Life Pacific College

Abstract This chapter explores the design and implementation of AI course assistants as a strategy to increase equity, engagement, and academic success in online higher education. At Los Angeles Pacific University, a fully online institution serving adult and historically underserved learners, an AI assistant named Spark was deployed across all courses within four weeks following a successful pilot study. The implementation process included a multi-phase rollout, beginning with faculty-led dialog and experimentation, followed by the development of a minimum viable product and continuous improvement based on student and faculty feedback. Spark was designed to support student learning through Socratic questioning, course-specific alignment, and inclusive interaction models that accommodated diverse learners, including those with language or neurodiversity needs. Outcomes from a randomized controlled trial and follow-up studies indicated improvements in GPA, motivation, and self-efficacy, with no demographic disparities in benefit. The chapter also examines how Spark was used to enable active learning through tools such as AI-facilitated think–pair–share discussions and role-playing simulations. Ethical and inclusive design practices were prioritized throughout the development process, and faith-informed considerations were integrated to reflect the institution’s Christian values. Finally, the chapter connects the case study to global priorities in education by aligning outcomes with the goals of inclusive, high-quality learning outlined in Sustainable Development Goal 4. Practical recommendations are provided to help institutions of all types responsibly scale AI tools that support learner success while maintaining human-centered values.

Gliomas represent the most frequent primary brain tumors in adults and are marked by considerable molecular and clinical heterogeneity. The 2021 WHO classification of central nervous system tumors identifies three major adult-type diffuse glioma subtypes: astrocytoma IDH-mutant, oligodendroglioma IDHmutant with 1p/19q-codeletion, and glioblastoma IDH-wildtype.1 Despite ongoing advances, treatment resistance and poor prognosis remain major challenges, largely due to intratumoral heterogeneity and the complexity of the tumor microenvironment.2 A promising yet underexplored avenue for patient-centered glioma profiling is the tumor secretome, which includes soluble factors, extracellular vesicles, and metabolites released by both neoplastic and stromal cells.3 Existing studies rely on in vitro models that fail to recapitulate the complexity of tumor-host interactions. To bridge this gap, we established an innovative, standardized ex vivo protocol for secretome isolation directly from glioma tissue. The protocol preserves microenvironmental cues from the operating room to laboratory analysis, ensuring spontaneous secretome release under controlled conditions. Feasibility and performance were assessed in a preliminary cohort of seven adult diffuse glioma patients. Transmission electron microscopy confirmed tissue structural integrity. Proteomic profiling identified 84 cancer-associated proteins within the secretome. Additionally, metabolomic analysis revealed a distinctive signature of small molecules. Notably, glioblastoma IDH-wildtype samples exhibited increased lactate levels, consistent with hypoxia-driven metabolism, and decreased N-acetylaspartate, indicating neuronal loss. Overall, this ex vivo secretome protocol offers a robust and physiologically relevant platform for glioma characterization, with potential applications in biomarker discovery, patient stratification, and the development of personalized therapeutic strategies.

The integration of artificial intelligence (AI) in education has demonstrated significant potential in enhancing learning experiences, yet many students choose to not engage with AI tools available within their courses. This mixed-methods exploratory study investigates the barriers to the adoption of Spark, the AI course assistant implemented at Los Angeles Pacific University (LAPU). Analyzing 602 student survey responses collected through End-of-Course (EOC) evaluations, this study investigates the reasons behind this non-utilization, utilizing combined sentiment analysis with thematic analysis. The findings reveal that perceived necessity, lack of familiarity, and minimal awareness of Spark are primary factors contributing to non-use, alongside external issues such as time constraints and preference for traditional study methods. Notably, the absence of negative sentiment suggests that resistance is not rooted in dissatisfaction with the tool itself, but in limited perceived value or awareness. The study highlights the critical role of communication strategies, faculty engagement, and integration of AI tools into coursework to improve adoption. Addressing these barriers through improved communication, faculty-led AI integration, and targeted training initiatives can significantly enhance student engagement and learning outcomes. Without intervention, non-utilization may limit students’ opportunity to build foundational AI literacy—an increasingly important competency in academic and professional environments shaped by AI technologies. These insights offer actionable guidance for educators and institutions seeking to maximize the benefits of AI powered learning tools, and foster AI literacy among students in the evolving technological landscape.

