Forum Discussion
Scaling feedback?
I think the type of feedback matters a lot when deciding whether it can actually scale. For objective tasks, automated feedback can work well, but for open-ended work, the value often comes from understanding the learner's reasoning rather than just identifying whether the answer is correct.
AI could help with the first layer of feedback, such as pointing out patterns or suggesting areas to revisit, while peer or instructor feedback could handle the parts that require more context and judgment. That might make scaling more realistic without relying on AI to replace the human side completely.
How do others decide which parts of learner feedback are suitable for automation and which should remain human-led?
I really like this answer alextaylorone — I like the idea of being critical about which parts of the feedback motion are possible to automate and which require a human in the loop. It allows us to not be so black and white about saying "AI can't replace human feedback" so that a piece of the motion can scale with the help of technology, while still ensuring quality.
Have we connected in the community before? I like how you think! It would be great to have a virtual coffee chat some time :)