Course Craft

The rapid infusion of artificial intelligence into education has created a complex paradox. The same systems designed to enhance learning can also deepen inequity. This tension between innovation and justice requires more than technical skill. It calls for a form of literacy grounded in ethics and humanity. Baker and Hawn (2022) remind us that algorithmic bias is not simply a mathematical error. It reflects the values, omissions, and assumptions embedded within data and design. They emphasize that bias often begins with the data that algorithms consume rather than within the code itself. This insight has profound implications for educators. In nursing education, both human judgment and algorithmic evaluation influence how students are selected, supported, and assessed. Reflecting on my own teaching across post-secondary and clinical environments, I see that algorithmic bias is not a future concern. It is already shaping the way we teach, evaluate, and understand learners. From Awarene...

The integration of participatory culture and civic engagement frameworks into higher education signals a profound shift in how learning communities operate. As highlighted by McCorkle (2024), participatory learning transcends traditional classroom assignments and positions students as active contributors to public discourse. Similarly, Kahne, Hodgin, and Eidman-Aadahl (2016) assert that the digital age has redefined civic engagement, offering learners new ways to collaborate, investigate, and influence through participatory actions. Together, these frameworks suggest that the most transformative educational experiences are not those that reproduce knowledge, but those that empower learners to co-create meaning, act ethically in digital spaces, and cultivate agency within their professional and social communities. In the context of nursing education, these ideas resonate deeply. Nurses, both in practice and in training, operate at the intersection of knowledge, ethics, and human experie...

Generative Artificial Intelligence (GenAI) can transform teaching and learning in both academic and clinical nursing environments. Bouguettaya et al. (2025) describe GenAI as marking a paradigm shift from traditional digital learning toward personalized, adaptive, and generative experiences that respond to individual learner needs. In nursing education, this resonates with our movement toward competency-based learning and simulation-based assessment, where every learner’s path is unique but still anchored to clinical standards of practice. As an educator, I was particularly struck by how Li and Bertrand (2026) framed the Design Fiction Pedagogy (DFP) as a method for fostering critical and ethical thinking about technology. DFP encourages learners to imagine the future implications of AI through narrative and prototyping. Translating this into nursing, I could envision a learning activity where students design a “future clinical scenario” involving an AI diagnostic assistant. This would...

According to Touretzky et al. (2019), the AI for K-12 initiative emerged to ensure that all learners understand the principles, applications, and ethical dimensions of artificial intelligence (AI). The authors proposed five “Big Ideas” to guide AI education:  (1) computers perceive the world using sensors,  (2) agents maintain models of the world,  (3) computers learn from data,  (4) agents interact with humans, and  (5) AI has both positive and negative social impacts. These foundational concepts aim to cultivate digital citizens who understand the technologies shaping their lives and careers.  The framework emphasizes not only technical literacy but also ethical reasoning, fairness, and transparency.  These skills are essential for any profession reliant on data-driven systems. According to Li and Bertrand (2025), the Design Fiction Pedagogy (DFP) model represents an innovative approach to teaching AI that integrates speculative design and narrative ...