Results 191 to 200 of about 399,216 (332)
Risky or rigorous? Developing trustworthiness criteria for AI‐supported qualitative data analysis
Anatomical Sciences Education, EarlyView.
Michelle D. Lazarus +4 more
wiley +1 more source
Abstract Biomass gasification technology has been extensively researched around the world; however, there is a need to evaluate the current research landscape and evolutionary direction of research in the broader context of energy transition. A systematic bibliometric analysis of the Web of Science database was performed for articles that fall within ...
Olasunkanmi Opeoluwa Adeoye +5 more
wiley +1 more source
Impact of virtual avatar appearance realism on perceptual interaction experience: a network meta-analysis. [PDF]
Tao Z, Liu Y, Qiu J, Li S.
europepmc +1 more source
Abstract Aims In the context of pharmacology and toxicology education, there is a growing shift toward programmatic assessment models that prioritize longitudinal learning, reflection and development of higher‐order cognitive skills. As part of this transition, we are exploring alternative and more meaningful forms of assessment. This qualitative study
Narin Akrawi +2 more
wiley +1 more source
Medical students' perceptions of AI-based feedback and feedforward on communication skills in doctor-patient consultation - an acceptance study in a video-based simulation. [PDF]
Bauermann M +5 more
europepmc +1 more source
Within the ConcePTION project we set out to design two mother–infant pair studies collecting breast milk and plasma from the mother and plasma from the infant (for metformin and prednisolone) in order to demonstrate the premises and conditions for investigating potential drug transfer in association with breastfeeding.
Mats Hansson +11 more
wiley +1 more source
Social versus nonsocial visual cues of trustworthiness uniquely influence trust related behavior and memory. [PDF]
Schotz J, Lam T, Ebner NC, Lighthall NR.
europepmc +1 more source
Student perspectives on AI‐supported formative assessment in pharmacology
Abstract Aims High‐quality feedback is crucial for helping medical students understand and apply core concepts of pharmacology, yet personalized feedback is resource‐intensive to produce. Artificial intelligence (AI) offers a potential solution, but little is known about students' perspectives on AI‐generated feedback.
Jon Andsnes Berg +6 more
wiley +1 more source
Enhancing trustworthiness and integrity in research. [PDF]
Soer R.
europepmc +1 more source

