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Enhancing learner-centered feedback with AI: teachers'' practices and perceptions

● high · 2026-08-03generative-ai, feedback-loop, teacher-role, higher-ed, faculty-development, scaffolding, human-in-the-loop-ai, faculty-development-genai, rag

Enhancing learner-centered feedback with AI: teachers' practices and perceptions

Summary

An empirical study of 21 higher-education teachers using PolyFeed, an AI-powered feedback tool combining (1) a BERT-based ML model (from Aldino et al. 2024) that detects which learner-centered feedback components are missing from teacher-written feedback and suggests them, and (2) ChatGPT-4o mini to rephrase/enhance the teacher's draft. Teachers gave feedback on a simulated student presentation, then used the tool, then were interviewed. The study answers two questions: how teachers interact with AI feedback tools (RQ1) and how they perceive them (RQ2). Framework: Ryan et al.'s (2023) learner-centered feedback dimensions — Future Impact, Sensemaking, Agency.

RQ1 — How teachers interacted with the tools

ML suggestion acceptance (detection → adoption)

  • The ML model most often flagged missing Meeting Learning Objective (20 of 21 teachers omitted it; 16 accepted the suggestion) and Student–Teacher Relationship (14 omitted; 12 accepted).
  • Affirmation and Encouragement was missing in 6 cases but accepted by all 6 teachers (100%).
  • Teachers used professional judgement to accept or reject suggestions — the ML functioned as a diagnostic assistant, not an autopilot.

    • Revision of ChatGPT-enhanced feedback

  • 12 of 21 teachers made further sentence-level revisions; the rest left the text unchanged.
  • Most common actions: editing (f = 32) and removing (f = 27); adding was rare (f = 8).
  • The dominant pattern was calibrating tone: Edit–Praise (f = 11, e.g. "First of all, I want to congratulate you on completing your assignment!" → "Well done on completing your assignment"), Remove–Suggestion (f = 9), Remove–Encouragement (f = 8), Remove–Praise (f = 6), Edit–Correction (f = 9). Teachers systematically moderated exaggerated praise and generic suggestions to protect authenticity and professional voice.
  • Revisions clustered most strongly in the Agency dimension — especially Student–Teacher Relationship (f = 24; encouragement f = 11) — confirming that the relational/affective dimension of feedback resists AI delegation.

    • RQ2 — Teacher perceptions

  • Benefits: promotes reflection (n = 14), improves language and structure (n = 11), identifies missing components (n = 10), saves time (n = 2).
  • Challenges: need for human editing (n = 9), inconsistent tone (n = 7), potential misinformation (n = 5), trust issues (n = 5).
  • Experience gap: teachers with >5 years of experience reported more challenges (tone, editing burden, trust, misinformation); less-experienced teachers valued scaffolding benefits (identifying missing components, saving time). The authors flag a developmental risk: novice teachers who defer to AI suggestions may build less independent feedback judgement.

    • Interpretation

  • The tools were most valuable as reflective prompts that surface overlooked aspects of feedback — "making invisible gaps visible" — scaffolding teachers' evaluative judgement, rather than as providers of finalised text (an "assist but verify" pattern; cf. Human In The Loop AI).
  • Design implications: adjustable tone parameters, discipline-specific templates, and transparency controls; without them AI adoption may increase rather than reduce workload and can erode teacher authority if positioned as autonomous providers.
  • The authors call for future research on student perspectives of AI-assisted teacher feedback and longitudinal study of whether reliance strengthens or displaces evaluative judgement.

    • Connected Concepts

  • Faculty Development
  • Higher Ed
  • Human In The Loop AI
  • Scaffolding
  • Teacher Role
  • Generative AI
  • RAG

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    • Citation

    Aldino, A. A., Maheshi, B., Li, Y., Zhou, Y., Tsai, Y.-S., Gašević, D., & Chen, G. (2026). Enhancing learner-centered feedback with AI: Teachers' practices and perceptions. Assessment & Evaluation in Higher Education, 51(5), 892–910