ABSTRACT
Sketch-based drawing assessments in art therapy are widely used to understand individuals’ cognitive and psychological states, such as cognitive impairment or mental disorders. Along with self-report measures based on a questionnaire, psychological drawing assessments can augment information about an individual psychological state. However, the interpretation of the drawing assessments requires much time and effort, especially in a large-scale group such as schools or companies, and depends on the experience of the art therapists. To address this issue, we propose an AI-based expert support system, AlphaDAPR, to support art therapists and psychologists in conducting a large-scale automatic drawing assessment. Our survey results with 64 art therapists showed that 64.06% of the participants indicated a willingness to use the proposed system. The results of structural equation modeling highlighted the importance of explainable AI embedded in the interface design to affect perceived usefulness, trust, satisfaction, and intention to use eventually. The interview results revealed that most of the art therapists show high levels of intention to use the proposed system while expressing some concerns about AI’s possible limitations and threats as well. Discussion and implications are provided, stressing the importance of clear communication about the collaborative role of AI and users.
- Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y Lim, and Mohan Kankanhalli. 2018. Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda. In Proceedings of the 2018 CHI conference on human factors in computing systems. 1–18.Google ScholarDigital Library
- Adobe Inc.2022. Adobe Illustrator CC 2022. https://adobe.com/products/illustratorGoogle Scholar
- Lewis Aiken. 1999. Personality assessment: Methods & practices (3rd ed. rev.). (1999).Google Scholar
- Brian Allen and Chriscelyn Tussey. 2012. Can projective drawings detect if a child experienced sexual or physical abuse? A systematic review of the controlled research. Trauma, violence, & abuse 13, 2 (2012), 97–111.Google Scholar
- Yasmeen Alufaisan, Laura R Marusich, Jonathan Z Bakdash, Yan Zhou, and Murat Kantarcioglu. 2021. Does explainable artificial intelligence improve human decision-making?. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 6618–6626.Google ScholarCross Ref
- Galit Amir and Rachel Lev-Wiesel. 2007. Dissociation as depicted in the traumatic event drawings of child sexual abuse survivors: A preliminary study. The arts in psychotherapy 34, 2 (2007), 114–123.Google Scholar
- Pui Kwan Au. 2020. An application of FEATS scoring system in Draw-A Person-in-the-Rain (DAPR): Distinguishing depression, anxiety, and stress by projective drawing. Ph. D. Dissertation. Hong Kong: Hong Kong Shue Yan University.Google Scholar
- Matthias Baldauf, Peter Fröehlich, and Rainer Endl. 2020. Trust me, I’ma doctor–user perceptions of AI-driven apps for mobile health diagnosis. In 19th International Conference on Mobile and Ubiquitous Multimedia. 167–178.Google ScholarDigital Library
- Enrico Bunde. 2021. AI-Assisted and explainable hate speech detection for social media moderators–A design science approach. In Proceedings of the 54th Hawaii International Conference on System Sciences. 1264.Google ScholarCross Ref
- Suellen M Carney. 1992. Draw a person in the rain: A comparison of levels of stress and depression among adolescents. Pace University.Google Scholar
- CELSYS, Inc.2022. CLIP STUDIO PAINT v1.5. https://www.clipstudio.net/enGoogle Scholar
- Shuqing Chen, Daniel Stromer, Harb Alnasser Alabdalrahim, Stefan Schwab, Markus Weih, and Andreas Maier. 2020. Automatic dementia screening and scoring by applying deep learning on clock-drawing tests. Scientific Reports 10, 1 (2020), 1–11.Google ScholarCross Ref
