How To Incorporate Feedback Loops In Curriculum Design

Too often, offering students feedback is an exercise in compliance. The professor offers feedback, and expects the students to incorporate all of it. (It’s like the professor is giving items on a checklist. The subtext: “do these things and I’ll give you an A.”) But I want my students to think about feedback differently. I want them to be able to cut between different sets of feedback, connecting them to each other and linking them back to their own understanding. With that in mind… Here’s the feedback cycle I’ve designed for my Comp II students at Berkeley. 1️⃣ Self-Assessment Students use their own self-designed rubric to evaluate their own performance. 2️⃣ Peer Assessment Students get feedback and assessment from other students. 3️⃣ Instructor Assessment I’ll offer feedback on the assignment. 4️⃣ AI Assessment Students get feedback from a custom chatbot. I will be incorporating some of Anna Mills’s prompts for the PAIRR framework. 5️⃣ Assessment Assessment (or Reflection) Students apply the different assessments to their own self-assessment. They defend their ultimate edits within the context of their Self-Empowering Writing Process (SEWP).

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

Back in 2017, my team had a simple but powerful ritual. We held "I have a design challenge" meetings, where someone would bring a project they were working on, and we’d workshop it together. These sessions weren’t just about fixing problems. They helped us grow our skills as a team and learn from each other’s perspectives. In 2024, I wanted to bring that same energy to learning designers looking to level up their skills in a fun and engaging way. This time, I turned to Tim Slade’s eLearning Challenges but took a different approach. Instead of just participating, we started doing live reviews of the challenge winners. How It Works One person drives the meeting, screensharing the challenge winner’s eLearning project while recording the session. We pause at each screen and ask two simple but high-impact questions: ✅ What worked well and why? ✅ What would you do differently and why? This sparks rich discussions on everything from instructional design and accessibility to visual design and interactivity. Everyone brings their unique expertise, turning the meeting into a collaborative learning experience. Want to Try It? Here’s What You Need ✔️ A web conferencing tool with recording capabilities ✔️ Adobe Premiere Pro or a transcript tool (optional, but helpful) ✔️ A generative AI tool like ChatGPT, Gemini, or Claude (optional for extracting themes from discussions) After the session, we take the recording and import it into Adobe Premiere, which generates a transcript in seconds. Then, using GenAI, we pull key themes, quotes, and takeaways, turning raw discussions into actionable insights. Why This Works This approach takes learning from passive to interactive. You’re not just seeing best practices. You’re critically analyzing them with peers, learning through feedback, and refining your own instructional design instincts. Would you try this with your team? Have you tried something similar? What worked well? #InstructionalDesign #GenAI #LearningDesign #eLearning #AIinLearning #CourseDevelopment #DigitalLearning #IDStrategy #EdTech #eLearningDesign #LearningTechnology #InnovationInLearning #CustomerEducation