Large Language Models in Medical and Dental Education: A Cross-Sectional Comparison of AI-Generated and Faculty-Authored Prosthodontic Materials

Background/Objectives: This study aimed to compare AI-generated educational material with faculty-authored content in Dental Prostheses Technology, evaluating perceived clarity, accuracy, structure, usefulness, and overall instructional quality across different age and professional groups. Methods: An analytical cross-sectional study was conducted using two versions of the first three chapters of a prosthodontics textbook: the original faculty-authored text and a reformulated version generated by ChatGPT 5.2 (OpenAI). Images were removed and formatting standardized to ensure a text-only comparison. An anonymized online questionnaire based on a five-point Likert scale assessed clarity, accuracy, readability, usefulness and structure. To reduce potential bias, participants were unaware of the authorship of the evaluated materials (human-authored vs. AI-generated). A total of 130 participants independently reviewed both documents. Data were analyzed using Wilcoxon signed-rank, Mann–Whitney U, and Friedman tests. Results: Both materials received favorable evaluations across all dimensions. The AI-generated version demonstrated a statistically significant advantage in clarity (Z = −2.107, p = 0.035; r = 0.19), while no significant differences were observed for structure, accuracy, readability, or usefulness. Generational differences emerged: younger participants valued improved clarity but reported reduced usefulness, mid-career participants showed the greatest improvement in perceived accuracy, and senior professionals reported substantial gains in usefulness and readability. Conclusions: AI-generated educational material demonstrates pedagogical equivalence to faculty-authored content, with clarity representing its principal advantage. Large language models may serve as effective complementary tools in dental education, particularly for restructuring complex content.