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A Study of the Effects of Interactive AI Image Processing Functions on Children’s Painting Education

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Human Aspects of IT for the Aged Population. Design, Interaction and Technology Acceptance (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13330))

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Abstract

Digital media technologies have been gradually integrated into teaching activities over the past few years, providing teachers with more possibilities for teaching. This study examines the teaching effects of an interactive AI based image-processing platform in assisting as a teaching aid for children painting education. In this study, we compared the learning interest, learning attitude, and continuous learning intention of 96 children aged 5 to 13 in the process of painting education. The subjects were divided into two groups: the experimental group used AI image processing for painting education, and the control group utilized traditional teaching methods for painting learning. Results showed that the use of AI image-processing tools in painting education reduces girls’ learning attitudes and continuous learning intention, while stimulating boys’ learning interest.

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References

  1. Grant Cooke, D.G.: Maureen Cox, Teaching Young Children to Draw. (2005)

    Google Scholar 

  2. Pavlou, V., Kambouri, M.: Pupils’attitudes towards art teaching in primary school: an evaluation tool. Stud. Educ. Eval. 33(3–4), 282–301 (2007)

    Google Scholar 

  3. Nikoltsos, C.: The Art of Teaching Art in Early Childhood Education. (2000)

    Google Scholar 

  4. Snyder, L., Klos, P., Grey-Hawkins, L.: Transforming teaching through arts integration: AI implementation results: middle school reform through effective arts integration professional development. J. Learn. Arts 10(1), n1 (2014)

    Google Scholar 

  5. Jiang, Q., et al.: The impact of perceived interactivity and intrinsic value on users’ continuance intention in using mobile augmented reality virtual shoe-try-on function. Systems 10(1), 3 (2022)

    Google Scholar 

  6. Lin, J.-C., Hsieh, C.-C.: A Real-time Posture Recognition System using YOLACT++ and ResNet18. In: 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). IEEE (2021)

    Google Scholar 

  7. Rai, A., Constantinides, P., Sarker, S.: Next generation digital platforms: toward human-AI hybrids. MIS Q. 43(1), iii–ix (2019)

    Google Scholar 

  8. Williamson, B., Eynon, R., Potter, J.: Pandemic Politics, Pedagogies and Practices: Digital Technologies and Distance Education During the Coronavirus Emergency. Taylor & Francis. pp. 107–114 (2020)

    Google Scholar 

  9. Holmes, W., Bialik, M., Fadel, C.: Artificial Intelligence in Education. Center for Curriculum Redesign, Boston pp. 1–35 (2019)

    Google Scholar 

  10. Bozkurt, A., et al.: Artificial intelligence and reflections from educational landscape: a review of AI studies in half a century. Sustainability 13(2), 800 (2021)

    Google Scholar 

  11. Boden, M.A.: Creativity and artificial intelligence. Artif. Intell. 103(1–2), 347–356 (1998)

    MathSciNet  MATH  Google Scholar 

  12. Draves, S.: The electric sheep screen-saver: a case study in aesthetic evolution. In: Workshops on Applications of Evolutionary Computation, Springer (2005)

    Google Scholar 

  13. Draves, S., Reckase, E.: The Fractal Flame Algorithm. Citeseerx. Recuperado de http://citeseerx.ist.psu.edu/viewdoc/summary (2008)

  14. Machado, P., Romero, J., Manaris, B.: Experiments in computational aesthetics. In: Romero, J., Machado, P. (eds.) The art of artificial evolution, pp. 381–415. Springer Berlin Heidelberg, Berlin, Heidelberg (2008). https://doi.org/10.1007/978-3-540-72877-1_18

    Chapter  Google Scholar 

  15. Colton, S.: The painting fool: stories from building an automated painter. In: McCormack, J., d’Inverno, M. (eds.) Computers and creativity, pp. 3–38. Springer Berlin Heidelberg, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31727-9_1

    Chapter  Google Scholar 

  16. Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  17. Blair, A.: Adversarial evolution and deep learning–how does an artist play with our visual system? In: International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar). Springer (2019)

    Google Scholar 

  18. Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014)