- Hwa-Yeon Chung. 2020. Characteristic of Response to ‘Draw A Person in the Rain’ (DAPR) Based on the Level of Job Stress of female workers.Master’s thesis. Seoul Women’s University.Google Scholar
- Fred D Davis. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly (1989), 319–340.Google ScholarDigital Library
- Mathias Eitz, James Hays, and Marc Alexa. 2012. How do humans sketch objects?ACM Transactions on graphics (TOG) 31, 4 (2012), 1–10.Google Scholar
- Glenn Jocher et. al.2022. ultralytics/yolov5: v6.1 - YOLOv5n ’Small’ models, Roboflow integration, TensorFlow export, OpenCV DNN support. https://doi.org/10.5281/zenodo.5563715Google ScholarCross Ref
- Mr C Fairhurst, T Linnell, Stephanie Glenat, RM Guest, Laurent Heutte, and Thierry Paquet. 2008. Developing a generic approach to online automated analysis of writing and drawing tests in clinical patient profiling. Behavior Research Methods 40, 1 (2008), 290–303.Google ScholarCross Ref
- Adam Graves, Leslie Jones, and Frances F Kaplan. 2013. Draw-a-Person-in-the-Rain: Does geographic location matter?Art Therapy 30, 3 (2013), 107–113.Google Scholar
- Simeng Gu, Yige Liu, Fei Liang, Rou Feng, Yawen Li, Guorui Liu, Mengdan Gao, Wei Liu, Fushun Wang, and Jason H Huang. 2020. Screening depressive disorders with tree-drawing test. Frontiers in psychology 11 (2020), 1446.Google Scholar
- Lijie Guo, Elizabeth M Daly, Oznur Alkan, Massimiliano Mattetti, Owen Cornec, and Bart Knijnenburg. 2022. Building Trust in Interactive Machine Learning via User Contributed Interpretable Rules. In 27th International Conference on Intelligent User Interfaces. 537–548.Google ScholarDigital Library
- David Ha and Douglas Eck. 2018. A Neural Representation of Sketch Drawings. In International Conference on Learning Representations (ICLR).Google Scholar
- Emanuel F Hammer. 1958. The clinical application of projective drawings.Charles C Thomas.Google Scholar
- Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision. 2961–2969.Google ScholarCross Ref
- Daire Hooper, Joseph Coughlan, and Michael R Mullen. 2008. Structural equation modelling: Guidelines for determining model fit. Electronic journal of business research methods 6, 1 (2008), pp53–60.Google Scholar
- Kuo-Lun Hsiao and Chia-Chen Chen. 2021. What drives continuance intention to use a food-ordering chatbot? An examination of trust and satisfaction. Library Hi Tech (2021).Google Scholar
- Yu-Kai Huang. 2010. The effect of airline service quality on passengers’ behavioural intentions using SERVQUAL scores: A Taiwan case study. Journal of the Eastern Asia Society for Transportation Studies 8 (2010), 2330–2343.Google Scholar
- Sie-Nae Huh. 2021. Characteristic of Response to ‘Draw A Person in the Rain’ (DAPR) Based on the Level of Job Stress of female workers.Master’s thesis. Seoul Women’s University.Google Scholar
- Hey-Kyoung Hwang. 2020. A study on characteristics of responses to DAPR(Draw-A-Person-in-the-Rain) Assessments based on the level of daily stress, self-efficacy of baby sitters.Master’s thesis. Seoul Women’s University.Google Scholar
- Chiara Ionio and Eleonora Mascheroni. 2021. Psychological well-being and graphic representations of self in child victims of violence. The Arts in Psychotherapy 72 (2021), 101740.Google ScholarCross Ref
- Hongchao Jiang, Yanci Zhang, Zhiwei Zeng, Jun Ji, Yu Wang, Ying Chi, and Chunyan Miao. 2021. Mobile-based Clock Drawing Test for Detecting Early Signs of Dementia. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 16048–16050.Google ScholarCross Ref
- Juliet Jue and Jung Hee Ha. 2019. The Person-in-the-Rain Drawing Test as an Assessment of Soldiers’ Army Life Adjustment and Resilience. Psychology 10, 8 (2019).Google Scholar
