AIMC Topic: Problem-Based Learning

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Eye-tracker and fNIRS: Using neuroscientific tools to assess the learning experience during children's educational robotics activities.

Trends in neuroscience and education
In technology education, there has been a paradigmatic shift towards student-centered approaches such as learning by doing, constructionism, and experiential learning. Educational robotics allows students to experiment with building and interacting w...

Fostering Clinical Judgment and Promoting Transition Into First Clinical Rotation Through Active Learning.

Nursing education perspectives
Undergraduate nursing education builds on knowledge obtained from previous semesters to grow critical thinking skills and promote clinical judgment attainment. Transitioning knowledge from the classroom to the clinical setting is often difficult. Stu...

Enhancing AI competence in health management: students' experiences with ChatGPT as a learning Tool.

BMC medical education
BACKGROUND: The healthcare industry has had to adapt to significant shifts caused by technological advancements, demographic changes, economic pressures, and political dynamics. These factors are reshaping the complex ecosystem in which healthcare or...

Development of an Artificial Intelligence Teaching Assistant System for Undergraduate Nursing Students: A Field Testing Study.

Computers, informatics, nursing : CIN
Keeping students engaged and motivated during online or class discussion may be challenging. Artificial intelligence has potential to facilitate active learning by enhancing student engagement, motivation, and learning outcomes. The purpose of this s...

Learning with AI Language Models: Guidelines for the Development and Scoring of Medical Questions for Higher Education.

Journal of medical systems
In medical and biomedical education, traditional teaching methods often struggle to engage students and promote critical thinking. The use of AI language models has the potential to transform teaching and learning practices by offering an innovative,...

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.

Journal of chemical information and modeling
The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods ...

AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden.

Sensors (Basel, Switzerland)
The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, t...

Selecting cardiac magnetic resonance images suitable for annotation of pulmonary arteries using an active-learning based deep learning model.

Scientific reports
An increasing and aging patient population poses a growing burden on healthcare professionals. Automation of medical imaging diagnostics holds promise for enhancing patient care and reducing manpower required to accommodate an increasing patient-popu...

Joint Fusion and Detection via Deep Learning in UAV-Borne Multispectral Sensing of Scatterable Landmine.

Sensors (Basel, Switzerland)
Compared with traditional mine detection methods, UAV-based measures are more suitable for the rapid detection of large areas of scatterable landmines, and a multispectral fusion strategy based on a deep learning model is proposed to facilitate mine ...

Subsequence and distant supervision based active learning for relation extraction of Chinese medical texts.

BMC medical informatics and decision making
In recent years, relation extraction on unstructured texts has become an important task in medical research. However, relation extraction requires a large amount of labeled corpus, manually annotating sequences is time consuming and expensive. Theref...