Background: STEM minority participation programs have been widely implemented in higher education with the goal of diversifying the global STEM workforce. Informed by research highlighting the potential of targeted exploration of STEM roles and reflection on theselfin relation to STEM (identity exploration), this work examines how engagement in a government funded STEM minority participation program shaped these processes in current students and program alumni.Results: Epistemic network analysis (ENA) was used to visualize conceptual connections between identity themes that emerged from interviews with present and past program participants. Network models were developed for current students and alumni for cross-group comparisons. Differences were found in how participants at different stages of their careers enact and describe their identity exploration processes. Summative network models highlighted how students discussed action-taking (sometimes through participation in STEM minority program initiatives) as they explored less-certain possible future STEM roles, while alumni integrated more diverse and holistic facets of their identities when conceptualizing their futures. To close the interpretive loop, a qualitative interpretation of interview discourse was used to give context to network patterns.Conclusions: Results highlight the differences between novices’ and professionals’ conceptualizations of their future selves and illustrate how minoritized individuals describe their long-term patterns of identity exploration related to STEM majors and careers. Implications for future STEM identity research and practice, including higher education programming as a tool to support students’ STEM identity exploration processes, are discussed.

Generative artificial intelligence (GenAI) tools developed to support teaching and learning are widely available. Trustworthiness concerns, however, have prompted calls for researchers to study their effectiveness and for educators and educational researchers to be involved in their creation and piloting processes. This study investigated one type of GenAI created to support educators: multiple-choice question generators (MCQ GenAI). Among the nine MCQ GenAI tools investigated, a variety of useful options were available, but only one indicated teacher involvement and none mentioned testing experts in development processes. MCQ GenAI-created items (n = 270) were coded based on MCQ quality item-writing guidelines. Results showed 80.00% of items (n = 216) violated at least one guideline, with 73.70% (n = 199) likely to produce major measurement error (should not use without revision), 6.30% (n = 17) likely to elicit minor measurement error (consider modifying), and 20.00% (n = 54) acceptable (usable as created). Implications suggest multidisciplinary teams are needed in educational GenAI tool development.

Prior to 2020, empirical research and reports on approaches to education during health crises were limited. They focused primarily on reporting local-level response and provided only limited analysis. Various historic epidemics, like SARS, Ebola, and HIV/AIDS, provided important lessons about educational efficacy during major health emergencies. However, the sudden emergence of the COVID-19 pandemic led to an explosion of research on educating during a worldwide health crisis. This integrative literature review (Torraco 2005) uses the INEE Minimum Standards framework to conceptualize the response to pandemics and epidemics from 1990 to 2020. The research analyzes 124 empirical studies, practitioner and governmental reports, and historic accounts of Ebola, SARS, and other epidemics, as well as early responses to COVID-19, in order to understand how education stakeholders continued educating during widespread communicable illnesses. The high-level themes that emerged included the foundational role of context and community support; access to an equitable education in the digital age; the social-emotional wellbeing of teachers and students; teachers' role in adapting curriculum and pedagogy; the need for additional training and support for teachers; and the opportunity for a creative shift in practices and policies in education.

Educators have increasingly turned to social media for their instructional, social, and emotional needs during the COVID-19 pandemic. In order to see where support and professional development would be needed and how the educational community interacted online, we sought to use existing Twitter data to examine potential educators’ networking and discourse patterns. Specifically, this mixed-methods study explores how educators used Twitter as a platform to seek and share resources and support during the transition to remote teaching around the start of massive school closures due to the pandemic. Based on a public COVID-19 Twitter chatter database, tweets from late March to early April 2020 were searched using educational keywords and analyzed using social network analysis and thematic analysis. Social network analysis findings indicate that the support networks for educators on Twitter were sparse and consisted of mainly small, exclusive communities. The networks featured one-on-one interactions during the early pandemic, highlighting that there were few large conversations that most educators were part of but rather many small ones. Thematic analysis findings further suggest that both informational and nurturant support were relatively equally present on Twitter among educators, particularly pedagogical content knowledge and gratitude. This study adds to an understanding of the educational networks as a means of professional and personal support. Additionally, findings present the discourse featured in educator networks at the onset of an educational emergency (i.e., COVID-19) as decentralized as well as desiring pedagogical content knowledge and emotional sharing.