Using AI to Build Student Feedback Literacy and Leverage Assessment as a Learning Tool

This conceptual article explores practical ways in which college and university instructors can leverage artificial intelligence (AI) to support assessment as learning and develop students’ feedback literacy. Traditional assessment practices often focus on assessment for learning (informing instruction) or of learning (measuring outcomes), while assessment as learning—when students actively monitor and reflect on their own learning—receives less attention. AI can help instructors leverage assessment as a learnin…

By the Truvace deskOctober 19, 20261 min read1 sourcePeer-reviewedTRV-2026-1892· cite & verify

In brief

A conceptual paper aimed at college and university instructors describes how artificial intelligence can support assessment as learning, in which students monitor and reflect on their own progress. It proposes AI-generated work samples for analysis and prompt feedback that students use to revise.

This matters because assessment as learning gets less attention than assessment for or of learning, so the approach could shift how instructors use feedback. As a conceptual piece, it does not report measured results, so the benefits remain claims to be tested in classrooms.

Main points

The article separates assessment for learning, of learning, and as learning, noting the last receives less attention.
AI can generate work samples that students analyze as part of building feedback literacy.
AI gives timely, actionable feedback that students can use to revise their work.
Instructor modeling, peer dialogue, guiding questions, and graphic organizers are paired with AI feedback.
The article supplies prompts and strategies for instructors.

The gain

The article argues instructors can use AI to generate work samples and give timely feedback, which, with scaffolds, may build students' metacognition and self-directed learning.

The rundown

The article frames traditional assessment as focused on informing instruction or measuring outcomes. It highlights assessment as learning, where students actively monitor and reflect on their own learning, as the underexplored area.

For practice, it recommends AI-generated work samples that students examine, alongside AI feedback they revise against. These are combined with instructor modeling and peer dialogue.

The authors present prompts and strategies intended to make feedback practices more efficient, effective, and student-centered, targeting higher education instructors.