    Google Scholar 

  19. Hidi, S., Renninger, K.A.: The four-phase model of interest development. Educ. Psychol. 41(2), 111–127 (2006)

    Google Scholar 

  20. Harackiewicz, J.M., et al.: Short-term and long-term consequences of achievement goals: predicting interest and performance over time. J. Educ. Psychol. 92(2), 316 (2000)

    Google Scholar 

  21. Pugh, K.J., Bergin, D.A.: Motivational influences on transfer. Educ. Psychol. 41(3), 147–160 (2006)

    Google Scholar 

  22. Hidi, S.: An Interest Researcher’s Perspective: The Effects of Extrinsic and Intrinsic Factors on Motivation. In: Intrinsic and extrinsic motivation, pp. 309–339. Elsevier (2000)

    Google Scholar 

  23. Mitchell, M.: Situational interest: its multifaceted structure in the secondary school mathematics classroom. J. Educ. Psychol. 85(3), 424 (1993)

    Google Scholar 

  24. Renninger, K.A.: Individual Interest and its Implications for Understanding Intrinsic Motivation. In: Intrinsic and extrinsic motivation, pp. 373–404. Elsevier (2000)

    Google Scholar 

  25. Harackiewicz, J.M., Smith, J.L., Priniski, S.J.: Interest matters: the importance of promoting interest in education. Policy Insights Behav. Brain Sci. 3(2), 220–227 (2016)

    Google Scholar 

  26. Renninger, K.A., Bachrach, J.E., Hidi, S.E.: Triggering and maintaining interest in early phases of interest development. Learn. Cult. Soc. Interact. 23, 100260 (2019)

    Google Scholar 

  27. Berlyne, D.E.: Novelty, complexity, and hedonic value. Percept. Psychophys. 8(5), 279–286 (1970)

    Google Scholar 

  28. Renninger, K.A., Hidi, S.E.: The Power of Interest for Motivation and Engagement. Routledge (2015)

    Google Scholar 

  29. Hidi, S.: Interest, reading, and learning: theoretical and practical considerations. Educ. Psychol. Rev. 13(3), 191–209 (2001)

    Google Scholar 

  30. Häussler, P., Hoffmann, L.: An intervention study to enhance girls’ interest, self-concept, and achievement in physics classes. J. Res. Sci. Teach. 39(9), 870–888 (2002)

    Google Scholar 

  31. Renninger, L.A., Wade, T.J., Grammer, K.: Getting that female glance: patterns and consequences of male nonverbal behavior in courtship contexts. Evol. Hum. Behav. 25(6), 416–431 (2004)

    Google Scholar 

  32. Schraw, G., Dennison, R.S.: Assessing metacognitive awareness. Contemp. Educ. Psychol. 19(4), 460–475 (1994)

    Google Scholar 

  33. Davidson, J.W., Sloboda, J.A., Howe, M.J.: The role of parents and teachers in the success and failure of instrumental learners. Bulletin Council Res. Music Educ. 40–44 (1995)

    Google Scholar 

  34. Harackiewicz, J.M., et al.: The role of achievement goals in the development of interest: reciprocal relations between achievement goals, interest, and performance. J. Educ. Psychol. 100(1), 105 (2008)

    Google Scholar 

  35. Perloff, R.M.: The Dynamics of Persuasion: Communication and Attitudes in the Twenty-First Century (2016)

    Google Scholar 

  36. Wixom, B.H., Todd, P.A.: A theoretical integration of user satisfaction and technology acceptance. Inf. Syst. Res. 16(1), 85–102 (2005)

    Google Scholar 

  37. LaTour, S.A., Peat, N.C.: Conceptual and methodological issues in consumer satisfaction research. ACR North American Advances, (1979)

    Google Scholar 

  38. Bhattacherjee, A.: Understanding information systems continuance: an expectation-confirmation model. MIS quarterly 351–370 (2001)

    Google Scholar 

  39. Oliver, R.L.: A cognitive model of the antecedents and consequences of satisfaction decisions. J. Mark. Res. 17(4), 460–469 (1980)

    Google Scholar 

  40. Seddon, P.B.: A respecification and extension of the DeLone and McLean model of IS success. Inf. Syst. Res. 8(3), 240–253 (1997)

