How do conversational case-based reasoning systems interact with their users: a literature review

Hillig, Stefanie; Müller, Romy

doi:10.1080/0144929x.2020.1767207

Cited by 3 publications

(2 citation statements)

References 67 publications

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“…All the formulas in the sequence are either axioms, assumptions, or direct inferences from previous formulas using rules of inference. In short, it is to comprehend something based on an argument (Hillig & Müller, 2021).…”

Section: Deductive Reasoningmentioning

confidence: 99%

Enhancing adolescent reasoning skills through a video game program

2023

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Much research has been done on the benefits of video games in a physical education context (Camunas-Vega & Alcaide-Risoto, 2020; Fang et al., 2020). However, little attention has so far been paid to the effectiveness of commercial video games combined with actual basketball practice in helping students improve their reasoning skills (Chuang et al., 2021; Rogers et al., 2020). The study presents a quasi-experimental pre-post design with a control group in order to evaluate the impact of a specific training program in reasoning with the video game NBA 2K16. A convenience sample of 215 high school students participated in the study. Three reasoning subscales of the Evalua-9 psycho-pedagogical test (inductive α = 0.88; deductive α = 0.85; spatial α = 0.89) were used for data collection. Our findings show a significant moderate effect in the students who participated in the training program. An additional outcome was that sex differences in spatial and deductive reasoning in the pre-test disappeared in the post-test. We discuss the educational implications of the use of the video game as the main learning tool to enhance the reasoning process of Secondary Education students in Physical Education.

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Section: Deductive Reasoningmentioning

confidence: 99%

Enhancing adolescent reasoning skills through a video game program

2023

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“…51-55). For diagnosis, this adds an additional interactive component to a customized electronic textbook by highlighting matching features between the target case and the problem description (Hillig & Müller, 2020). From an intelligence perspective, while these systems provide an alternative interface for establishing relevance in the search process, there is still no visualization of the patient, and this is necessary if the exceptional case is to be recognized medically in its possible (and probable) exceptionality.…”

Section: Procedural Rationality and The Symbolic Visualization Of Bod...mentioning

confidence: 99%

Artificial intelligence and the situational rationality of diagnosis: Human problem‐solving and the artifacts of health and medicine

Raphael

2022

Sociology Compass

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What is the problem‐solving capacity of artificial intelligence (AI) for health and medicine? This paper draws out the cognitive sociological context of diagnostic problem‐solving for medical sociology regarding the limits of automation for decision‐based medical tasks. Specifically, it presents a practical way of evaluating the artificiality of symptoms and signs in medical encounters, with an emphasis on the visualization of the problem‐solving process in doctor‐patient relationships. In doing so, the paper details the logical differences underlying diagnostic task performance between man and machine problem‐solving: its principle of rationality, the priorities of its means of adaptation to abstraction, and the effects of seeking optimization in the problem‐solving process. Using these parameters as a heuristic for evaluating the capacity of AI to address issues of diagnostic error through design, the paper presents a conceptual review of the discipline of AI in medicine. Studies relying on procedural rationality describe models that treat diagnosis as a “natural artifact” by employing symbolic methods designed to simulate human problem‐solving. Research adhering to probabilistic rationality describes models that treat diagnosis as a “natural artifact” of an ecological image by utilizing sub‐symbolic methods designed to simulate neural networks. Research guided by situational rationality describes models that require treating diagnosis as a “socio‐cognitive artifact,” the artificiality of which is organized in discourses of patient‐centered decision‐making. The paper concludes with a commentary on the ethical application of AI in health and medicine, given the logical differences underlying diagnostic task performance.

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