Some scholars argue that students do not achieve higher level learning, or cognitive presence, in online courses. Online discussion has been proposed to bridge this gap between online and face-to-face learning environments. However, the literature indicates that the conventional approach to online discussion -asking probing questions -does not necessarily advance the discussion through the phases of cognitive presence: triggering events, exploration, integration and resolution, which are crucial for deep knowledge construction. Using mixed methods, we examined the contribution of four scenario-based online discussion strategies -structured, scaffolded, debate and role play -to the learners' cognitive presence, the outcome of the discussion. Learners' discussion postings within each strategy were segmented and categorized according to the four phases. The discussion strategies, each using the same authentic scenario, were then compared in terms of the number of segments representing these phases. We found that the structured strategy, while highly associated with triggering events, produced no discussion pertaining to the resolution phase. The scaffolded strategy, on the other hand, showed a strong association with the resolution phase. The debate and role-play strategies were highly associated with exploration and integration phases. We concluded that discussion strategies requiring learners to take a perspective in an authentic scenario facilitate cognitive presence, and thus critical thinking and higher levels of learning. We suggest a heuristic for sequencing a series of discussion forums and recommend areas for further related research.

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The use of artificial intelligence (AI) in patient care can offer significant benefits. However, there is a lack of independent evaluation considering AI in use. The paper argues that consideration should be given to how AI will be incorporated into clinical processes and services. Human factors challenges that are likely to arise at this level include cognitive aspects (automation bias and human performance), handover and communication between clinicians and AI systems, situation awareness and the impact on the interaction with patients. Human factors research should accompany the development of AI from the outset.

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