En febrero de 1955, el etnólogo mexicanista y político Jacques Soustelle fue nombrado Gobernador General de Argelia por un año, al comienzo del período que se convertiría en la Guerra de Argelia. Diseñó e implementó una serie de reformas llamadas “Plan Soustelle”, en parte inspiradas en el modelo mexicano de integración y el indigenismo que había observado durante sus investigaciones etnográficas, en México, a principios de la década de treinta. Tras recordar los análisis de Soustelle sobre la política indigenista mexicana, el artículo repasa algunas de las medidas emblemáticas de su plan que tienen sus raíces en la ingeniería social indigenista.

This chapter states that criteria for art evaluation today is particular to the judging community. Since it is the arbitrating community that extends its own history and fosters its own narrative, it also propagates the rules of engagement for any artifact that relates to the community’s guiding narrative. A person’s experiences are grafted into a community’s guiding narrative through what Paul Ricoeur calls “emplotment.” Experiences are essentially emplotted into a greater narrative, and this emplotment gives the experience both context and meaning. These experiences can be understood and later judged through hermeneutical methods. This chapter argues that art in a narrative-hermeneutical theory of art is subjected to formal, historical, presentational, and social impact analyses. These hermeneutical methods are universal for classifying, understanding, and evaluating artworks on their own narrative terms.

With the expanding growth of Arabic electronic data on the web, extracting information, which is actually one of the major challenges of the question-answering, is essentially used for building corpus of documents. In fact, building a corpus is a research topic that is currently referred to among some other major themes of conferences, in Natural Language Processing (NLP), such as, Information Retrieval (IR), Question-Answering (QA), Automatic Summary (AS), etc. Generally, a question-answering system provides various passages to answer the user questions. To make these passages truly informative, this system needs access to an underlying knowledge base; this requires the construction of a corpus. The aim of our research is to build an Arabic question-answering system. In addition, analyzing the question must be the first step. Next, it is essential to retrieve a passage from the web that can serve as an appropriate answer. In this paper, we propose a method to analysis the question and retrieve the passage answer in the Arabic language. For the question analysis, five factual question types are processed. Additionally, our purpose is to experiment with the generation of a logic representation from the declarative form of each question. Several studies, deal with the logic approaches in question-answering, are discussed in other languages than the Arabic language. This representation is very promising because it helps us later in the selection of a justifiable answer. The accuracy of questions that are correctly analyzed and translated into the logic form achieved 64%. And then, the results of passages of texts that are automatically generated achieved an 87% score for accuracy and a 98% score for c@1.

The Division of Lung Diseases of the NHLBI and the Cardiovascular Medical Education and Research Fund held a workshop to discuss how to leverage the anticipated scientific output from the recently launched "Redefining Pulmonary Hypertension through Pulmonary Vascular Disease Phenomics" (PVDOMICS) program to develop newer approaches to pulmonary vascular disease. PVDOMICS is a collaborative, protocol-driven network to analyze all patient populations with pulmonary hypertension to define novel pulmonary vascular disease (PVD) phenotypes. Stakeholders, including basic, translational, and clinical investigators; clinicians; patient advocacy organizations; regulatory agencies; and pharmaceutical industry experts, joined to discuss the application of precision medicine to PVD clinical trials. Recommendations were generated for discussion of research priorities in line with NHLBI Strategic Vision Goals that include: (1) A national effort, involving all the stakeholders, should seek to coordinate biosamples and biodata from all funded programs to a web-based repository so that information can be shared and correlated with other research projects. Example programs sponsored by NHLBI include PVDOMICS, Pulmonary Hypertension Breakthrough Initiative, the National Biological Sample and Data Repository for PAH, and the National Precision Medicine Initiative. (2) A task force to develop a master clinical trials protocol for PVD to apply precision medicine principles to future clinical trials. Specific features include: (a) adoption of smaller clinical trials that incorporate biomarker-guided enrichment strategies, using adaptive and innovative statistical designs; and (b) development of newer endpoints that reflect well-defined and clinically meaningful changes. (3) Development of updated and systematic variables in imaging, hemodynamic, cellular, genomic, and metabolic tests that will help precisely identify individual and shared features of PVD and serve as the basis of novel phenotypes for therapeutic interventions.