feedback from human teachers (Mah et al., 2025;Seßler et al., 2025;Steiss et al., 2024), it is often overlooked that the most important thing is for learners to use and process the feedback to revise their texts (e.g. Lipnevich Rong et al., 2025). AI appears to have little or no impact on this initial situation, which is what this article aims to discuss. To this end, it is divided into three parts: The first step is to present the potential of genAI for providing feedback on learner texts and to highlight the problem that many learners in elementary and secondary schools do not meaningfully engage with AI-generated feedback for revision. In a second step, we will discuss how this may be due not only to the way AI works, but also to the unfavourable integration of feedback into teaching and learning processes. To provide an alternative, the third step will outline a teaching model that aims to achieve effective integration.Feedback is widely recognized as one of the most powerful tools for supporting learner development in writing (Hattie MacArthur, 2016;Wisniewski et al., 2020). This holds true for learning to write in the context of L1 acquisition as well as in the context of L2-writing (Hyland, 2016;Hyland Gibbs Nikolopoulou, 2025). Chatbots such as ChatGPT, LeChat, and Gemini, as well as specialized tools like Flint, Writeable, or the Khan Academy's Writing Coach, now deliver instant feedback that numerous studies have shown to resemble human feedback in terms of how it is rated and assessed by researchers (Almegren et al., 2025;Steiss et al., 2024;Usher, 2025). GenAI thus seems to offer teachers a means of transforming feedback from a timeconsuming burden into a more manageable and scalable practice. However, AI-based feedback on texts has conceptual limitations from a writing education perspective. Firstly, the systems are hardly capable of providing feedback on the writing process: Although some AI applications (e.g., Khan Academy's Writing Coach) are able to provide feedback on ideas and drafts, this feedback can only be provided after these texts or text fragments have been entered into the input mask (and submitted) and not during the writing process itself. In other words, the systems are unable to provide feedback on a paragraph, sentence, or word that has just been started while writing. This is particularly problematic given that formative feedback during the writing process has been shown to be crucial for learner development (Graham et al., 2011(Graham et al., , 2015)). Mekheimer (2025) also notes that providing extensive feedback "all at once" at the end of the writing process is suboptimal based on cognitive load theory. Secondly, despite offering high levels of personalisation, AIbased feedback applications may lack depth, logical coherence, and relevance with regard to the learner's text (Elmotri et al., 2025;Venter et al., 2025). It is not uncommon for such applications to focus on the structure of the text, particularly the introduction (e.g. Writing Coach), even when the actual underlying issue in the learner's text is a lack of reader orientation throughout. Individualisation therefore often means measuring all learners in a personalised way while always using "the same yardstick". Thirdly, it is important to remember that AI-based feedback systems (and AI systems in general) only consider the text entered and offer direct solutions on how to improve this specific text. They do not provide any feedback on how writing in general could be improved. This contradicts the goals of learning to write, which also include, for example, identifying problem areas in one's own text and finding alternatives (by oneself) before implementing them (Bereiter Hayes Jantzen, 2003).While these constraints are important, we would like to turn our attention to what is probably an even more pressing issue: the uptake of AI-generated feedback by learners when revising their writing. Modern feedback research has long pointed out that, in addition to the source of the feedback and the feedback message, the cognitive, behavioural, and affective processing of feedback are also relevant factors -as illustrated, for example, in the Student-Feedback Interaction Model (Lipnevich Yu Wrede et al., 2023) is immediately negated if learners do not engage with the feedback. When attempting to identify why learners do not take up AI-generated feedback during revision, it would be reasonable to consider potential differences between human and AI feedback. Mah et al. (2025), for example, observe that AI and human teachers may focus on different aspects of a text, providing feedback at disparate levels of text (e.g., primarily at the sentence level in the case of AI). We, however, suspect that neither the (differences in the) quality nor the (technical) functionality of AI-generated feedback alone is responsible for its lack of use by learners, but rather an unfavourable embedding of AI-generated feedback in teaching and the learning processes. The reason for this assumption is that the lack of use of human feedback for text revision, if not properly embedded in teaching, has been recognised for a long time, even before the advent of AI (MacDonald, 1991;Sinclair Busse et al., 2022).Finally, bringing these discussions back to the whole class (phase 5) enables collective reflection on the quality, appropriateness, and limitations of AI-generated comments. In such a model, the teacher takes on a new role: not as the ultimate judge of text quality, but as a facilitator who helps learners interpret feedback, identify mismatches, and refine their revisions accordingly. This leads to a shift in the teaching dynamic: the teacher is no longer responsible for providing feedback themselves, but they still maintain authority in that they can disagree with or question the AI feedback. After these stages, students should again revise their texts to integrate insights gained from the process.It should be noted that not every writing assignment lends itself to such a multi-stage approach; especially when the texts to be written have clearly defined (and traditional) structures (e.g. argumentative essays), the pattern-oriented approach of AI can provide valuable support in the feedback process. Secondly, the age of the learners must be taken into account. Understanding AI-based feedback alone (Phase 2) places considerable demands on reading skills. In the context of primary school, teacher mediation would certainly be necessary.The position we have outlined in this article has clear implications for AI-oriented research.We propose that, when it comes to research in writing, equal consideration must be given both to the quality of AI feedback and to how AI feedback is embedded in writing instruction.Rather than merely assessing differences in the quality and quantity of human and AI feedback, or the quality of AI feedback across models, tasks or prompts, empirical studies should also be designed to identify characteristics of learning settings in which feedback on a written text leads to productive revision. Studies should also be designed to identify characteristics of learning settings in which feedback on a written text, whether AI-based or not, leads to productive revision (uptake). Examining and identifying the prerequisites for feedback uptake and the goals that teachers and learners associate with it, as well as the extent to which these influence uptake, are key to providing teachers with specific guidance on designing effective learning scenarios. So too will investigating the extent to which a tailormade educational setting can increase uptake. These questions remain relevant even without AI-based feedback. However, they become even more pertinent when AI facilitates feedback, potentially reintroducing it into everyday teaching. Against this backdrop, we should also take advantage of the current focus on AI feedback to address important research questions independent of it (Jensen et al., 2024), particularly those concerning the conditions for effective writing feedback.