.png){kind=icon}
.png){kind=icon}
Return
Artificial Intelligence-Supported Performance-Based Assessment in Oral Radiology Education: A Constructive Alignment Perspective
.png){kind=icon}
OA
AI
S
F
G
Ö
I
K
DOI：10.3390/diagnostics16162542.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Background/Objectives: This study aimed to examine the educational implications of an AI-supported performance-based assessment platform in oral radiology education within a constructively aligned framework. Methods: A total of 266 fourth-year dental students evaluated five panoramic radiographs using an AI-supported lesion detection platform with a predefined detection confidence threshold of 40%. Student performance was assessed using a confusion matrix framework, and precision and sensitivity values were calculated. Receiver operating characteristic (ROC) analysis was conducted to explore the relationship between region selection frequency and AI-supported diagnostic performance. Group comparisons were performed using the Mann–Whitney U test. Results: ROC analysis demonstrated a statistically significant but weak inverse association between region selection frequency and AI-supported diagnostic performance (AUC = 0.414, 95% CI: 0.342–0.486, p = 0.018). Students exceeding the cutoff of 8.5 marked regions demonstrated significantly lower performance scores than those at or below the cutoff (p < 0.001). No significant correlation was observed between AI-supported performance scores and traditional summative examination outcomes. Conclusions: AI-supported performance-based assessment demonstrated value in measuring applied diagnostic reasoning distinct from traditional written examinations. Within a constructively aligned framework, such tools may serve as complementary strategies for competency-oriented assessment in dental education.
Keywords:
artificial intelligence
assessment
automated pathology detection
constructive alignment
dental imaging
oral and maxillofacial radiology
Journal
IF:
3.3
Papers:
1.9W
Citations:
3.6W
Organization
Cited Papers