concept

AI+Knowledge Graph teaching model

Also known as: AI+Knowledge Graph

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Construction and Evaluation of an "AI+Knowledge Graph" Teaching ... researchsquare.com Research Square 22 facts

claimThe study utilized a prospective, randomised, controlled design where the control group received traditional teaching and the experimental group received an "AI+Knowledge Graph" teaching model based on the ARCS motivation model.

procedureThe 'AI+Knowledge Graph' teaching model employs a three-dimensional assessment framework consisting of: (1) Learning and Thinking Dimension (pre-class engagement, in-class participation, computer vision metrics, and post-class assignments), (2) Collaboration and Innovation Dimension (group collaboration, clinical reasoning, and innovative solutions), and (3) Diagnosis and Summary Dimension (interim test results, reflection reports, peer assessments, and satisfaction surveys).

claimThe 'AI+Knowledge Graph' teaching model includes a 'Step into Clinical Practice' scenario video library that records authentic diagnostic and therapeutic scenarios involving cancer patients, covering clinical manifestations, doctor-patient communication, and treatment decision-making processes.

procedureThe Computer Vision Technology System used in the 'AI+Knowledge Graph' teaching model includes an Attention Analysis Module that employs OpenPose pose estimation technology to capture skeletal keypoints and uses boosting algorithms (GBDT/XGBoost) to classify skeletal vector features for identifying classroom behaviors such as listening, reading, writing, mobile phone use, and resting.

procedureIn the 'AI+Knowledge Graph' teaching model, classroom instruction is divided into two 20-minute parts: teacher-led precision teaching using a knowledge map to show clinical connections, and collaborative case discussion where students analyze tiered integrated oncology cases generated by ChatGPT.

procedureThe Computer Vision Technology System used in the 'AI+Knowledge Graph' teaching model includes a Comprehension Analysis Module that uses real-time facial video capture and deep learning-based expression recognition algorithms to analyze eyebrow-eye angles and lip state changes to identify student emotional states like confusion, comprehension, and concentration.

claimThe 'AI+Knowledge Graph' teaching model for the course 'Integrated Chinese and Western Oncology' at Hunan University of Chinese Medicine utilizes a flipped classroom approach combined with a tiered case analysis method.

claimThe study designed an 'AI+Knowledge Graph' teaching model based on the ARCS motivation model, which incorporates five core functional modules: learning resources, classroom teaching, teacher-student interaction, formative assessment, and computer vision technology.

claimThe classroom teaching module of the 'AI+Knowledge Graph' teaching model employs a blended online-offline model where theoretical lectures use an interactive approach, allowing students to answer questions in person while simultaneously engaging in synchronous Q&A and real-time chat interactions via mobile devices on an online platform.

procedureThe "AI+Knowledge Graph" teaching model follows the ARCS motivation model, which structures the teaching process into a four-stage closed loop of "Attention-Relevance-Confidence-Satisfaction."

measurementThe satisfaction questionnaire used to evaluate the 'AI+Knowledge Graph' teaching model has a total Cronbach's α coefficient of 0.812.

accountThe research subjects for the 'AI+Knowledge Graph' teaching model study were undergraduate medical students majoring in Integrated Chinese and Western Medicine at Hunan University of Chinese Medicine, specifically the 2022–2024 cohort enrolled in the 'Integrated Chinese and Western Oncology' course.

claimThe "AI+Knowledge Graph" teaching platform provides preparatory resources including navigation-based learning modules, instructor-recorded "Clinical Insights" videos, structured PowerPoint presentations, mind maps, and curated supplementary resources from PubMed, Medscape, and Osmosis.

claimThe 'AI+Knowledge Graph' teaching model utilizes a ChatGPT-based intelligent question-answering module to support the dynamic generation and analysis of clinical cases.

procedureThe 'AI+Knowledge Graph' teaching model employs a dual-track approach of 'intelligent assistance + split-classroom teaching' with sessions lasting 40 minutes.

claimPractical sessions in the 'AI+Knowledge Graph' teaching model combine flipped classroom principles with a tiered case analysis approach, where students acquire foundational knowledge through knowledge maps and scenario-based videos before class, and engage in discussions centered on clinical cases of progressive difficulty during class.

measurementThe 'AI+Knowledge Graph' teaching model consists of two 40-minute sessions scheduled weekly over a 16-week teaching cycle.

procedureIn the 'AI+Knowledge Graph' teaching model, students acquire foundational knowledge through knowledge maps and scenario-based videos prior to class, followed by in-depth discussions of clinical cases with progressive difficulty levels during class sessions.

measurementThe satisfaction questionnaire used to evaluate the 'AI+Knowledge Graph' teaching model comprises 25 structured items and open-ended questions based on the ARCS motivation model, utilizing a 5-point Likert scale.

referenceThe learning resources module in the 'AI+Knowledge Graph' teaching model uses a knowledge graph as its core structure to integrate multimodal learning resources, including core concepts, pathological mechanisms, aetiology, pathogenesis, and diagnostic and therapeutic protocols from Integrated Chinese and Western Oncology.

procedurePost-class tasks in the 'AI+Knowledge Graph' teaching model include completing chapter assignments and online quizzes, submitting structured learning reflection reports comparing student reasoning to 'best practice' pathways provided by the knowledge graph, and completing tiered extension tasks assigned based on individual test performance.

claimThe 'AI+Knowledge Graph' teaching model integrates professional medical databases and teaching platforms, specifically PubMed, MedSci, Medscape, Osmosis, Jove, TeachMe Anatomy, and Radiopaedia, into its learning resource system.