Feedback-Driven Language Instruction

Ensuring Students Act on Feedback Feedback is only as valuable as the action students take in response to it. Too often, feedback becomes a passive exchange,teachers give comments, students glance at them, and then move on to the next task without making meaningful improvements. To truly accelerate progress, we need to create structures that ensure feedback leads to independent development. Here’s how: 1. Build Dedicated Feedback Lessons into Your Scheme of Work If feedback is to be effective, there must be time for students to engage with it properly. This means moving beyond a quick ‘read your comments’ approach and embedding dedicated feedback lessons into the scheme of work. By protecting this time within the curriculum, feedback becomes a continuous, structured process rather than an afterthought. 2. Use Targeted and Specific Feedback Vague comments like ‘be more analytical’ or ‘develop your explanation’ don’t give students a clear direction. Instead, feedback should be precise and actionable. For example: • Before: ‘Your analysis is weak.’ • After: ‘To strengthen your analysis, explain why this event was significant and link it to a wider consequence.’ Or Pose questions to help students develop their answer or guide them to the correct knowledge. Pairing feedback with examples or sentence starters can help students apply improvements more effectively. 3. Teach Students How to Use Feedback Students need to be explicitly taught how to engage with feedback. This includes: • Modelling the process – Show students how to act on feedback by walking them through a worked example. • Guiding self-reflection – Use prompts like, ‘How does my answer compare to the model? Where can I improve?’ • Encouraging peer support – Structured peer review can help students identify strengths and areas for development before teacher intervention. I often like to highlight a weak paragraph in a green box so students know what area to precisely improve/re-write, as you can see below. 4. Use Feedback Trackers to Monitor Progress Instead of feedback disappearing into exercise books, encourage students to keep a feedback tracker where they record teacher comments and their own reflections. They can then set targets for the next piece of work and review previous feedback to ensure they’re improving over time. Feedback is most powerful when it becomes part of the learning process, not just an add-on. By allocating time in the curriculum for feedback lessons, making guidance explicit, and encouraging students to take ownership, we can transform feedback from words on a page into meaningful improvement. The ultimate goal? Students who no longer just receive feedback, but actively use it to progress.

I watched a teacher with a group of leaders the other day give nearly every student feedback as she circulated. Eager to see how much better students’ writing was going to be after all that feedback, we walked around the room expecting to see big improvements. From student to student, their work looked exactly the same as it had before the feedback. What gives? So we listened more closely to the feedback she was giving: “You’re missing key details. Go back and revise.” We watched as a confused 7th grader flipped back through the text, unsure where to start. So we helped in the moment by having the teacher adjust her feedback: “You’ve written strong topic sentences for your two body paragraphs—nice work. Your second paragraph is missing two key details, like we named in the criteria for success. Re-read page 71 and find at least one detail to add. I’ll come back in five minutes to check the detail you added.” What happened next? Students’ writing actually improved, and here's the added bonus: so did their connection with the teacher. Instead of feeling frustrated or stuck, they were eager to show her their revisions. That formula — affirm the effort, name the gap, name the fix, and plan the follow-up — is one worth practicing, especially if you have 32 other students you need to give feedback to like she did. When teachers use it, student work gets better, and so do relationships.

Can students judge like experts? New research challenges assumptions about AI feedback in education. A new large scale study (Nazaretsky, Gabbay & Käser 2026) compared AI-generated and human-crafted feedback for 472 STEM students. Here is what they found: 📊 Quality is comparable. AI-generated feedback matched human-authored feedback in pedagogical quality. Both had strengths, and both had gaps, particularly around metacognitive guidance. 🧠 Perception ≠ reality. Students' evaluations of feedback quality were driven more by who they thought provided it than by the feedback's actual merit. This held across academic levels, genders and fields of study. 📋 A new standard emerges. The researchers introduced a structured rubric for assessing formative feedback quality, addressing a real gap in how we evaluate AI tools in education. ⚡ Key reflection. We need to help students become better evaluators of the feedback they receive regardless of its source. Feedback is only as effective as a learner's ability to use it. So how do we do that? Here are some ideas: ➜ Demonstrate the different levels feedback can operate at. Show them: task-level feedback says "this is wrong." Process-level feedback says "your approach broke down here, try this strategy." Self-regulation feedback says "before starting problems like this, estimate the answer first to catch errors early.” ➜ Encourage and scaffold structured self-questioning when students are in the planning and process phases of completing tasks. ➜ Have students rate feedback on metacognitive criteria. Don't ask "was this helpful?" Ask: Did it help me understand why I made the error?” ➜ Compare feedback examples. Show two pieces of feedback on the same work: one purely corrective, one with metacognitive guidance. ➜ Model metacognitive evaluation out loud. Teachers modelling their own thinking and self-talk to demonstrate metacognitive strategies helps students see how to evaluate feedback critically. Any other suggestions?

