Dark Play of Serious Games: Effectiveness and Features (G4HE2018)

Buijs-Spanjers, Kiki R; Hegge, Harianne HM; Cnossen, Fokie; Hoogendoorn, Evert; Jaarsma, Debbie; Rooij, Sophia E. de

doi:10.1089/g4h.2018.0126

Cited by 14 publications

(10 citation statements)

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“…Yet, our results imply that engaging in different simulation situations in The Delirium Experience (eg, the severity of the delirious episodes) does not result in differences in lessons learned or self-reported knowledge. This is in line with the results of our previous study showing that normal or dark play did not affect game effectiveness in students who were allocated to the two conditions [ 19 ].…”

Section: Discussionsupporting

confidence: 92%

“…Although many studies on serious games in medical education have been reported in recent years [ 16 - 18 ], these studies often investigated serious games as whole artifacts without focusing on the effect of different play strategies such as dark play. In a previous study, we showed that dark play did not affect game effectiveness [ 19 ]. However, our students had been allocated to a normal or dark play condition without being able to choose their game.…”

Section: Introductionmentioning

confidence: 99%

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Reasons to Engage in and Learning Experiences From Different Play Strategies in a Web-Based Serious Game on Delirium for Medical Students: Mixed Methods Design

Buijs-Spanjers¹,

Hegge²,

Cnossen³

et al. 2020

JMIR Serious Games

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Background Although many studies have recently been published on the value of serious games for medical education, little attention has been given to the role of dark play (choosing unacceptable actions in games). Objective This study aimed to investigate potential differences in the characteristics of medical students who have the opportunity to choose normal or dark play in a serious game. This study also aimed to compare their reasons for choosing a play strategy and their perceptions of what they learned from their game play. Methods We asked undergraduate medical students to play a serious game in which they had to take care of a patient with delirium (The Delirium Experience). After getting acquainted with the game, students could opt for normal or dark play. Student characteristics (age, gender, experience with caring for older or delirious patients, and number of completed clerkships) were collected, and the Delirium Attitude Scale and Learning Motivation and Engagement Questionnaire were administered. Reasons for choosing normal or dark play were evaluated with an open-ended question. Information on lessons they had learned from the game was collected using an open-ended question and self-reported knowledge on delirium. Results This study had 160 participants (89 normal play, 71 dark play). Male students (26/160, 56.5%) chose dark play significantly more often than female students (45/160, 39.5%; P=.049). We did not find significant differences in student characteristics or measurement outcomes between play strategies. Participants’ main reason for choosing normal play was to learn how to provide care to delirious patients, and the main reason for dark play was to gain insight into what a delirious patient has to endure during delirious episodes. All participants learned what to do when taking care of a delirious patient and gained insight into how a patient experiences delirium. We found no differences in self-reported knowledge. Conclusions When medical students have the opportunity to choose dark play in a serious game, half of them will probably choose this play strategy. Male students will more likely opt for dark play than female students. Choice of play strategy is not affected by any other student characteristic or measurement outcome. All students learned the same lessons from playing the game, irrespective of their learning strategy.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Reasons to Engage in and Learning Experiences From Different Play Strategies in a Web-Based Serious Game on Delirium for Medical Students: Mixed Methods Design

Buijs-Spanjers¹,

Hegge²,

Cnossen³

et al. 2020

JMIR Serious Games

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“…Tele-empathy is a promising emerging field where clinicians and carers can get a sense of what the patient is experiencing physically, such as tremors in Parkinson’s disease ( Palanica et al, 2019 ). Research on serious games for medical education (The Delirium Experience) shows certain game features, being able to “switch perspective,” can enhance medical student empathy if they play the game from the patient or nurse perspective ( Buijs-Spanjers et al, 2019 ; Ma et al, 2021 ). Experiments in the Netherlands on a serious game for care workers for people with disabilities (The world of EMPA) showed participation did not enhance empathy for disabled people but it did decrease personal distress in care workers ( Sterkenburg and Vacaru, 2018 ).…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

et al. 2023

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BackgroundAdvances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.ObjectivesThe aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?Materials and methodsA systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011–2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.ResultsSearches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan–Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.ConclusionThere is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.ImplicationsIn a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.

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“…It was shown in one of the included studies that a voluntarily poor decision making in an SG compared with a "normal" one did not influence the improvement of cognitive skills [SMD = 0.00 (95% CI = −0.31 to 0.31)]. 51 Another study showed that more frequent but lighter sessions of SG usage led to higher knowledge acquisition than fewer but more intensive sessions of SG usage [SMD = 0.43 (95% CI = 0.30 to 0.56)]. 53 The last one focused on the evaluation of 2 similar SGs; the experimental group received an SG an educational content aligned with the learning objectives as the control group received an SG with a similar, but irrelevant educational content, to avoid compensatory equalization in this group.…”

Section: Descriptive Results Of Included Studiesmentioning

confidence: 99%

“…Few included studies compared different versions of an SG between one another, which is essential to isolate the impact of individual design choices. 47,51,52,72 Future studies should focus on evaluating the efficacy of different versions of an SG on engagement and learning outcomes.…”

Section: Discussionmentioning

confidence: 99%

Efficacy of Serious Games in Healthcare Professions Education

et al. 2020

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Serious games (SGs) are interactive and entertaining software designed primarily with an educational purpose. This systematic review synthesizes evidence from experimental studies regarding the efficacy of SGs for supporting engagement and improving learning outcomes in healthcare professions education. Randomized controlled trials (RCTs) published between January 2005 and April 2019 were included. Reference selection and data extraction were performed in duplicate, independently. Thirty-seven RCTs were found and 29 were included in random-effect meta-analyses. Compared with other educational interventions, SGs did not lead to more time spent with the intervention {mean difference 23.21 minutes [95% confidence interval (CI) = −1.25 to 47.66]}, higher knowledge acquisition [standardized mean difference (SMD) = 0.16 (95% CI = −0.20 to 0.52)], cognitive [SMD 0.08 (95% CI = −0.73 to 0.89)], and procedural skills development [SMD 0.05 (95% CI = −0.78 to 0.87)], attitude change [SMD = −0.09 (95% CI = −0.38 to 0.20)], nor behavior change [SMD = 0.2 (95% CI = −0.11 to 0.51)]. Only a small SMD of 0.27 (95% CI = 0.01 to 0.53) was found in favor of SGs for improving confidence in skills.

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Dark Play of Serious Games: Effectiveness and Features (G4HE2018)

Cited by 14 publications

References 24 publications

Reasons to Engage in and Learning Experiences From Different Play Strategies in a Web-Based Serious Game on Delirium for Medical Students: Mixed Methods Design

Reasons to Engage in and Learning Experiences From Different Play Strategies in a Web-Based Serious Game on Delirium for Medical Students: Mixed Methods Design

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Efficacy of Serious Games in Healthcare Professions Education

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