AI as a pedagogical partner: enhancing art history education and quality assurance through AI-generated critiques

Purpose This study aims to examine the effectiveness of AI-generated critiques using DALL·E 3 in improving visual analysis skills, engagement and learning assurance in art history education compared with traditional methods. Design/methodology/approach A quasi-experimental, mixed-methods design was employed with 80 undergraduate art history students in Kuwait. Quantitative data were collected through pre-test/post-test assessments and rubric-based evaluations, while qualitative insights were gathered via surveys and focus groups to explore student perceptions of AI-assisted critique. Findings AI-assisted learning significantly enhanced students’ ability to analyze composition, brushstrokes and color palettes, with the experimental group achieving higher rubric scores. However, AI struggled to interpret symbolism and historical context, highlighting the need for educator oversight to ensure academic quality assurance. Research limitations/implications The study’s focus on Kuwait may limit generalizability, and results are specific to DALL·E 3. Future research should explore the role of AI in diverse cultural settings, its long-term implications for quality assurance, and strategies to improve contextual accuracy. Ethical considerations, such as bias and cultural preservation, must also be addressed. Originality/value This study provides empirical evidence of AI’s strengths and limitations in art critique, offering a structured framework for using tools like DALL·E 3 to enhance engagement and learning assurance. It underscores the need for human oversight and ethical integration, with broader implications for quality assurance in education.