Esra Yıldırım Manav

Purpose: Artificial intelligence (AI) chatbots are increasingly used by dental students and faculty for literature review, coursework preparation, and scholarly writing. In restorative dentistry education, where evidence‐based decision‐making is fundamental, inaccurate AI‐generated citations may pose educational risks. This study evaluated the bibliographic reliability of AI‐generated references in restorative dentistry topics and examined their potential implications for dental education. Methods: Five large language model (LLM)–based chatbots were tested using ten standardised restorative dentistry prompts representing commonly assigned educational topics. Each chatbot generated 100 references ( N = 500). References were manually verified using indexed databases and evaluated using the Reference Hallucination Score (RHS; range 0–11). Chatbot performance was compared using the Friedman test, followed by Dunn–Bonferroni post hoc pairwise comparisons. Binary bibliographic error parameters were analysed using generalised linear mixed models, with chatbot type, prompt complexity, and their interaction as fixed effects and prompt as a random intercept (α = 0.05). Results: A significant overall difference in RHS was observed among the evaluated AI chatbots ( p < 0.001), with post hoc analyses identifying significant differences between specific model pairs. ChatGPT‐5.2 and Perplexity AI demonstrated the lowest hallucination severity, whereas Copilot and DeepSeek‐v3.2 demonstrated the highest. Prompt complexity did not significantly influence hallucination severity. Errors most frequently involved author names, journal titles, and DOIs. Conclusions: AI‐generated references in restorative dentistry demonstrated substantial variability in bibliographic accuracy across the evaluated AI systems. These inaccuracies may pose potential risks to evidence‐based learning and academic integrity, highlighting the importance of systematic reference verification and AI literacy in dental education.

Objective: This in vitro study aims to investigate how different bioactive base materials influence the bond strength of two universal adhesive systems, Single Bond Universal and Prime&Bond Universal, when applied to dentin-liner complexes. Understanding the interaction between adhesives and base materials is critical to improving the longevity of restorations placed in deep cavities.Methods: Eighty extracted human molars were sectioned and standardized dentin surfaces were prepared. Four bioactive base materials (TheraCal LC, Riva LC, Riva SC, and BioMTA+) were applied, followed by one of the two universal adhesives. Composite build-ups were placed and specimens underwent 10,000 thermocycles to simulate oral aging. Shear bond strength (SBS) testing was performed, and failure modes were analyzed microscopically. A two-way ANOVA and Tukey's post hoc tests were used (α = 0.05).Results: The type of base material had a statistically significant effect on bond strength (p < 0.001), while the adhesive brand did not (p = 0.247). TheraCal LC yielded the highest mean bond strength (20.6 ± 2.1 MPa), whereas BioMTA+ showed the lowest (10.2–11.4 MPa). A failure mode analysis revealed that cohesive failures predominated in the BioMTA+ group, suggesting internal material weakness.Conclusions: The bond strength of universal adhesives is substantially affected by the choice of bioactive base material. TheraCal LC demonstrated superior performance, which is likely due to its resin-modified composition and light-curing capability. No significant differences were observed between the two adhesives tested.

Objective:This in vitro study aims to evaluate the effect of different polishing protocols on the surface roughness and gloss of two additively manufactured permanent resin materials used for definitive restorations.Materials and Method: Sixty disk-shaped specimens (12 × 2 mm) were fabricated from two 3D-printed permanent resins, Bego VarseoSmile Crown Plus and Saremco CrownTec, and post-cured using a xenon flash light-curing unit. Each material was divided into three subgroups (n = 10): (1) unpolished control; (2) Diaprint 3D Twist multi-step rubber polishers; and (3) OptiDisc aluminum oxide discs. The surface roughness (Ra, µm) was measured with a contact profilometer, and the gloss (GU) was recorded with a glossmeter at 20°, 60°, and 85° incident angles, according to ISO 2813. The data were analyzed using a two-way ANOVA and Tukey’s post hoc test (α = 0.05).Results:The polishing protocol significantly influenced the surface roughness (p < 0.001), whereas the material type did not (p > 0.05). The Diaprint 3D Twist system produced the smoothest surfaces (Ra = 0.22 ± 0.04 µm), which were significantly lower than those achieved with OptiDisc or the control group (p < 0.05). Both the polishing protocol (p = 0.001) and the material type (p < 0.001) significantly affected the gloss. The highest gloss was observed in the Saremco-Diaprint 3D Twist group (79.0 ± 4.6 GU). No significant interaction was found between the factors.Conclusion:The choice of polishing protocol plays a critical role in optimizing the surface quality of the 3D-printed permanent resins. The Diaprint 3D Twist multi-step rubber system provided the smoothest and most glossy surfaces for both materials, achieving roughness values below the clinical threshold for bacterial adhesion. Saremco CrownTec exhibited a higher gloss than Bego VarseoSmile Crown Plus, which was likely due to its filler characteristics.