One of the ways I'm incorporating #AI in the feedback loop for students in my writing class is to use it as a guide for talking points when they go to the language lab for support. I told students I would be using Brisk Teaching for round 1 (maintaining transparency about when I'm using AI and hopefully leading by example), where it creates feedback points based on my rubric and inserts them in a table at the top of their essay. Using Google Docs, I converted the bullet points to checkboxes (though it would be nice if Brisk did this part automatically), so students can go through point by point and show me that they're at the very least looking at the feedback before the next round of writing. Next, I asked students to highlight one point from each category and use the comment feature to speak to it. This could be any variation of responses: 🔦 Spotlighting an issue that they know they need to work on and how they're dealing with it in this paper 🙅♀️ Disagreeing with the AI and explaining why they don't want to make the change it's suggesting ❓ Asking for clarification on how to respond to a point ➕ Etc. Next, when they go to the lab to get help, these highlights and the changes they made will form the foundation for the talking points when they work with the professor. One of the biggest problems when students go to a lab for support is always training them to be prepared instead of going in and saying "please check my paper" rather than empowered with a specific learning goal in mind. So the goal here is to have them go in with 5 already acted upon (or at least considered) points to discuss in order to make a more productive lab time. The screenshot is a sample that I sent to my students to understand the concept. I'm sure there will be some fine-tuning, but already many of them are interacting more with their early drafts and even coming to me to make sure that they're building good responses to talk to the professor in the lab about. I'll need more exploration, but to me this is a good way to take advantage of the strengths of AI, continue to challenge students to think critically about what it generates, and wrap it all in a human-centered approach focused on student learning rather than just using a shiny toy for the sake of it. #AIinESL #ArtificialIntelligence #TESOL #TESL #TESOL #ELT #LanguageLearning #Composition #StudentSuccess

After analyzing 51 studies on AI feedback for second language writers, I can tell you the reality is messier—and more interesting—than the hype suggests. When I started this systematic review, I expected to find clear answers about whether ChatGPT could effectively give writing feedback to L2 learners. Instead, I found a field wrestling with fundamental questions about what good feedback actually looks like. The most striking finding? AI + human feedback consistently outperformed either approach alone. We're not looking at replacement—we're looking at augmentation. Students improved most when they received both AI corrections on grammar/vocabulary AND teacher guidance on content and organization. But here's what surprised me: students readily incorporated AI suggestions about language form but largely ignored content feedback from AI. This isn't about trust—it reveals something deeper about how L2 writers process different types of input. Form-focused feedback feels actionable and "safe." Content feedback from AI? Not so much. The methodological gaps were eye-opening. Only 27 of 51 studies shared their ChatGPT prompts—imagine trying to replicate a chemistry experiment without knowing the formula. And 46 out of 51 studies focused solely on English L2 writing, mostly from East Asian contexts. We're making broad claims based on remarkably narrow evidence. For language educators, this suggests a clear path forward: leverage AI for what it does well (catching linguistic errors, providing immediate feedback) while maintaining human expertise for higher-order concerns like argumentation, voice, and cultural appropriateness. The technology is moving faster than our understanding of how to use it effectively. That's both the challenge and the opportunity. What's been your experience combining AI and human feedback in language teaching? Are you seeing similar patterns with your students? Paper with these findings here - https://lnkd.in/g2JCnEss