- Daiki Kato and Miyako Morita. 2009. Form, content, and gender differences in Lego® block creations by Japanese adolescents. Art Therapy 26, 4 (2009), 181–186.Google ScholarCross Ref
- Hong-hoe Kim, Paul Taele, Stephanie Valentine, Erin McTigue, and Tracy Hammond. 2013. KimCHI: a sketch-based developmental skill classifier to enhance pen-driven educational interfaces for children. In Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling. 33–42.Google Scholar
- Hyun-Ji Kim. 2012. Characteristic of Response to ‘Draw A Person in the Rain’ (DAPR) Based on the Level of Job Stress of female workers.Master’s thesis. Daejin University.Google Scholar
- Jeoungkun Kim, Soongeun Hong, Jinyoung Min, and Heeseok Lee. 2011. Antecedents of application service continuance: A synthesis of satisfaction and trust. Expert Systems with applications 38, 8 (2011), 9530–9542.Google Scholar
- Limor Kissos, Limor Goldner, Moshe Butman, Niv Eliyahu, and Rachel Lev-Wiesel. 2020. Can artificial intelligence achieve human-level performance? A pilot study of childhood sexual abuse detection in self-figure drawings. Child Abuse & Neglect 109 (2020), 104755.Google ScholarCross Ref
- Bart P Knijnenburg, Martijn C Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. Explaining the user experience of recommender systems. User modeling and user-adapted interaction 22, 4 (2012), 441–504.Google Scholar
- Kate Kravits, Randi McAllister-Black, Marcia Grant, and Christina Kirk. 2010. Self-care strategies for nurses: A psycho-educational intervention for stress reduction and the prevention of burnout. Applied Nursing Research 23, 3 (2010), 130–138.Google ScholarCross Ref
- Christine P Krom. 2000. Hospice nurses and the palliative care environment: Indicators of stress and coping in the Draw-a-Person-in-the-Rain test. Ph. D. Dissertation. Albertus Magnus College.Google Scholar
- Heidi S Lack. 1997. The person-in-the-rain projective drawing as a measure of children’s coping capacity: a concurrent validity study using rorschach, psychiatric, and life history variables. California School of Professional Psychology-Berkeley/Alameda. 156–174 pages.Google Scholar
- Eva Fishell Lichtenberg. 2014. Draw-A-Person-in-the Rain Test.In Drawings in assessment and psychotherapy: Research and application, Leonard Handler and A. D. Thomas (Eds.). Routledge/Taylor & Francis Group., 164–183.Google Scholar
- Yu Shiou Lin, Peter Hartwich, Annemarie Wolff, Mehrshad Golesorkhi, and Georg Northoff. 2020. The self in art therapy–brain-based assessment of the drawing process. Medical Hypotheses 138(2020), 109596.Google ScholarCross Ref
- Pin Luarn and Hsin-Hui Lin. 2005. Toward an understanding of the behavioral intention to use mobile banking. Computers in human behavior 21, 6 (2005), 873–891.Google Scholar
- Karen Machover. 1949. Personality projection in the drawing of the human figure: A method of personality investigation.(1949).Google Scholar
- Natalie Mackenzie. 2013. A brief exploration of the role of dramatherapy within a multi-modal arts therapy approach to working with children aged 4–14 years impacted by trauma. Dramatherapy 35, 2 (2013), 131–139.Google ScholarCross Ref
- Maria Madsen and Shirley Gregor. 2000. Measuring human-computer trust. In 11th australasian conference on information systems, Vol. 53. Citeseer, 6–8.Google Scholar
- A Meneghetti. 2004. Ontopsychology handbook. Ontopsicologia Editrice, Roma(2004).Google Scholar
- Habshah Midi, Saroje Kumar Sarkar, and Sohel Rana. 2010. Collinearity diagnostics of binary logistic regression model. Journal of interdisciplinary mathematics 13, 3 (2010), 253–267.Google ScholarCross Ref
- Seungjin Oh. 2014. Diagnostic test of art psycology.Dongmunsa, Seoul, Korea.Google Scholar
