AI Generated Feedback

I would like to share a recent AI SoTL article entitled “The dimensions of abundance in AI-generated feedback” by Lindsay (2026) (https://doi.org/10.3390/educsci16030465 ). This study advances an important shift in how we conceptualize AI in education by examining the “dimensions of abundance” in AI-generated feedback. Rather than focusing solely on accuracy or efficiency, the authors argue that generative AI introduces a fundamentally new condition: learners are now immersed in abundant, immediate, and iterative feedback environments that require new forms of learner competence.

From a learning sciences perspective, this reframes feedback from a scarce instructional resource to a cognitively saturated environment, directly implicating cognitive load theory. While AI reduces barriers to accessing feedback (lowering extraneous load), the volume and variability of feedback can overwhelm working memory unless learners develop feedback literacy to evaluate, prioritize, and act on inputs. This aligns with research suggesting that AI effectiveness depends not on access, but on learners’ capacity to regulate and interpret information.

The study also aligns with metacognitive and self regulated learning frameworks. Students must now engage in monitoring, evaluating, and adapting based on multiple AI generated responses, shifting feedback from a unidirectional instructor input to a dialogic, iterative process. This reflects a move toward co-regulation, where AI acts as a dynamic partner, but the learner retains epistemic responsibility.

Critically, the authors highlight that without explicit instructional scaffolds, students may engage superficially with abundant feedback, reinforcing patterns of cognitive offloading rather than deeper learning. This reinforces prior findings that AI can both enhance and diminish critical thinking depending on task design and learner engagement.

Findings
AI-generated feedback creates a condition of abundance that shifts the learning challenge from accessing feedback to effectively managing and using it. Learner success increasingly depends on feedback literacy, metacognitive regulation, and the ability to discriminate between multiple AI-generated responses.

From Research to Practice
• Teach explicit feedback literacy skills, including how to evaluate, compare, and refine AI-generated responses
• Design tasks that require students to justify how they used AI feedback in revisions
• Structure iterative AI interactions that promote reflection rather than one-step answers
• Align AI feedback use with authentic assessments emphasizing synthesis and transfer

Reference

Lindsay, E. (2026). The dimensions of abundance in AI-generated feedback. Education Sciences, 16(3), 465.https://doi.org/10.3390/educsci16030465