University of the Immaculate Conception

In the United States, pancreatic cancer ranks in third place amongst all cancers, with a very low survival rate of 20

Research in artificial intelligence is shifting from model innovations and benchmark scores towards problem definition and rigorous real-world evaluation. As the field enters the "second half," the central challenge becomes real utility in long-horizon, dynamic, and user-dependent settings such as agentic coding, deep research, and computer use, where LLM-based agents face context explosion beyond fixed context windows and must continuously accumulate, manage, and selectively reuse information across extended interactions. Memory, with hundreds of papers released in 2025, therefore emerges as the critical solution to fill this utility gap. Beyond passive storage, memory is increasingly the substrate through which agents self-evolve: short-term memory gates which experiences are perceived and abstracted during execution, while long-term memory consolidates them into reusable knowledge and skills, forming the loop through which agents improve from their own experience. In this survey, we provide a unified view of foundation agent memory along three dimensions: memory substrate (internal parametric state and external retrieval-augmented stores), cognitive mechanism (sensory, working, episodic, semantic, and procedural), and memory subject (user-centric personalization and agent-centric experience). We then analyze how memory is operated under single- and multi-agent topologies and highlight learning policies over memory operations, showing how memory management itself is becoming a trainable capability spanning reinforcement-learned context curation, experience consolidation at decision time, and the emerging ecosystem of portable, shareable agent skills. Finally, we review evaluation benchmarks and metrics for memory utility, and outline open challenges and future directions.

The primary goal of the University of the Immaculate Conception is to produce competent graduates who can help transform the society. The competence of the UIC graduates could be manifested in their employability mainly on their field of expertise. Hence, this research was designed to gather information on the employability of the UIC’s Accountancy graduates. The survey was conducted in a nationwide scope using the social media network like Facebook, personal distribution, and short messaging approaches. Results show that 75% of the respondents are Certified Public Accountants; 79% was employed with 26% landing on their first job in less than a month after submitting their application; and a majority (56%) is employed with the commerce and industry sector. The primary reason of changing job is for economic – better salary scale. On the other hand, the abilities identified by the respondents useful in their job include: the skills on entrepreneurial, human resource, communication, critical thinking, information technology and problem-solving.

Knowledge distillation from Large Language Models (LLMs) to smaller models has emerged as a critical technique for deploying efficient AI systems. However, current methods for distillation via synthetic data lack pedagogical awareness, treating knowledge transfer as a one-off data synthesis and training task rather than a systematic learning process. In this paper, we propose a novel pedagogically-inspired framework for LLM knowledge distillation that draws from fundamental educational principles. Our approach introduces a three-stage pipeline—**Knowledge Identifier**, **Organizer**, and **Adapter** (**IOA**)—that systematically identifies knowledge deficiencies in student models, organizes knowledge delivery through progressive curricula, and adapts representations to match the cognitive capacity of student models. We integrate Bloom's Mastery Learning Principles and Vygotsky's Zone of Proximal Development to create a dynamic distillation process where student models approach teacher model's performance on prerequisite knowledge before advancing, and new knowledge is introduced with controlled, gradual difficulty increments. Extensive experiments using LLaMA-3.1/3.2 and Qwen2.5 as student models demonstrate that IOA achieves significant improvements over baseline distillation methods, with student models retaining 94.7\% of teacher performance on DollyEval while using less than 1/10th of the parameters. Our framework particularly excels in complex reasoning tasks, showing 19.2\% improvement on MATH and 22.3\% on HumanEval compared with state-of-the-art baselines.

BACKGROUNDS:Artificial intelligence (AI) is rapidly reshaping nursing education by transforming teaching strategies, student engagement, and clinical learning. Despite its growing influence, there is limited evidence exploring how nurse educators experience and navigate the integration of AI, particularly within the Philippine academic context. AIM:To explore the lived experiences of nurse educators integrating AI in classroom and clinical instruction, focusing on adaptation processes, perceived benefits, ethical considerations, and evolving professional roles. DESIGN:Descriptive phenomenological study grounded in Husserlian philosophy. METHODS:Thirteen nurse educators with direct experience in AI-related teaching were purposively selected from nursing schools across the Philippines. Semi-structured interviews were conducted face-to-face and online. Data were analyzed using Colaizzi's seven-step method. Trustworthiness was ensured through bracketing, member checking, reflexive journaling, and audit trails. RESULTS:Six themes emerged: (1) navigating AI adoption and readiness; (2) transforming pedagogy through AI; (3) reimagining nurse educators' identity; (4) adaptive practices and institutional support; (5) ethical stewardship and nursing values; and (6) human-technology partnership for the future. Educators perceived AI as a transformative yet ethically sensitive tool that enhances teaching efficiency, supports personalized learning, strengthens student engagement, and reshapes their professional roles. However, they emphasized the need for institutional readiness, faculty development, and clear guidelines to ensure the responsible and value-aligned use of AI. CONCLUSIONS:AI integration redefines nursing education by fostering innovation, adaptability, and reflective teaching. Responsible adoption necessitates human-centered approaches, robust ethical safeguards, and organizational support. IMPLICATIONS FOR NURSING EDUCATION:Developing structured AI policies, providing continuous faculty training, and aligning technological integration with nursing values may promote safe, ethical, and effective AI-enhanced pedagogy in both classroom and clinical settings.