- Cecilia Panigutti, Andrea Beretta, Fosca Giannotti, and Dino Pedreschi. 2022. Understanding the impact of explanations on advice-taking: a user study for AI-based clinical Decision Support Systems. In CHI Conference on Human Factors in Computing Systems. 1–9.Google ScholarDigital Library
- Sung Youl Park. 2009. An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Journal of Educational Technology & Society 12, 3 (2009), 150–162.Google Scholar
- Robin Pennington, H Dixon Wilcox, and Varun Grover. 2003. The role of system trust in business-to-consumer transactions. Journal of management information systems 20, 3 (2003), 197–226.Google ScholarDigital Library
- Ochilbek Rakhmanov, Nwojo Nnanna Agwu, and Steve Adeshina. 2020. Experimentation on hand drawn sketches by children to classify Draw-a-Person test images in psychology. In The Thirty-Third International Flairs Conference.Google Scholar
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015).Google Scholar
- Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 1135–1144.Google ScholarDigital Library
- Woosuk Seo, Joonyoung Jun, Minki Chun, Hyeonhak Jeong, Sungmin Na, Woohyun Cho, Saeri Kim, and Hyunggu Jung. 2022. Toward an AI-assisted Assessment Tool to Support Online Art Therapy Practices: A Pilot Study. In Proceedings of 20th European Conference on Computer-Supported Cooperative Work. European Society for Socially Embedded Technologies (EUSSET).Google Scholar
- Rania Shibl, Meredith Lawley, and Justin Debuse. 2013. Factors influencing decision support system acceptance. Decision Support Systems 54, 2 (2013), 953–961.Google ScholarDigital Library
- Donghee Shin. 2021. The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies 146 (2021), 102551.Google ScholarCross Ref
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556(2014).Google Scholar
- Donghua Tao. 2009. Intention to use and actual use of electronic information resources: further exploring Technology Acceptance Model (TAM). In AMIA Annual Symposium Proceedings, Vol. 2009. American Medical Informatics Association, 629.Google Scholar
- JS Verinis, EF Lichtenberg, and L Henrich. 1974. The Draw-a-Person in the rain technique: Its relationship to diagnostic category and other personality indicators.Journal of Clinical Psychology(1974).Google Scholar
- Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y Lim. 2019. Designing theory-driven user-centric explainable AI. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1–15.Google ScholarDigital Library
- Katharina Weitz, Dominik Schiller, Ruben Schlagowski, Tobias Huber, and Elisabeth André. 2021. “Let me explain!”: exploring the potential of virtual agents in explainable AI interaction design. Journal on Multimodal User Interfaces 15, 2 (2021), 87–98.Google ScholarCross Ref
- Fan Wenjuan, Jingnan Liu, Zhu Shuwan, and Panos M Pardalos. 2020. Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research 294, 1-2 (2020), 567–592.Google Scholar
- Lisa R Willis, Stephen P Joy, and Donna H Kaiser. 2010. Draw-a-Person-in-the-Rain as an assessment of stress and coping resources. The Arts in Psychotherapy 37, 3 (2010), 233–239.Google ScholarCross Ref
- Barbara H Wixom and Peter A Todd. 2005. A theoretical integration of user satisfaction and technology acceptance. Information systems research 16, 1 (2005), 85–102.Google Scholar
- Minfan Zhang, Daniel Ehrmann, Mjaye Mazwi, Danny Eytan, Marzyeh Ghassemi, and Fanny Chevalier. 2022. Get To The Point! Problem-Based Curated Data Views To Augment Care For Critically Ill Patients. In CHI Conference on Human Factors in Computing Systems. 1–13.Google ScholarDigital Library
Index Terms
- AlphaDAPR: An AI-based Explainable Expert Support System for Art Therapy
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