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Comparing ChatGPT Feedback and Peer Feedback in Shaping Students' Evaluative Judgement of Statistical Analysis: A
Xiao Xie1, Lawrence Jun Zhang1, Aaron J Wilson1
1Faculty of Arts and Education, University of Auckland, Auckland 1010, New Zealand.
Behavioral Sciences (Basel, Switzerland)
|July 29, 2025
Summary
ChatGPT and peer feedback offer distinct benefits for doctoral students learning statistical analysis. Combining both may enhance research competence in hybrid learning environments.
Area of Science:
- Education
- Artificial Intelligence
- Statistics
Background:
- Doctoral students in language and education often need statistical analysis skills but may lack confidence.
- Thesis-only programs present unique challenges for developing research competence.
Purpose of the Study:
- To investigate the pedagogical potential of ChatGPT-4o and peer feedback.
- To support doctoral students' evaluative judgment in statistical analysis.
- To compare the influence of AI versus peer feedback on learning.
Main Methods:
- A 14-week doctoral-level statistical analysis course.
- Thirty-two doctoral students received either ChatGPT-4o or peer feedback on an assignment.
- Follow-up interviews were conducted with six participants.
Main Results:
- ChatGPT-4o provided timely, detailed feedback but limited confidence in accuracy verification.
- Peer feedback fostered critical reflection and collaboration but varied in quality.
- Both feedback types influenced evaluative judgment differently across accuracy, value, and process.
Conclusions:
- Neither ChatGPT-4o nor peer feedback alone fully supported students' statistical competence.
- Strategically combining AI and peer feedback may optimize learning in hybrid environments.
- This approach can better equip novice researchers with essential analytical skills.
Keywords:
ChatGPT feedbackHigher Degree by Research studentsevaluative judgementpeer feedbackstatistical analysisMore Related Videos
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