Through the process of completing both peer and AI reviews, I felt like I was able to expand on my work in a meaningful way. When first completed, my work had the basic structure necessary, but there were important details and evidence missing. For example, below you can see my AI review suggested that I clarify learning objectives in my work and connect them to curriculum and standards. After receiving that feedback, I added specific learning outcomes as created by the National Association for Music Education, as well as requiring learners to do the same in their culminating project for the module.

The peer review that was completed also helped to fill in gaps that I had missed within the work. For example, the reviewer mentioned that I failed to suggest applications for my learning module outside of my content area. This was helpful in my revision process as I was able to gain an outside view and create a module that is more applicable to a diverse group of learners.

With each version of this work, I found that I was able to view it from a different point of view. At first, I was focusing on just my perspective, but as I got reviews I was able to see how this might be applicable to others and what I could change to enhance it.

I also benefited from a peer review of some specific requirements for the work. For example, my peer pointed out an issue with the references. This allowed me a chance to review the requirements and ensure my work was ready for submission and publishing.

Throughout the revision process, I found there to be benefits to each type of review, although the peer review was especially helpful. I found the AI review to be repetitive, with it mentioning the same issues in my work under many different sections of the rubric. However, I did appreciate the fact that I could complete this review with little pressure, and as many times as I wanted. I did not feel like I was wasting a peer’s time if I submitted an AI review that I was not particularly proud of yet.

What I appreciated most about the review process in general was suggestions made by the peer and AI. Even when giving a “+” annotation, my peer provided suggestions to make it even better.

One challenge I faced with reviews in CGMap included formatting and media elements. For example, I had worked to ensure my work was in the proper learning module before submitting. However, when imported into CGMap, the format was incorrect. I also embedded video demonstrations into the module, which did not transfer to CGMap. These formatting issues led to my receiving 2 comments from my peer about specific things that I had actually already done.

Overall, both reviews are important and I think they work together well. I am appreciative of the flexibility and immediacy of the AI review, and also the interaction and understanding that are available from a peer. Through the revision process, I believe incorporating both is the best way to achieve a great work.