The Effects of Explanations in Automated Essay Scoring Systems on Student Trust and Motivation
Problem
It was unknown how explanations in automated essay scoring systems affect students' trust and motivation.
Why it matters
Student trust in AI grading affects acceptance and fairness of AI use in education.
What the AI did
User study testing full-text global explanations and an accuracy statement in an automated essay scoring system, designed via needs-elicitation and explainable AI guidelines.
Results
Both explanation types had no effect on student trust or motivation versus no explanations; the gap between self-estimated and system grade showed a larger effect (abstract truncated).
Impact
Shows explanations alone did not increase student trust or motivation in AI essay scoring.
Record
| Venue | Journal of Learning Analytics |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Rianne Conijn, Patricia K. Kahr, Chris C. P. Snijders |
| Citations | 76 (OpenAlex, at import time) |
| References | 79 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.18608/jla.2023.7801 |
| Open access | diamond |
| Keywords | explainable AI, automated essay scoring, student trust, motivation, education |
Importance rating
real but narrower advance
Trust and fairness in AI grading is a real education concern, but the study is narrow and the abstract states no scale of impact.