California State University (CSU): Open Journal Systems
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The Variational Principle for Entropy of Countable State Shift Spaces with Specification
Full text linkWe generalize the well-studied specification property to countable-state shift spaces. We present an infinite class of examples of such spaces. Using a generalized notion of Gurevich entropy, we prove the variational principle for these spaces. This lays the foundation for developing the theory of equilibrium states in this setting
Developing Scientific Literacy and Research Skills in an Online Psychology Class
No full textStudents at community colleges have few opportunities for an undergraduate research experience. Compared to students who start at four-year institutions, two-year students are less likely to take a class where peer-reviewed articles are incorporated into the curriculum. As a result, these students often transfer into universities with a limited understanding of scientific literacy and research methodology. This study examined whether embedding peer-reviewed research articles and scaffolded assignments into an online General Psychology course at a large, diverse community college would improve students’ research skills and, by extension, their broader scientific literacy. Specifically, the study asked: Do students who participate in an embedded undergraduate research experience demonstrate measurable gains in their ability to interpret, evaluate, and use peer-reviewed research articles? The intervention consisted of four research-based lab reports and a guided instructional module, “How to Read a Research Article,” designed to help students develop the skills necessary to analyze scholarly sources. Results showed significant gains in students’ ability to interpret psychological research, particularly among students who began the course with lower baseline skills. Findings highlight the value of embedding course-based research opportunities into online community college classes to promote equity, access, and early development of scientific literacy
Balancing Wellness and Learning: Self-Care Practices for Educators
No full textExperiential education often requires educators to be deeply involved in the learning process with enhanced preparation, coordination, and logistical work, which may require significant time and effort. Faculty and staff development that includes self-care strategies to enhance their overall well-being is thus becoming crucial for experiential educators. By prioritizing self-care, educators can better navigate the demands of experiential education and maintain their well-being, ultimately benefiting themselves, their students, and their communities of interest. In this paper, readers will review self-care as a multifaceted phenomenon, explore self-care strategies, and learn to integrate them into their daily routines to build resilience.
Keywords: experiential education, faculty development, self-care, self-care strategie
Counselors Inspiring Hope in Higher Education
No full textThis reflective piece raises the voices of two counselors who are specs of hope at their campuses because of their commitment to advocate and serve their students. The first voice is from a counselor who opts to hold themselves accountable to be a source of community for colleagues and students. The second voice is a counselor who speaks to how the attacks on immigrant communities have provoked fear and how they have chosen to make sense of them.  
Polarization Microscopy: A Useful Tool for Biological Imaging
Full text linkPolarized light microscopy allows for detailed imaging of birefringent structures, such as cell membranes, that are not visible with bright-field imaging. The goal of this project was to view the structure of an onion skin using a polarization microscope. We built the microscope using a light source, condenser lens, 20× objective, and two polarizers. The setup produced about 33× magnification. Compared to bright-field imaging, the polarized image showed a darker background and clearer cell outlines
A Conceptual Model of Institutional Adaptation: Developmental Trajectories of AI Integration in Medical Education
Full text linkArtificial intelligence (AI) is increasingly reshaping how biomedical knowledge is accessed, synthesized, and applied, raising important questions about the competencies that medical education seeks to cultivate. Although often framed as unprecedented, these developments can be situated within a broader history of technological change in education, in which new tools have reconfigured relationships among knowledge, expertise, and learning. This study adopts a historical conceptual analyses approach to examine how medical education may respond to AI by drawing insights from an earlier technological transition: the introduction of handheld calculators in mathematics education. Using a structured comparative analysis informed by institutional adaptation theory, the study traces how educational systems have engaged with technological disruption over time, including patterns of institutional resistance, pedagogical repositioning, gatekeeping practices, curricular reconsideration, assessment debates, and governance responses. The calculator transition is considered not as a direct analogue to AI, but as a historical case that illuminates recurring dynamics of institutional change. The analysis informs the articulation of a developmental trajectory model that characterizes how medical education systems may adapt as AI becomes integrated into medical training and clinical learning environments. The model is presented as an interpretive framework for understanding institutional adaptation rather than as a predictive schema. Situating contemporary debates within broader historical patterns, the study offers a theoretically informed perspective on how medical education engages with technological change. It underscores the value of integrating historical insight with conceptual analysis to better understand ongoing transformations in professional education
Evaluating AI-Generated Feedback for Formative Assessment in Higher Education
Full text linkThe consistent provision of effective formative feedback on assessment is an integral part of the learning process, yet it is one of the most demanding and challenging aspects of learning and teaching, especially when required at scale. It is therefore unsurprising that amid the exploration of AI tools in teaching and learning, there is growing interest in the application of Large Language Models (LLMs) to support formative feedback in the learning process. Using student submissions from a course that included structured peer review activities, the study reported in this paper is a comparative analysis of AI-generated feedback and scoring (GPT-4). The findings show AI-generated feedback provides structured and detailed comments for assessment components based on clear and objective criteria, with strong agreement with human marking practices. Limitations appeared in areas requiring subjective judgment, such as evaluating the quality of peer reviews and the depth of student reflection. In these cases, human educators provide more nuanced interpretations based on contextual and pedagogical understanding. Findings put emphasis on the importance of human oversight for qualitative and interpretive evaluation. These findings suggest that a balanced human-AI approach, grounded in pedagogical intent and careful integration, is essential for the effective use of AI-assisted feedback in higher education
Critical Trust in Generative AI: Suspending Disbelief and Developing Critical Friend Groups Among University Students
Full text linkIn a 2025 international study involving 11,706 undergraduate students, 29% overall (67% in the U.S.) reported regular use of generative artificial intelligence (GenAI) programs. This could be problematic, since GenAI outputs often contain randomly parroted falsehoods, and students could overuse the technology. This manuscript proposes a new framework, critical trust, to help university students balance trust and mistrust in the validity of information in GenAI outputs. A critical trust framework could also help students make ethical decisions about how and when to use GenAI applications in their academic work. University students can employ critical trust in critical friend groups, which could be especially beneficial for students from historically marginalized communities without college-educated mentors, helping them avoid accusations of misuse of GenAI and the loss of credibility that can result from plagiarism. The critical trust framework could also provide education researchers with a pathway to develop and test a scale that measures critical trust, ultimately determining the optimal levels to minimize harms from GenAI hallucinations and inappropriate overuse
