Artificial Intelligence-Supported Performance-Based Assessment in Oral Radiology Education: A Constructive Alignment Perspective


COŞKUN ALBAYRAK S., Gürel F. S., Kubat G., COŞKUN Ö., Budakoğlu I. İ., ORHAN K.

Diagnostics, cilt.16, sa.16, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 16
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3390/diagnostics16162542
  • Dergi Adı: Diagnostics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO)
  • Anahtar Kelimeler: artificial intelligence, assessment, automated pathology detection, constructive alignment, dental imaging, oral and maxillofacial radiology
  • Lokman Hekim Üniversitesi Adresli: Evet

Özet

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.