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Remediation has emerged as a pivotal component of modern nursing education, serving as an essential bridge between academic instruction and clinical practice. Far beyond a reactive intervention for struggling students, remediation is a proactive, structured, and student-centered approach that fosters a growth mindset, supports clinical judgment development, and cultivates resilience. When thoughtfully integrated into the nursing curriculum, remediation contributes meaningfully to both short-term academic achievement and long-term professional competence. It prepares nursing students to meet the demands of an increasingly complex and dynamic healthcare environment.

Remediation – a continuous cycle of learning

At its core, remediation involves a continuous cycle of self-assessment, targeted learning, clinical application, and reflective practice. This iterative process reinforces deep learning while encouraging students to take ownership of their educational journey. The initial stage centers on self-assessment, which enables students to identify areas of weakness or knowledge gaps. Using formative assessment tools, such as quizzes, reflective writing, and concept mapping, students are guided to recognize their learning needs. These insights are then organized thematically, allowing for strategic prioritization and individualized learning goals.

To address these identified gaps, students are encouraged to engage with a variety of reputable resources, including course resources, peer-reviewed journal articles, and clinical decision-making tools like Lippincott® Advisor. This multifaceted approach to content acquisition supports diverse learning preferences and promotes evidence-based practice. Importantly, students are also taught to employ metacognitive strategies such as self-regulation, elaboration, and critical reflection. By documenting their learning processes and monitoring their progress, students enhance their ability to think critically and develop habits associated with lifelong learning.

The strength of remediation lies in its cyclical nature. After acquiring new knowledge, students are expected to apply their learning in realistic, practice-based settings. Clinical simulations, patient case studies, and scenario-based discussions serve as effective mechanisms for translating theoretical knowledge into practical skills. These experiences allow students to refine their clinical judgment, identify residual gaps in understanding, and solidify their competency. Following each application experience, students engage in reflective exercises that prompt them to evaluate their performance, integrate feedback, and adapt their strategies accordingly. This continuous loop of learning and reflection promotes mastery and supports long-term retention.

A targeted, data-driven approach to reviewing content is a distinguishing feature of effective remediation. Rather than revisiting all instructional material indiscriminately, students are guided to focus on specific content areas where deficits remain. The use of academic performance analytics and diagnostic tools enhances this process by providing actionable insights into students’ strengths and weaknesses. Faculty play a critical role in interpreting this data and coaching students through personalized learning plans. Additionally, the inclusion of diverse content modalities such as infographics, podcasts, interactive modules, and visual case studies serves to increase engagement and accommodate varied learning styles.

Assessing applied learning

Equally vital to the success of remediation is the integration of applied learning opportunities. Encouraging students to use newly acquired knowledge in complex clinical scenarios facilitates higher-order thinking and supports the development of clinical decision-making skills. Strategies such as case-based learning, team-based simulations, and unfolding patient scenarios have consistently demonstrated improved learning outcomes and deeper engagement. Without this application phase, remediation risks becoming a passive exercise, potentially leaving critical knowledge gaps unresolved.

Assessment following remediation serves a dual purpose: it provides an opportunity for students to retrieve and apply knowledge in a meaningful context, and it offers faculty a chance to evaluate the effectiveness of the remediation process. Programs that incentivize thorough remediation — for example, by awarding academic credit or offering second-chance assessments — motivate students to invest in their learning. Research indicates that assigning targeted remediation activities to students who score below a certain threshold, followed by opportunities for reassessment, leads to improved academic performance and increased confidence.

An emerging and transformative element in contemporary remediation practices is the integration of artificial intelligence (AI). AI-powered tools, such as Lippincott® Ready for NCLEX®, offer personalized learning pathways that adapt dynamically to each student’s performance metrics. These platforms provide rich, multimodal content ranging from adaptive quizzes to interactive case scenarios that support knowledge reinforcement, critical thinking, and skill application. Students identified as at-risk are offered expanded remediation content, including scenario-based exercises and targeted feedback loops. By utilizing AI and a research-backed remediation process that includes assessing, reviewing, applying, and reflecting, educators can make remediation both individualized and scalable, ultimately increasing its effectiveness and accessibility.

The positive impact of remediation on nursing education

Remediation in nursing education should be conceptualized not as a punitive or remedial measure, but as a forward-looking, empowering strategy that supports the holistic development of nursing students. By fostering self-directed learning, encouraging targeted review, facilitating clinical application, and promoting reflective practice, remediation prepares students to become safe, effective, and adaptable healthcare professionals. As nursing education continues to evolve in response to technological advancements and changing patient needs, the integration of data-informed strategies and AI-enhanced platforms will further elevate the role of remediation. Ultimately, well-executed remediation fosters a culture of excellence and accountability, ensuring that every student is equipped to contribute meaningfully to patient care and the healthcare system at large.

OpenAI said Monday it is working with chipmaker Broadcom to design its own artificial intelligence computer chips.

The two California companies didn’t disclose the financial terms of the deal but said they will start deploying the new racks of customized “AI accelerators” late next year.

It’s the latest big deal between OpenAI, maker of ChatGPT, and the companies building the chips and data centers required to power AI.

OpenAI in recent weeks has announced partnerships with chipmakers Nvidia and AMD that will supply the AI startup with specialized chips for running its AI systems. OpenAI has also made big deals with Oracle, CoreWeave and other companies developing the data centers where those chips are housed.

Many of the deals rely on circular financing, in which the companies are both investing in OpenAI and supplying the world’s most valuable startup with technology, fueling concerns about an AI bubble. OpenAI doesn’t yet turn a profit but says its products now have more than 800 million weekly users.

“What’s real about this announcement is OpenAI’s intention of having its own custom chips,” said analyst Gil Luria, head of technology research at D.A. Davidson. “The rest is fantastical. OpenAI has made, at this point, approaching $1 trillion of commitments, and it’s a company that only has $15 billion of revenue.”

OpenAI CEO Sam Altman said the work with Broadcom to develop a custom chip began about 18 months ago. Broadcom also works with other leading AI developers, including tech giants Amazon and Google.

Altman said on a podcast announcing the deal that the computing power made possible through the Broadcom partnership will amount to 10 gigawatts, which he described as “a gigantic amount of computing infrastructure to serve the needs of the world to use advanced intelligence.”

Broadcom shares surged more than 9% on Monday.

Broadcom CEO Hock Tan said on the same podcast that OpenAI needs more computing capacity as it progresses toward a “better and better frontier model and towards superintelligence.”

“If you do your own chips, you control your destiny,” he added.