Objective: This in vitro study aimed to evaluate the effects of substrate composition, surface conditioning protocol, and repair material type on the repair shear bond strength (SBS) of an aged self‐cure bulk‐fill resin composite. Materials and Methods: A total of 80 specimens were fabricated using two substrate compositions of a self‐cure bulk‐fill composite (Stela; Automix and Capsule). After initial aging by thermocycling, specimens were repaired according to a 2 × 2 × 2 factorial design based on surface conditioning (mechanical roughening with or without 37% phosphoric acid etching) and repair material type (self‐cure composite with proprietary primer or light‐cure nanohybrid composite with a universal adhesive). SBS was measured using a universal testing machine. Failure modes were analyzed using a stereomicroscope. Data were analyzed using three‐way ANOVA, Tukey's HSD, and Fisher's exact tests ( p < 0.05). Results: Substrate composition, surface conditioning protocol, and repair material type each significantly affected repair bond strength ( p < 0.001). Capsule‐based substrates showed higher SBS values than Automix substrates. Phosphoric acid etching significantly increased SBS compared with mechanical roughening alone. Repairs performed with the same self‐cure composite exhibited significantly higher SBS than those repaired with a light‐cure composite. Significant two‐way interactions were observed between substrate composition × repair material type ( p < 0.001) and surface conditioning × repair material type ( p = 0.015). The highest SBS was observed in the Capsule + acid etching + self‐cure repair group. Failure mode analysis revealed predominantly cohesive or mixed failures in self‐cure repair groups, whereas light‐cure repair groups showed a higher frequency of adhesive failures. Conclusion: Repair bond strength of a self‐cure bulk‐fill composite is highly material‐ and protocol‐dependent. Utilizing a chemically compatible self‐cure material system combined with adjunctive phosphoric acid etching significantly optimizes repair longevity, especially when applied to mechanically triturated capsule‐based substrates. Clinical Significance: The selection of repair material and surface conditioning protocol significantly influences the repair bond strength of aged self‐cure bulk‐fill composites. When the original restorative material is known, repair with the same composite system following phosphoric acid conditioning may provide more durable bonding.

Aims: This study aimed to conduct a structured three-year program evaluation of an oral and maxillofacial radiology course using the context-input-process-product (CIPP) model within a constructive alignment framework. The evaluation examined national curriculum alignment, instructional design, assessment practices, and longitudinal student performance.Methods: A retrospective institutional program evaluation was conducted across the 2022-2023, 2023-2024, and 2024-2025 academic years. Document analysis included National Core Curriculum in Predoctoral Dental Education (NCCPDE)-2024 national core curriculum standards, course information packages, learning outcomes, accreditation reports, examination questions, blueprint analyses, examination results, and student feedback reports. Curriculum alignment was quantified using structured mapping matrices. Final examinations (40 multiple-choice items; passing threshold ≥60) were analyzed using blueprint-based content and Bloom’s revised taxonomy. Descriptive and comparative statistical analyses were performed.Results: Coverage analysis demonstrated that the course addressed 72.8% of NCCPDE-defined core conditions and 35.2% of core signs and symptoms. All course learning outcomes were aligned with at least one national competency component. Blueprint analysis revealed a structured progression from foundational knowledge in period 3 to applied diagnostic reasoning in period 4. Artificial Intelligence (AI)-supported instructional integration was gradually reflected in assessment items (5.0%-10.0%). Across three academic years, pass rates remained high (95.9%-100%), and no statistically significant decline in mean final scores was observed (p>0.05).Conclusion: A CIPP-based, policy-aligned evaluation demonstrated that national curriculum integration, blueprint-informed assessment design, and AI-supported instructional innovation can be implemented without compromising student performance. The findings highlight the value of theory-informed curriculum governance in competency-based dental education.

