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Enhancing graduate students' evaluative skills through AI-supported collaborative marking.
Di Wu1, Tiong-Thye Goh2, Dexin Chen1
1Education College, Hubei University, Wuhan, China.
Frontiers in Psychology
|April 16, 2026
Summary
AI-supported collaborative marking improves graduate students' ability to assess research writing by providing expert criteria and facilitating calibration, enhancing academic assessment literacy. This approach reduces errors and boosts consistency in evaluating research papers.
Area of Science:
- Educational Technology
- Higher Education Pedagogy
- Artificial Intelligence in Education
Background:
- Developing reliable evaluative judgment is crucial for graduate students assessing research writing.
- Existing AI-assisted assessment research primarily focuses on predictive accuracy, neglecting the psychological impact on learners.
- Learners need to internalize standards, monitor accuracy, and calibrate judgments against expert benchmarks for effective evaluation.
Purpose of the Study:
- To investigate how AI-supported collaborative marking influences learners' evaluative judgment development.
- To conceptualize AI-supported collaborative marking as a metacognitive scaffold for externalizing criteria and supporting calibration.
- To compare the effectiveness of AI-supported collaborative marking against student-only, AI-only, and retrieval-augmented AI marking.
Main Methods:
- A mixed-method approach was used with 121 graduate research papers.
- Four assessment conditions were employed: student-only, AI-only, retrieval-augmented AI, and AI-supported collaborative marking.
- Evaluations were benchmarked against expert judgments across rigor, originality, significance, and academic conventions.
Main Results:
- AI-supported collaborative marking significantly reduced deviations from expert ratings (MAE decreased from 3.260 to 1.221) and improved inter-rater consistency.
- Behavioral sequence analyses showed distinct evaluative patterns: senior students demonstrated stronger metacognitive monitoring and reflective reasoning, while junior students used more trial-and-error strategies.
- AI-supported collaborative marking was associated with observable changes in students' evaluative interaction patterns and reflective behaviors.
Conclusions:
- AI-supported collaborative marking enhances assessment accuracy and consistency in graduate education.
- This approach acts as a metacognitive scaffold, promoting self-regulated learning and the development of academic assessment literacy.
- Findings highlight developmental differences in evaluative competence and the potential of AI to support these differences.
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