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📄 Research Article

Making AI-Generated Feedback Matter: From Provision to Student Enactment

● high · 2026-08-13feedback-loop, formative-assessment, learning-analytics, higher-ed, student-experience, self-regulated-learning, human-ai-collaboration, llm

Synthesis: Alsaiari et al. (2026) report a large-scale quasi-experimental cohort study (13,037 students; 51,296 student-authored resources) comparing three AI-mediated feedback workflows. Students in the Enacted Feedback condition — prompted to select feedback suggestions, evaluate their relevance, and engage in targeted AI dialogue anchored to those selections — showed significantly higher uptake of AI-generated feedback (26.2% estimated probability) than Directed Feedback (14.1%) or Self-Directed Feedback (0.1%), along with higher self-assessment confidence and submitted-work quality. The finding positions student enactment, not comment quality, as the decisive variable in AI feedback, connecting to Feedback Loop, Self Regulated Learning, and Human AI Collaboration research.

From Provision to Enactment

Feedback value depends on two challenges: providing high-quality, timely, individualized feedback at scale, and supporting students to interpret, evaluate, and act on that feedback productively. Generative AI credibly addresses the provision challenge, but students' uptake of AI-generated feedback remains limited without structured support.

Three Workflows

  • Directed Feedback (n=3,723): students received AI-generated feedback comments without structured support.
  • Self-Directed Feedback (n=3,951): students could initiate optional AI-supported dialogue.
  • Enacted Feedback (n=5,363): students were prompted to select feedback suggestions, evaluate their relevance, and engage in targeted AI-supported dialogue anchored to those selections.

    • Key Findings

    Enacted Feedback was associated with significantly higher uptake (26.2% vs 14.1% Directed vs 0.1% Self-Directed), significantly higher self-assessment confidence, and higher submitted-work quality. The authors conclude that AI access alone is insufficient; purposeful workflow design that positions learners as active participants in judgement, dialogue, and improvement is central to productive feedback use.

    Connected Concepts

  • Feedback Loop
  • Self Regulated Learning
  • Human AI Collaboration
  • Learning Analytics
  • Formative Assessment
  • Student Experience
  • AI Feedback Quality
  • Scaffolding
  • Higher Ed
  • LLM

    • Connected Articles

  • AI Generated Feedback Higher Ed
  • GenAI Feedback Design Multisite Experiment
  • Sequenced AI Feedback Learning
  • AI Internal Feedback Evaluative Judgments
  • Learner Centered Feedback AI
  • Repeated AI Writing Feedback Semester
  • Feedback Futures GenAI
  • Care Full Feedback GenAI

    • Citation

    Alsaiari, O., Baghaei, N., Lodge, J. M., Gašević, D., Winstone, N., & Khosravi, H. (2026). Making AI-generated feedback matter: From provision to student enactment. arXiv:2608.11625.