Objective: To investigate how polymerization mechanism influences surface roughness, microhardness, and curing homogeneity of bulk-fill resin composites. Materials and Methods: Two light-cure bulk-fill composites (Estelite Bulk Fill Flow, VisCalor Bulk) and two self-cure bulk-fill composites (Stela Automix, Stela Capsule) were evaluated (n = 12 per material). Cylindrical specimens (6 mm diameter × 4 mm thickness) were fabricated. Surface roughness (Ra) was measured using contact profilometry. Vickers microhardness (VHN) was determined on both top and bottom surfaces. Curing homogeneity was assessed using the bottom-to-top hardness ratio. Data were analyzed using two-way ANOVA and Tukey post hoc tests (α = 0.05). Results: Self-cure composites demonstrated significantly higher surface roughness than light-cure materials (p < 0.001). Light-cure composites showed significantly greater top and bottom surface microhardness (p < 0.001). However, light-cure materials exhibited a significant reduction in hardness at the bottom surface, resulting in lower hardness ratios (0.83–0.85). In contrast, self-cure materials showed no significant top–bottom difference and presented significantly higher hardness ratios (0.95–0.96; p < 0.001). Conclusions: The polymerization mechanism appears to influence the surface characteristics and curing behavior of bulk-fill resin composites. Light-cured materials tended to exhibit higher hardness values, whereas chemically cured materials demonstrated a more uniform polymerization profile throughout the material thickness. These findings suggest that the type of polymerization may be a factor worth considering during material selection, particularly in deep restorations where light transmission may be limited.

Objective: This study aimed to evaluate whether radiopacity differs among additively manufactured permanent resins, temporary resins, and conventional resin‐based composites at clinically relevant thicknesses. Materials and Methods: Eight dental materials were evaluated and categorized into four groups: additively manufactured permanent resins (VarseoSmile Crown Plus, Saremco Print CrownTec), additively manufactured temporary resins (Arma Resin TEMP Teeth, Alias Temp C&B), conventional self‐cure resin‐based composites (Stela Automix, Stela Capsule), and conventional light‐cure resin‐based composites (Gradia Posterior, G‐ænial Injectable). Rectangular specimens (5 × 5 mm) were fabricated at two standardized thicknesses (1 and 2 mm) ( n = 10). Extracted human tooth sections and a 21‐step aluminum wedge were used as reference standards. Radiographic images were obtained using a digital intraoral radiography system, and radiopacity values were measured in mmAl using ImageJ software. Statistical analysis was performed using Kruskal–Wallis, Dunn–Bonferroni, and Mann–Whitney U tests ( α = 0.05). Results: Significant differences in radiopacity were observed among the tested materials ( p < 0.05). Additively manufactured temporary resins demonstrated the lowest radiopacity values, whereas conventional resin‐based composites exhibited the highest radiopacity. Permanent additively manufactured resins showed intermediate radiopacity values. Increasing specimen thickness from 1 to 2 mm significantly increased radiopacity values for all materials ( p < 0.001). Several temporary additively manufactured materials presented radiopacity values lower than dentin and enamel. Conclusion: The findings suggest that radiopacity varies significantly among restorative materials and is influenced by specimen thickness. In particular, the relatively low radiopacity of some additively manufactured resins may limit their radiographic detectability, which should be considered during material selection in clinical practice. Clinical Significance: Radiopacity differences among restorative materials may influence the detection of recurrent caries and restoration margins. Lower radiopacity of some additively manufactured resins highlights the importance of careful material selection for accurate radiographic diagnosis.

