644 research outputs found
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Interactivity, computers and orthodontic training for undergraduates
This phenomenological study investigates the interactivity taking place when students use computer-assisted-learning (CAL) in orthodontics and what can be inferred about why these interactions occur. CAL has been proposed in orthodontics because it provides an opportunity to follow a case through to completion. This training is needed if only to give all dentists sufficient knowledge to identify and refer cases for treatment. Two programs have been developed for the pilot: an introductory e-book and a narrative case study based on real records that takes students through a series of decisions relating to case assessment, treatment planning and appliance design.The mixed-methodology approach of the main study uses activity theory to provide a. framework combining qualitative and quantitative data to analyse the interactivity of 48 students as they work through the case study. Observations and transcripts of recordings of conversations between pairs of students, together with post-session interviews, facilitate a deeper understanding of students' conceptions of orthodontics particularly when they explain their reasoning in negotiations over answers, clarified where necessary by data recorded by computer activity log-files. The linear sequence of questions in the program allows students'interactions to be compared on a "like-for-like" basis.Activity systems are used to identify various tensions in students' responses whilst using CAL, facilitating a deeper understanding of the observed interactivity. A phenomenological profile of the students has been developed based on these interactions, particularly in response to the unexpected caused by the complex reality of the case. Further supporting quantitative data is obtained from a questionnaire survey and end-of-year examination results used to provide contextual background material particularly when presenting the results to a domain heavily dominated by a scientific epistemology.Throughout the program many students seem to ignore features not in their immediate focus. Students' reactions to the unexpected (extraction of 7s) indicates about half of the students are so reliant on simplified taught procedure they are unable to relate the extractions to these "hidden" features. Other students adopt a deeper approach and are able to identify reasons why the unexpected occurs. The program has been found to promote an active approach to learning in most students, whether their approach is surface or deep. Most students learn from the feedback provided by the program, even when this feedback is not explicit on a point. Students also benefit from working with a partner. The deeper understanding of students' misconceptions afforded by the adopted research methodology enables the development of guidelines for the future design of CAL in dentistry
Informatics innovation in clinical care: A visionary scenario for dentistry
Health information technology (HIT) is one of the most significant developments in health care in recent years. However, there is still a large gap between how HIT could support clinical work versus how it does. In this project, we developed a visionary scenario to identify opportunities for improving patient care in dentistry. In the scenario, patients and care providers are supported by a ubiquitous, embedded computing infrastructure that captures and processes data streams from multiple sources. Practical decision support, as well as automated background data processing (e.g., to screen for common conditions), helps clinicians provide quality care. A holistic view of clinical information technology (IT) focuses on supporting clinicians and patients in a user-centered manner. While clinical IT is still in very much a work in progress, scenarios such as the one presented may be helpful to keep us focused on the possibilities of tomorrow, not on the limitations of today
Artificial intelligent based teaching and learning approaches: A comprehensive review
The goal of this study is to investigate the potential effects that Artificial intelligence (AI) could have on education. The narrative and framework for investigating AI that emerged from the preliminary research served as the basis for the study’s emphasis, which was narrowed down to the use of AI and its effects on administration, instruction, and student learning. According to the findings, artificial intelligence has seen widespread adoption and use in education, particularly by educational institutions and in various contexts and applications. The development of AI began with computers and technologies related to computers; it then progressed to web-based and online intelligent education systems; and finally, it applied embedded computer systems in conjunction with other technologies, humanoid robots, and web-based chatbots to execute instructor tasks and functions either independently or in partnership with instructors. By utilizing these platforms, educators have been able to accomplish a variety of administrative tasks. In addition, because the systems rely on machine learning and flexibility, the curriculum and content have been modified to match the needs of students. This has led to improved learning outcomes in the form of higher uptake and retention rates
ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model
The ChatGPT, a lite and conversational variant of Generative Pretrained
Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large
Language Models (LLMs) with billions of parameters. LLMs have stirred up much
interest among researchers and practitioners in their impressive skills in
natural language processing tasks, which profoundly impact various fields. This
paper mainly discusses the future applications of LLMs in dentistry. We
introduce two primary LLM deployment methods in dentistry, including automated
dental diagnosis and cross-modal dental diagnosis, and examine their potential
applications. Especially, equipped with a cross-modal encoder, a single LLM can
manage multi-source data and conduct advanced natural language reasoning to
perform complex clinical operations. We also present cases to demonstrate the
potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical
application. While LLMs offer significant potential benefits, the challenges,
such as data privacy, data quality, and model bias, need further study.
Overall, LLMs have the potential to revolutionize dental diagnosis and
treatment, which indicates a promising avenue for clinical application and
research in dentistry
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