Using a Machine Learning Architecture to Create an AI-Powered Chatbot for Anatomy Education

Li, Yik Sum; Lam, Cynthia Sin Nga; See, Cyh

doi:10.1007/s40670-021-01405-9

Cited by 31 publications

(24 citation statements)

References 3 publications

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“…This result might have been due to the limitations of our study’s chatbot, that was a type of transaction chatbot, which provided feedback based on predicted responses, but did not address specific questions asked by the students. However, along with the development of data processing and AI technology, computer programs using chatbots are able to understand and answer questions similar to humans [ 15 ]. Therefore, application of conversation-type or hybrid chatbot technology in future studies could possibly improve students’ feedback satisfaction.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, applications of nursing education utilizing AI are needed. To date, studies regarding chatbots in the medical field have focused on its application in the anatomy class for medical students [ 15 ] and chatbot programs for managing mental health, learning achievement, and well-being of college students [ 16 ]. In particular, in a study targeting 4th grade medical students in Hong Kong, clinical history was obtained by talking with a virtual patient through a chatbot mobile app without time or geographical restrictions.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the effect of an artificial intelligence chatbot educational program on non-face-to-face classes: a quasi-experimental study

2022

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Background Education and training are needed for nursing students using artificial intelligence-based educational programs. However, few studies have assessed the effect of using chatbots in nursing education. Objectives This study aimed to develop and examine the effect of an artificial intelligence chatbot educational program for promoting nursing skills related to electronic fetal monitoring in nursing college students during non-face-to-face classes during the COVID-19 pandemic. Design This quasi-experimental study used a nonequivalent control group non-synchronized pretest–posttest design. Methods The participants were 61 junior students from a nursing college located in G province of South Korea. Data were collected between November 3 and 16, 2021, and analyzed using independent t-tests. Results The experimental group—in which the artificial intelligence chatbot program was applied—did not show statistically significant differences in knowledge (t = -0.58, p = .567), clinical reasoning competency (t = 0.75, p = .455), confidence (t = 1.13, p = .264), and feedback satisfaction (t = 1.72, p = .090), compared with the control group; however, its participants’ interest in education (t = 2.38, p = .020) and self-directed learning (t = 2.72, p = .006) were significantly higher than those in the control group. Conclusion The findings of our study highlighted the potential of artificial intelligence chatbot programs as an educational assistance tool to promote nursing college students’ interest in education and self-directed learning. Moreover, such programs can be effective in enhancing nursing students’ skills in non-face-to face-situations caused by the ongoing COVID-19 pandemic.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of the effect of an artificial intelligence chatbot educational program on non-face-to-face classes: a quasi-experimental study

2022

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“…This AIEd also offers opportunities for enhanced consistency and precision across anatomy education delivery and assessments, as well as the capacity to detect issues and patterns that may otherwise go undetected by humans. This leads to claims that AI‐driven tutoring systems can quickly get to “know” students in far more detail than is possible for a human tutor (Briggs, 2014; Li et al, 2021; Wong, 2021). Human educators can also benefit from being relieved of routine teaching tasks (e.g., attendance, marking etc.)…”

Section: Strengths and Opportunities With Artificial Intelligence‐inf...mentioning

confidence: 99%

“…Anatomy educators are beginning to make use of AI‐assisted assessment as well—from the automated grading of essays and open‐ended answers, through to adaptive testing that alters the content of assessment to fit the capabilities of the individual test‐taker (Lee et al, 2012; Meyer et al, 2016). More recent advances include the development of chatbots and conversational agents to answer “Googleable” anatomy knowledge questions (e.g., structure names and textbook reported innervations) (Li et al, 2021). These chatbots are also helping to enhance learners' radiological anatomy knowledge (Marsh, 2018).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and clinical anatomical education: Promises and perils