Objective: To evaluate the influence of adhesive curing mode and composite viscosity type on the fracture resistance of reattached anterior teeth using the internal groove technique. Materials and Methods: Seventy‐two extracted human maxillary central incisors were fractured using a standardized protocol and randomly assigned to six groups according to adhesive curing mode (light‐cure, self‐cure, and dual‐cure) and composite viscosity type (flowable and packable) ( n = 12). Fragments were reattached using standardized procedures. All specimens were subjected to thermocycling (5000 cycles) and fracture resistance was measured using a universal testing machine. Fracture resistance recovery (%) was calculated. Data were analyzed using two‐way ANOVA and Tukey post hoc tests ( α = 0.05). Results: Both adhesive curing mode and composite viscosity type significantly influenced fracture resistance ( p < 0.001), while no significant interaction between factors was observed ( p > 0.05). The highest fracture resistance recovery was observed in the dual‐cure packable group, whereas the lowest values were recorded in the light‐cure flowable group. Packable composites demonstrated higher fracture resistance than flowable composites across all adhesive curing modes. Conclusion: Dual‐cure adhesives combined with packable composites demonstrated the highest fracture resistance, whereas light‐cure adhesives with flowable composites demonstrated the lowest values. Clinical Significance: Dual‐cure adhesives combined with packable composites demonstrated higher fracture resistance in fragment reattachment procedures.

This study aimed to evaluate changes in surface roughness after simulated toothbrushing in conventional and additively manufactured resin-based restorative materials using different dentifrices. Four conventional resin composites (flowable, ormocer-based, supra-nano-filled, and bulk-fill) and two additively manufactured restorative resins were included. Specimens were subjected to five brushing conditions: distilled water (control), fluoridated, bioactive, probiotic, and whitening dentifrices ( n = 10). Toothbrushing was performed for 5,000 cycles under a 200-g load. Surface roughness (Ra) was measured before and after brushing using a contact profilometer, and surface morphology was qualitatively assessed by scanning electron microscopy. Data were analyzed using two-way analysis of variance followed by post hoc tests (α = 0.05). No significant main effect of restorative material ( p = 0.540) or dentifrice type ( p = 0.645) was observed; however, a significant material–dentifrice interaction was detected ( p < 0.001). Pairwise comparisons revealed material-dependent differences in roughness change following brushing. Within the limitations of this in vitro study, post-brushing surface roughness was influenced by the specific combination of restorative material and dentifrice. These findings may be relevant when selecting restorative materials and dentifrices for clinical use.

Aim: This study aimed to evaluate the effectiveness of virtual reality (VR) haptic simulators and a multi‐layered dental preparation plate (CaviPrep) in developing manual dexterity among third‐semester dental students during preclinical training in restorative dentistry. Methods: Seventy‐five dental students were randomly assigned to three groups ( n = 25). Students in Group 1 and Group 2 received initial training using a VR haptic simulator and CaviPrep, respectively prior to performing procedures on extracted human mandibular first molar teeth. Group 3 served as the control group, performing cavity preparations on extracted mandibular first molar teeth immediately following a conventional instructional demonstration. Manual dexterity was evaluated using educator assessments and students' self‐preclinical scores in terms of cavity preparations. The post‐training questionnaire was administered to gather data on students' perceptions of their training experiences. Results: The CaviPrep group (Group 2) demonstrated significantly better performance in specific parameters, particularly cavity outline, retention form and cavity width, compared to the other groups ( p < 0.05). A statistically significant agreement was observed between self‐assessed and educator‐assessed preclinical scores across all evaluated criteria ( p < 0.05). Student feedback indicated that the CaviPrep training plate was favoured over the VR haptic simulator in terms of tactile realism, ease of use, manual skill improvement and overall training effectiveness. Conclusion: CaviPrep was associated with better performance in key operative parameters and was perceived as a more effective training tool for developing manual dexterity. While VR simulators offer valuable digital feedback and guided learning, their current limitations in haptic realism restrict their standalone effectiveness. Therefore, integrating VR systems with physical simulation models may provide a more effective and balanced approach for preclinical dental education.