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Related Experiment Videos

Rethinking AI-assisted writing instruction: feedback literacy scripts, calibration training, and student writing

Zhe Dai1

  • 1School of Humanities (School of Marxism), Zhejiang Institute of Communications, Hangzhou, China.

Frontiers in Psychology
|June 29, 2026
PubMed
Summary

Two metacognitive interventions improved AI-assisted writing. Feedback Literacy Script (FRAC) enhanced writing quality, while Assessment-Performance Calibration Activity (APCA) boosted self-assessment accuracy, with combined use yielding the best overall results.

Keywords:
AI-assisted writingfeedback literacymetacognitive interventionself-assessment accuracywriting quality

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Area of Science:

  • Educational Technology
  • Writing Studies
  • Artificial Intelligence in Education

Background:

  • Generative AI is increasingly used in writing instruction, posing challenges in student feedback interpretation and critical use.
  • Effective AI-assisted writing requires students to critically evaluate and utilize feedback for improvement.

Purpose of the Study:

  • To investigate the impact of two metacognitive interventions on writing quality and self-assessment accuracy in AI-assisted writing.
  • To compare the effects of a Feedback Literacy Script (FRAC) and an Assessment-Performance Calibration Activity (APCA) individually and in combination.

Main Methods:

  • A 2x2 mixed factorial design with 120 undergraduate English majors across four conditions: regular AI use, FRAC only, APCA only, and FRAC+APCA.
  • Data collected on writing quality gain, self-assessment accuracy, overconfidence, feedback uptake, and revision depth across four writing tasks.

Main Results:

  • FRAC significantly improved writing quality, feedback uptake, and revision depth, enhancing students' processing of AI feedback.
  • APCA demonstrated the strongest effect on self-assessment accuracy and overconfidence reduction, improving internal judgment.
  • The combined FRAC+APCA intervention yielded the highest overall writing quality gains and retention but did not surpass APCA alone in self-assessment accuracy.

Conclusions:

  • Writing quality and self-evaluation accuracy are related but distinct outcomes in AI-assisted writing.
  • The educational value of AI-assisted writing hinges on learners' ability to evaluate feedback, calibrate self-judgment, and develop independent revision skills.
  • Metacognitive interventions like FRAC and APCA are crucial for maximizing the benefits of AI in writing education.