Enhancing teaching and learning in health sciences education through the integration of Bloom's taxonomy and artificial intelligence - Hachoumi, Nadia; Eddabbah, Mohamed; El adib, Ahmed Rhassane (Informatics and Health, 2025)
The purpose of this research is to integrate artificial intelligence (AI) in Bloom's Taxonomy with the aim of improving critical thinking in health science education. Innovative teaching approaches will be employed for the broad spectrum of students; integrating AI across all levels of Bloom's processes-from production, comprehension, application, clinical case scenario harbingers, dynamic information delivery, and interactive teaching. Mixed-method design comprising both the quantitative (survey based analysis) and the qualitative (far-reaching theoretical framework and case studies review) methodologies was used in the current study. A structured survey was administered to collect data on the AI pattern, efficacy, and frequency of usage among 181 health sciences students. Case studies in practice were also used to serve as evidence of AI's role in medical training: from clinician-initiated simulations to AI-powered assessment tools. The data analysis approaches included descriptive statistics, correlation heat maps, and comparative analysis between AI-assisted teaching and standard teaching. It is concluded that such engagement of the use of AI makes for engagement and deep learning but requires strong institution-wide foundations and ethical frameworks, and measures of growth against institutional preparedness and of having a very rich ethical framework for risk management are still lacking. Hence, the study recommends that balancing technology with ethics would be needed in AI integration and proposes future studies to address gaps in teacher training, institution preparedness, and ethical consideration. •Innovative use of Bloom's taxonomy and AI.•Addresses educational deficiencies.•Promotes personalized learning.•Encourages critical thinking using AI-powered ways.