Governors State University

Teachers consistently describe the development of expertise as central to their instructional success and long-term commitment to the profession. However, professional development and leadership structures often limit their ability to grow, influence colleagues, or shape schoolwide decisions. This mixed-methods study investigates the impact of current systems of professional learning and leadership on the empowerment of elementary teachers in suburban Chicago. Survey responses from twenty-six teachers and follow-up interviews with four participants reveal a central tension. Teachers are expected to differentiate instruction to meet the diverse needs of students, yet they often receive standardized professional development that disregards their experience, expertise, and classroom context. Teachers value learning that supports instructional improvement, but they report barriers that include limited implementation time, administrative demands, initiative fatigue, and the minimal relevance of externally delivered sessions. Using Bass and Riggio's transformational leadership framework, the analysis highlights how current systems fall short of individualized consideration, intellectual stimulation, and shared influence. Teachers propose differentiated professional development, teacher-led learning, protected collaboration time, and career pathways that enable advancement within the classroom. The study concludes that effective systems must recognize teachers as knowledgeable professionals and design professional learning and leadership structures that support autonomy, collaboration, and instructional impact.

The ideal Weyl "hydrogen-atom" semimetal exhibits only a single pair of Weyl nodes and no other trivial states at the Fermi energy. Such a material would be a panacea in the study of Weyl quasi-particles, allowing direct unambiguous observation of their topological properties. The alluaudite-like K2Mn3(AsO4)3 compound was recently proposed as such a material. Here, we use comprehensive experimental work and first-principle calculations to assess this prediction. We find K2Mn3(AsO4)3 crystallizes in the C2/c symmetry with a quasi-one-dimensional Mn sublattice, growing as small needle-like crystals. Bulk property measurements reveal magnetic transitions at approximate to 8 and approximate to 4 K, which neutron scattering experiments show correspond to two distinct magnetic orders, first a partially ordered ferrimagnetic k1 = (0, 0, 0) structure at 8 K and a second transition of k2 = (1, 0, 0) at 4 K to a fully ordered state. Below the second transition, both ordering vectors are necessary to describe the complex magnetic structure with modulated spin magnitudes. Both of the best-fit magnetic structures in this work are found to break the symmetry necessary for the generation of Weyl nodes, though one of the magnetic structures allowed by k1 does preserve this symmetry. However, the crystals are optically transparent and ellipsometry measurements reveal a large band gap, undermining expectations of semimetallic behavior. Density functional theory calculations predict an insulating antiferromagnetic ground state, in contrast to previous reports, and suggest potential frustration on the magnetic sublattice. Given the wide tunability of the alluaudite structure, we consider ways to push the system closer to a semimetallic state.

The study examines how generative artificial intelligence (GenAI) functioned as a coaching partner in three education preparation courses for future teachers and educational leaders. The courses included early childhood lesson planning, research writing in teacher preparation, and school improvement planning in educational leadership. A digital autoethnographic design guided the work, relying on student reflections, AI transcripts, and faculty reflections to understand how coaching interactions shaped thinking across programs. The design enabled instructors to examine their own positionalities while interpreting the digital records of student reasoning. Students reported gains in clarity, confidence, and alignment as they revised their work with AI supports. Several students noted that reflective questioning encouraged them to explain their decisions and refine their instructional or leadership plans. Others expressed caution when AI suggestions did not match their intentions or preferred frameworks. Faculty observed that the transcripts revealed misunderstandings and areas of growth that were not visible in traditional assignments. Recommendations for educator preparation emphasize the value of introducing AI coaching after students create their own drafts, requiring documentation of prompts, modeling reflective questioning, and preserving student agency. The study offers guidance for programs seeking to integrate GenAI as a reflective partner while supporting ethical engagement and professional judgment across the licensure spectrum.

IntroductionMale loneliness has become one of the most significant public health concerns in recent years, also referred to as an “epidemic.” Research demonstrates that men are at greater risk than women for severe health impacts resulting from loneliness; therefore, a gender-informed synthesis of patterns, mechanisms, and intervention gaps is needed to provide clarity.PurposeThis systematic review evaluates the current literature on loneliness among men in the United States. Specifically, this evaluation examines prevalence and temporal trends, gendered and life-course risk factors, health consequences, and the effectiveness of extant interventions addressing loneliness.MethodologyFollowing PRISMA recommendations, we searched PubMed/MEDLINE, CINAHL, PsycINFO, SCOPUS, and Google Scholar for peer-reviewed empirical studies and systematic reviews that included male-specific data. We identified 485 potential references through initial screening, then screened titles and abstracts, reviewed 262 full texts, and ultimately identified 30 core studies that met the inclusion criteria. To assess the quality of each article, we used the Newcastle-Ottawa Scale, the Cochrane Risk of Bias Tool, and AMSTAR-2.ResultsEvidence indicates that a widespread epidemic of male loneliness is absent, although specific groups are at higher risk. Younger males (ages 18–29) appear to be experiencing a sharp increase in loneliness, while older males (ages 60+) show more stable patterns. However, this trend is accompanied by a notable imbalance in the evidence base, with younger men underrepresented relative to older populations. Traditional masculine norms, relationship loss, unemployment, disabilities, and veteran status have all been identified as risk factors for male loneliness. Our research demonstrated that male loneliness is associated with serious health problems, including depression, cardiovascular disease, cognitive decline, risk of suicide, and increased mortality. While community-based programs targeted toward males show high participation, they are almost entirely under-evaluated.ConclusionMale loneliness is a patterned, life-course-related experience and should therefore be viewed as an ongoing process rather than a single “epidemic” event. Existing intervention strategies do not sufficiently consider how socialized gender processes influence men’s social withdrawal or disengagement. Future research may include conducting male-only randomized controlled clinical trials and longitudinal studies.Systematic review registrationhttps://osf.io/q97yv/, The review protocol was developed a priori and has been made publicly available through the Open Science Framework (OSF) to enhance transparency and reproducibility.

Artificial Intelligence (AI) is transforming education, workplaces, and daily life, yet adoption patterns vary significantly across generational cohorts. This study examines differences in AI acceptance using the AI-UTAUT framework, integrating constructs such as performance expectancy, trust, transparency, explainability, and ethical concerns. A mixedmethod design combined quantitative surveys $(n=209)$ and qualitative interviews across four cohorts: Baby Boomers, Generation X, Millennials, and Generation Z. Results reveal that Generation $\mathbf{Z}$ and Millennials exhibit higher adoption rates, driven by perceived usefulness and trust, while Generation $X$ and Baby Boomers demonstrate cautious engagement, emphasizing transparency and ethical safeguards. Ethical concerns, particularly job displacement and fairness, emerged as key barriers for earlier cohorts. The findings extend UTAUT by incorporating AI-specific variables and highlighting the moderating effects of generation. Practical implications include the development of tailored training programs, implementation of institutional transparency measures, and intergenerational AI literacy initiatives to reduce digital inequality. This research contributes to theory and practice by offering actionable strategies for inclusive and responsible AI integration.