Lazarus

Truong

Douglas

et al. 2022

Anatomical Sciences Ed

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Anatomy educators are often at the forefront of adopting innovative and advanced technologies for teaching, such as artificial intelligence (AI). While AI offers potential new opportunities for anatomical education, hard lessons learned from the deployment of AI tools in other domains (e.g., criminal justice, healthcare, and finance) suggest that these opportunities are likely to be tempered by disadvantages for at least some learners and within certain educational contexts. From the perspectives of an anatomy educator, public health researcher, medical ethicist, and an educational technology expert, this article examines five tensions between the promises and the perils of integrating AI into anatomy education. These tensions highlight the ways in which AI is currently ill‐suited for incorporating the uncertainties intrinsic to anatomy education in the areas of (1) human variations, (2) healthcare practice, (3) diversity and social justice, (4) student support, and (5) student learning. Practical recommendations for a considered approach to working alongside AI in the contemporary (and future) anatomy education learning environment are provided, including enhanced transparency about how AI is integrated, AI developer diversity, inclusion of uncertainty and anatomical variations within deployed AI, provisions made for educator awareness of AI benefits and limitations, building in curricular “AI‐free” time, and engaging AI to extend human capacities. These recommendations serve as a guiding framework for how the clinical anatomy discipline, and anatomy educators, can work alongside AI, and develop a more nuanced and considered approach to the role of AI in healthcare education.

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“…Interactive teaching programs such as Anatomy Chatbots [ 48 ], immediate formative assessment tools or clinical application quizzes could be modified and programmed into the deep learning AI framework to assess the extent of deeper and more logical learning and application of this knowledge in a clinical setting among students [ 49 ].…”

Section: Novel Uses Of Artificial Intelligence To Facilitate Anatomy ...mentioning

confidence: 99%

Teaching, Learning and Assessing Anatomy with Artificial Intelligence: The Road to a Better Future

Abdellatif

Mushaiqri

Albalushi

et al. 2022

IJERPH

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Anatomy is taught in the early years of an undergraduate medical curriculum. The subject is volatile and of voluminous content, given the complex nature of the human body. Students frequently face learning constraints in these fledgling years of medical education, often resulting in a spiraling dwindling academic performance. Hence, there have been continued efforts directed at developing new curricula and incorporating new methods of teaching, learning and assessment that are aimed at logical learning and long-term retention of anatomical knowledge, which is a mainstay of all medical practice. In recent years, artificial intelligence (AI) has gained in popularity. AI uses machine learning models to store, compute, analyze and even augment huge amounts of data to be retrieved when needed, while simultaneously the machine itself can be programmed for deep learning, improving its own efficiency through complex neural networks. There are numerous specific benefits to incorporating AI in education, which include in-depth learning, storage of large electronic data, teaching from remote locations, engagement of fewer personnel in teaching, quick feedback from responders, innovative assessment methods and user-friendly alternatives. AI has long been a part of medical diagnostics and treatment planning. Extensive literature is available on uses of AI in clinical settings, e.g., in Radiology, but to the best of our knowledge there is a paucity of published data on AI used for teaching, learning and assessment in anatomy. In the present review, we highlight recent novel and advanced AI techniques such as Artificial Neural Networks (ANN), or more complex Convoluted Neural Networks (CNN) and Bayesian U-Net, which are used for teaching anatomy. We also address the main advantages and limitations of the use of AI in medical education and lessons learnt from AI application during the COVID-19 pandemic. In the future, studies with AI in anatomy education could be advantageous for both students to develop professional expertise and for instructors to develop improved teaching methods for this vast and complex subject, especially with the increasing paucity of cadavers in many medical schools. We also suggest some novel examples of how AI could be incorporated to deliver augmented reality experiences, especially with reference to complex regions in the human body, such as neural pathways in the brain, complex developmental processes in the embryo or in complicated miniature regions such as the middle and inner ear. AI can change the face of assessment techniques and broaden their dimensions to suit individual learners.

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Using a Machine Learning Architecture to Create an AI-Powered Chatbot for Anatomy Education

Cited by 31 publications

References 3 publications

Analysis of the effect of an artificial intelligence chatbot educational program on non-face-to-face classes: a quasi-experimental study

Analysis of the effect of an artificial intelligence chatbot educational program on non-face-to-face classes: a quasi-experimental study

Artificial intelligence and clinical anatomical education: Promises and perils

Teaching, Learning and Assessing Anatomy with Artificial Intelligence: The Road to a Better Future

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