🎯 Monitoring & Feedback: The Heart of Effective Classroom Interaction In every successful classroom, two powerful teaching tools quietly shape learning: monitoring and feedback. They may look simple , walking around the classroom, asking questions, writing on the board but when used purposefully, they transform learning. 👀 What is Monitoring? Monitoring is more than just observing. It means: 🔎 Watching and listening carefully while learners work 🚶 Moving around instead of staying at the front 🎯 Identifying difficulties and common errors 📊 Tracking learners’ progress 🤝 Showing learners you are present and interested When teachers move among learners, they create a more dynamic learning environment not a static one. Sensitive monitoring makes teachers appear more approachable, supportive, and engaged. 🌱 Why Monitoring Matters Effective monitoring: ✨ Builds rapport ✨ Keeps learners on task ✨ Increases engagement ✨ Provides individual attention ✨ Prepares the teacher for meaningful feedback Learners feel supported when they know their teacher is actively interested in their work. 💬 Giving Feedback: More Than Correction Monitoring and feedback go hand in hand. What we observe during monitoring guides the type of feedback we give. Here are six powerful feedback techniques: 1️⃣ 🤝 Peer Correction ✔ Encourages collaboration ✔ Develops critical thinking ⚠ Needs training to avoid over-criticism 2️⃣ 🔁 Learner Review (Self-Review) ✔ Promotes autonomy ✔ Reduces teacher workload ⚠ Requires clear guidance and criteria 3️⃣ 🧍 Individual Language Feedback ✔ Provides direction ✔ Personalised support ⚠ Challenging in large classes 4️⃣ 🏫 Class Language Feedback ✔ Quick and efficient ✔ Highlights common errors ⚠ May not apply to everyone 5️⃣ 🔎 Self-Correction ✔ Builds independence ✔ Deepens learning ⚠ Must be checked 6️⃣ 💡 Feedback on Content ✔ Values creativity ✔ Builds confidence 🔑 Key rule: Always comment on content first, then language. 🧩 Staging Feedback for Greater Impact Effective feedback is not random. It follows clear micro-stages: 1️⃣ Recap the previous activity 2️⃣ Ask specific questions 3️⃣ Write key answers on the board 4️⃣ Elicit a model sentence 5️⃣ Set up the next task Breaking feedback into smaller stages makes it: ✔ Clearer ✔ More constructive ✔ Less overwhelming ✔ More motivating 🏆 What Strong Teachers Do During Feedback ✔ Keep a brisk pace ✔ Don’t always ask the same learners ✔ Highlight good examples ✔ Correct common errors ✔ Praise effort and improvement ✔ Ask for personal responses Feedback isn’t just about mistakes — it’s about growth. 🌟 Final Reflection Monitoring is not simply walking around. Feedback is not simply correcting. When done intentionally, they: ✨ Build confidence ✨ Encourage autonomy ✨ Strengthen classroom culture ✨ Support meaningful learning #ClassroomInteraction #TeachingStrategies #PrimaryEducation #ELT #TeacherDevelopment #AssessmentForLearning #StudentEngagement #EducationLeadership

Speed vs. Sensemaking: The Case for Human–AI Feedback Teams 🚀A randomized, blinded field experiment in a higher-education course compared teacher, peer, and LLM feedback on students’ perceptions and on objective improvements after revision, while controlling for learning from giving peer feedback. 🌎#Human #and #AI #feedback #are #not #the #same: teacher feedback yielded the strongest gains in scientific argumentation (and outperformed LLM on formal quality), whereas students perceived teacher feedback as less fair/acceptable than peer or LLM feedback, and LLM produced the smallest overall improvements. --> Use teacher feedback when higher-order reasoning, nuanced judgment, and discipline-specific guidance are needed; use AI and peers to scale timely input, support surface/formal aspects of writing, and provide accessible language. --> Effects depended on learners: higher feedback literacy amplified benefits (especially from teacher feedback), and intrinsic motivation increased willingness to revise in the LLM condition. --> Based on these results, the sources should complement rather than replace one another—combine teacher input for depth with AI/peer feedback for coverage, speed, and uptake. ⚠️Potential risks of replacing humans: * #Loss #of #nuanced, #context-#aware #judgment (especially for higher-order reasoning and disciplinary conventions). * #Lower #student #trust, motivation, and uptake of revisions; weaker feedback literacy over time. * #Amplification #of #model #biases and hallucinations, yielding inaccurate or unfair guidance. * #Equity #and #access #gaps if AI works better for some writing styles, languages, or backgrounds than others. * #Accountability, #privacy, #and #academic integrity concerns when feedback origins and rationales aren’t transparent.   Source: https://lnkd.in/efdRXwaT

🧩 Language That Builds Insight Instead of Shame Feedback shapes student identity. When phrased harshly, it creates defensiveness and avoidance. When phrased reflectively, it strengthens executive functioning, emotional regulation, and accountability. Reflections: 🔹 Students internalize the tone, not just the words—we can teach responsibility without shaming. 🔹 Phrases grounded in reflection (“Let’s pause,” “What part can we fix?”, “What strategy helped last time?”) build problem-solving. 🔹 These scripts strengthen metacognition—the ability to think about one’s own thinking. 🔹 When students feel emotionally safe, they are much more willing to take feedback and try again. 🔹 Growth-oriented language reduces conflict and increases collaboration. When adults choose reflective language, students learn to choose reflective actions. — Marc L. Esposito, LMSW 🌐 https://lnkd.in/em_gkhTf 📩 Guide2Empower345@gmail.com IG: @unlockingpotential1 #SelfMonitoring #ExecutiveFunctioning #TeacherTools #SEL #UnlockingPotential