In Assessment in the Age of AI, we examined how formative and summative assessment can remain authentic and aligned with learning goals as generative AI becomes part of teaching and learning. Closely tied to assessment is feedback: assessment signals what we value, and feedback shapes how students respond to that signal. As students gain access to instant, AI-generated comments and suggestions, educators face a new question: How do we ensure feedback remains personal, instructive, and connected to learning goals rather than reduced to automated responses?
Why Feedback Matters
Feedback is central to learning because it helps students understand where they are in relation to learning goals and what steps they can take next. Well-designed feedback is timely, specific, and actionable; above all, it invites students to revise and reflect. Generative AI makes it easier than ever to produce quick comments, model revisions, or suggested edits. That speed can increase opportunities for practice and self-monitoring, but it also risks encouraging students to attend only to surface fixes rather than deeper reasoning. Recent work on classroom formative assessment and AI emphasizes that feedback is most effective when it supports reflection and self-regulated learning rather than simply providing answers (Hopfenbeck et al., 2023).
Feedback as Part of the Learning Process
Effective feedback is not a one-way transmission of information. It is embedded in cycles of practice, revision, and reflection. When educators design opportunities for students to act on feedback by revising drafts, solving new problems, or explaining their reasoning, feedback becomes a path for growth. In the age of AI, educators still set the conditions that make feedback productive: clear criteria, opportunities for action, and prompts that encourage students to explain their thinking.
AI can extend these cycles by producing examples, highlighting patterns in student work, or offering immediate suggestions that students can test. But those affordances have pedagogical value only when students are taught to interpret and evaluate machine-generated suggestions. Educators can scaffold this by asking students to annotate the feedback they accept or reject, to compare AI-generated suggestions with peer comments, or to write brief reflections on how feedback shaped a revision.
Formative Feedback and AI
Formative feedback supports learning as it unfolds. It is often low-stakes and focused on growth rather than evaluation, making it a natural space to consider thoughtful use of generative AI.
When guided by clear learning goals, AI can support formative feedback in ways that extend learning opportunities rather than replace instructor input. For example, students might use AI to receive initial feedback on clarity, organization, or completeness before submitting work. This can help them identify areas for improvement early and arrive at instructor feedback better prepared to engage with it.
At the same time, formative feedback in the age of AI requires careful framing. Students benefit from understanding the limits of AI-generated feedback and from evaluating suggestions critically. Instructors can encourage this by asking students to reflect on how feedback, whether from AI or an instructor, shaped their revisions or by prompting them to justify the changes they chose to make.
Summative Feedback and AI
Feedback also plays a role in summative assessment, even when grades are involved. Summative feedback helps students understand their performance, carry learning forward into future courses, and reflect on their growth over time.
Using AI for summative feedback presents both opportunities and challenges. AI can assist instructors by helping organize comments or identify common patterns in student work. However, summative feedback risks becoming impersonal if over-automated. Students still value feedback that reflects an instructor’s judgment, perspective, and understanding of their work (Alghamdi & Alghizzi, 2025).
Meaningful summative feedback emphasizes reasoning, decision-making, and application. Rather than focusing solely on what was “wrong,” it helps students understand how their thinking aligns with expectations and how they might approach similar tasks differently in the future.
Feedback and Human Connection
One of the lasting strengths of feedback is its human connection. Feedback communicates care, attention, and belief in a student’s ability to improve. These qualities are central to students’ motivation and engagement and are not easily replicated by generative AI.
As educators integrate AI into feedback practices, maintaining this human connection becomes even more important. Clear communication about when and how AI may be used, along with opportunities for dialogue such as conferences, peer review, or reflective activities, helps ensure that feedback remains relational and instructional.
Feedback, Pedagogy, and Purpose
In the age of AI, feedback choices should reflect what educators want students to learn and how they want them to learn it. When feedback is guided by pedagogy, AI can support timely responses and extended practice while educators focus on interpretation, judgment, and encouragement.
Rather than replacing feedback, generative AI invites educators to be more intentional about its role. By designing feedback practices that emphasize reflection, agency, and growth, educators can ensure that feedback remains a powerful tool for learning—one that reinforces the central message of this series: pedagogy comes first, and technology should serve to extend, not replace, good teaching.
Looking Ahead
Feedback and collaboration are closely connected. Feedback often occurs through interaction between educators and students, and among peers. In the next article, Collaboration in the Age of AI, we will explore how generative AI intersects with group work, shared problem-solving, and learning communities, and how collaboration can remain meaningful and authentic when AI is part of the learning environment.
References
Alghamdi, L. H., & Alghizzi, T. M. (2025). Educators’ reflections on AI-automated feedback in higher education: Potentials, pitfalls, and ethical dimensions. Frontiers in Education, 10, 1704820. https://doi.org/10.3389/feduc.2025.1704820
Hopfenbeck, T. N., Zhang, Z., Sun, S. Z., Robertson, P., & McGrane, J. A. (2023). Challenges and opportunities for classroom-based formative assessment and AI. Frontiers in Education, 8, 1270700. https://doi.org/10.3389/feduc.2023.1270700
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Washington, G. (2026, January 31). Feedback in the Age of AI [Blog post].
Retrieved from https://pedagogybeforetechnology.blogspot.com/
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