The landscape of surgical training is rapidly evolving with the advent of artificial intelligence (AI) and its integration into education and simulation. This manuscript aims to explore the potential applications and benefits of AI-assisted surgical training, particularly the use of large language models (LLMs), in enhancing communication, personalizing feedback, and promoting skill development. We discuss the advancements in simulation-based training, AI-driven assessment tools, video-based assessment systems, virtual reality (VR) and augmented reality (AR) platforms, and the potential role of LLMs in the transcription, translation, and summarization of feedback. Despite the promising opportunities presented by AI integration, several challenges must be addressed, including accuracy and reliability, ethical and privacy concerns, bias in AI models, integration with existing training systems, and training and adoption of AI-assisted tools. By proactively addressing these challenges and harnessing the potential of AI, the future of surgical training may be reshaped to provide a more comprehensive, safe, and effective learning experience for trainees, ultimately leading to better patient outcomes. .

Software assistants have significantly impacted software development for both practitioners and students, particularly in capstone projects. The effectiveness of these tools varies based on their knowledge sources; assistants with localized, domain-specific knowledge may have limitations, while tools like ChatGPT, using broad datasets, might offer recommendations that do not always match the specific objectives of a capstone course. Addressing a gap in current educational technology, this paper introduces an AI Knowledge Assistant specifically designed to overcome the limitations of existing tools by enhancing the quality and relevance of Large Language Models (LLMs). It achieves this through the innovative integration of contextual knowledge from a local “lessons learned” database tailored to the capstone course. We conducted a study with 150 students using the assistant during their capstone course. Integrated into the Kanban project tracking system, the assistant offered recommendations using different strategies: direct searches in the lessons learned database, direct queries to a GPT model, query enrichment with lessons learned before submission to GPT and LLaMa models, and query enhancement with Stack Overflow data before GPT processing. Survey results underscored a strong preference among students for direct LLM queries and those enriched with local repository insights, highlighting the assistant's practical value. Further, our linguistic analysis conclusively demonstrated that texts generated by the LLM closely mirrored the linguistic standards and topical relevance of university course requirements. This alignment not only fosters a deeper understanding of course content but also significantly enhances the material's applicability to real-world scenarios.

This article presents a controlled case study focused on implementing and using Generative Artificial Intelligence (AI), specifically Large Language Models (LLM) in physiotherapy education to assist instructors with formulating effective technology-mediated feedback for students. It outlines how these advanced technologies have been integrated into an existing feedback-oriented platform to guide instructors in providing feedback inputs and establish a reference framework for future innovations in practical skills training for health professions education. Specifically, the proposed solution uses LLM to automatically evaluate feedback inputs made by instructors based on pre-defined and literature-based quality criteria and generates actionable textual explanations for reformulation. In addition, if the instructor requires, the tool supports summary generation for large sets of text inputs to achieve better student reception and understanding. The case study describes how these features were integrated into the feedback-oriented platform, how their effectiveness was evaluated in a controlled setting with documented feedback inputs, and the results of its implementation with real users through cognitive walkthroughs. Initial results indicate that this innovative implementation holds great potential to enhance learning and performance in physiotherapy education and has the potential to expand to other health disciplines where the development of procedural skills is critical, offering a valuable tool to assess and improve feedback based on quality standards for effective feedback processes. The cognitive walkthroughs allowed us to determine participants' usability decisions in the face of these new features and to evaluate the perceived usefulness, how this would integrate into their workload, and their opinion regarding the potential for the future within this teaching strategy. The article concludes with a discussion of the implications of these findings for practice and future research directions in this developing field.

Knowledge management (KM) is a key element for the development of small-to-medium enterprises (SMEs) in the construction industry. This is particularly relevant in Chile, where this industry is composed almost entirely of SMEs. Although various KM system proposals can be found in the literature, they are not suitable for SMEs, due to usability problems, budget constraints, and time and connectivity issues. Mobile Cloud Computing (MCC) systems offer several advantages to construction SMEs, but they have not yet been exploited to address KM needs. Therefore, this research is aimed at the development of a MCC-based KM platform to manage lessons learned in different construction projects of SMEs, through an iterative and user-centered methodology. Usability and quality evaluations of the proposed platform show that MCC is a feasible and attractive option to address the KM issues in SMEs of the Chilean construction industry, since it is possible to consider both technical and usability requirements.