RESUMO
Collaboration is key to STEM, where multidisciplinary team research can solve complex problems. However, inequality in STEM fields hinders their full potential, due to persistent psychological barriers in underrepresented students' experience. This paper documents teamwork in STEM and explores the transformative potential of computational modeling and generative AI in promoting STEM-team diversity and inclusion. Leveraging generative AI, this paper outlines two primary areas for advancing diversity, equity, and inclusion. First, formalizing collaboration assessment with inclusive analytics can capture fine-grained learner behavior. Second, adaptive, personalized AI systems can support diversity and inclusion in STEM teams. Four policy recommendations highlight AI's capacity: formalized collaborative skill assessment, inclusive analytics, funding for socio-cognitive research, human-AI teaming for inclusion training. Researchers, educators, and policymakers can build an equitable STEM ecosystem. This roadmap advances AI-enhanced collaboration, offering a vision for the future of STEM where diverse voices are actively encouraged and heard within collaborative scientific endeavors.
RESUMO
In a cross-over RCT, portable NIV (pNIV) reduced dynamic hyperinflation (DH) compared to pursed lip breathing (PLB) during recovery from intermittent exercise in COPD, but not consistently in all subjects. In this post-hoc analysis, DH response was defined as a reduction ≥4.5 % of predicted resting inspiratory capacity with pNIV compared to PLB. At exercise iso-time (where work completed was consistent between pNIV and PLB), 8/24 patients were DH non-responders (DH: 240 ± 40 mL, p = 0.001 greater using pNIV). 16/24 were DH responders (DH: 220 ± 50 mL, p = 0.001 lower using pNIV). Compared to DH responders, DH non-responders exhibited greater resting DH (RV/TLC: 65 ± 4% versus 56 ± 2%; p = 0.028) and did not improve exercise tolerance (pNIV: 30.9 ± 3.4 versus PLB: 29.9 ± 3.3 min; p = 0.603). DH responders increased exercise tolerance (pNIV: 34.9 ± 2.4 versus PLB: 27.1 ± 2.3 min; p = 0.001). Resting RV/TLC% was negatively associated with the magnitude of DH when using pNIV compared to PLB (r=-0.42; p = 0.043). Patients with profound DH were less likely to improve exercise tolerance with pNIV. Further studies using auto-adjusted ventilators are warranted.