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An AI-based online learning platform improves blood cell morphology skills in medical laboratory technologists: a sequential explanatory study

Year: 2026

Type: article

Abstract: Blood cell morphology training requires repetitive, feedback-rich practice, whereas conventional continuing professional development (CPD) relies largely on static materials. Rigorous evidence for theory-informed, AI-supported skill-based CPD remains limited. We evaluated whether the RuiZhiXue (RZX) platform, informed by Vygotsky’s zone of proximal development (ZPD) and operationalised through intelligent distractors and real-time AI-generated probability feedback, improves morphology skills among medical laboratory technologists compared with conventional textbook- and atlas-based self-directed learning. We used an explanatory sequential mixed-methods design. Fifty-two medical laboratory technologists were randomised to RZX ( n = 26) or conventional textbook- and atlas-based self-directed learning ( n = 26) for two weeks. Parallel pretest and posttest assessments, each consisting of 50 image-based MCQs drawn from a validated test bank strictly separated from practice items, were auto-scored. Semi-structured interviews with 16 technologists and three mentors were subsequently analysed thematically to help interpret the quantitative findings. Both groups improved from pretest to posttest, but the RZX group achieved a significantly higher mean posttest score than the conventional self-directed learning group (86.62 vs. 64.77, p < 0.001), with a very large observed between-group effect size (Cohen’s d = 2.16). The RZX group also showed lower posttest-score variance ( p = 0.01), indicating more consistent learning outcomes. Participants described real clinical case content, repeatable practice, intelligent distractors, and real-time AI-generated probability feedback as key learning supports. Compared with conventional textbook- and atlas-based self-directed learning, the RZX platform was associated with significantly higher posttest scores and lower posttest-score variance in blood cell morphology training among medical laboratory technologists. These preliminary findings suggest that a theory-informed, AI-supported learning environment may offer a promising model for further evaluation in laboratory medicine CPD. Future research should examine the effectiveness of this approach in multicentre settings, assess longer-term and workplace-based outcomes, and evaluate cost-effectiveness and implementation barriers.

Source: BMC Medical Education

DOI: 10.1186/s12909-026-10188-9