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 & Smith, 2022). However, when considering learning situations in elementary and secondary schools, it appears that if text revision takes place at all, it is rarely in a form where previously provided feedback guides the revision (Jansen et al., 2025;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 & Timperley, 2007;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 & Hyland, 2019). However, for feedback to be effective, it should consist of an internal structure of feed up (Where am I going?), feed back (How am I going?), and feed forward (Where to next?), and be relevant to the learners and given in a timely manner (Brandmo & Gamlem, 2025;Gibbs & Simpson, 2004).Yet, providing feedback that is both timely and targeted remains a significant challenge, especially when teachers are faced with lengthy student texts and larger heterogeneous learning groups (Applebee & Langer, 2011). The public availability of AI could transform this area and take some of the burden of providing feedback off the shoulders of teachers (e.g. Kolade et al., 2024;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,…