    Google Scholar 

  41. Gentle, K.: Children and art teaching. Routledge (1990)

    Google Scholar 

  42. Stankiewicz, M.A.: Discipline and the future of art education. Stud. Art Educ. 41(4), 301–313 (2000)

    Google Scholar 

  43. Herne, S.: Art in the Primary School. Policy and Guidelines for the Art National Curriculum. London Borough of Tower Hamlets Inspection and Advisory Services, London (1994)

    Google Scholar 

  44. Meager, N.A.: Teaching art at Key Stage 2. Corsham, Wiltshire: National Society for Education in Art&Design. VIsual Impact Publications (1995)

    Google Scholar 

  45. Cochran, K.F., DeRuiter, J.A., King, R.A.: Pedagogical content knowing: an integrative model for teacher preparation. J. Teach. Educ. 44(4), 263–272 (1993)

    Google Scholar 

  46. Molloy, J.C., Noe, R.A.: Learning a living: Continuous learning for survival in today’s talent market. Learn., Train., Dev. Organ. 333, 361 (2010)

    Google Scholar 

  47. Grant, D.M., Malloy, A.D., Murphy, M.C.: A comparison of student perceptions of their computer skills to their actual abilities. J. Inf. Technol. Educ. Res. 8(1), 141–160 (2009)

    Google Scholar 

  48. Jin, L., et al.: Understanding user behavior in online social networks: a survey. IEEE Commun. Mag. 51(9), 144–150 (2013)

    Google Scholar 

  49. Mohammadi, H.: Investigating users’ perspectives on e-learning: an integration of TAM and IS success model. Comput. Hum. Behav. 45, 359–374 (2015)

    Google Scholar 

  50. Dağhan, G., Akkoyunlu, B.: Modeling the continuance usage intention of online learning environments. Comput. Hum. Behav. 60, 198–211 (2016)

    Google Scholar 

  51. Guo, Z., et al.: Promoting online learners’ continuance intention: an integrated flow framework. Inf. Manage. 53(2), 279–295 (2016)

    Google Scholar 

  52. Huang, L., Zhang, J., Liu, Y.: Antecedents of student MOOC revisit intention: moderation effect of course difficulty. Int. J. Inf. Manage. 37(2), 84–91 (2017)

    Google Scholar 

  53. Tarhini, A., Hone, K., Liu, X.: A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. Br. J. Edu. Technol. 46(4), 739–755 (2015)

    Google Scholar 

  54. Teo, T., Noyes, J.: An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: a structural equation modeling approach. Comput. Educ. 57(2), 1645–1653 (2011)

    Google Scholar 

  55. Li, Y., et al.: An empirical study on behavioural intention to reuse e-learning systems in rural China. Br. J. Edu. Technol. 43(6), 933–948 (2012)

    Google Scholar 

  56. Playform: Available from: https://www.playform.io/

  57. Martindale, C.: The Clockwork Muse: The Predictability of Artistic Change. Basic Books (1990)

    Google Scholar 

  58. Elgammal, A., et al.: Can: Creative Adversarial Networks, Generating “Art” by Learning about Styles and Deviating from Style Norms. arXiv preprint arXiv:1706.07068 (2017)

  59. Setbon, M., Raude, J.: Factors in vaccination intention against the pandemic influenza A/H1N1. Eur. J. Pub. Health 20(5), 490–494 (2010)

    Google Scholar 

  60. Kline, R.B.: Principles and Practice of Structural Equation Modeling. Guilford publications (2015)

    Google Scholar 

  61. Al-Refaie, A., Ghnaimat, O., Ko, J.-H.: The effects of quality management practices on customer satisfaction and innovation: a perspective from Jordan. Int. J. Prod. Quality Manage. 8(4), 398–415 (2011)

    Google Scholar 

  62. Cohen, B.H.: Explaining psychological statistics. John Wiley & Sons (2008)

    Google Scholar 

  63. Hahs-Vaughn, D.L.: Applied multivariate statistical concepts. Routledge (2016)

    Google Scholar 

  64. Dirin, A., Alamäki, A., Suomala, J.: Gender Differences in Perceptions of Conventional Video, Virtual Reality and Augmented Reality. pp. 93-103 (2019)

    Google Scholar 

  65. Knogler, M., et al.: How situational is situational interest? investigating the longitudinal structure of situational interest. Contemp. Educ. Psychol. 43, 39–50 (2015)

    Google Scholar 

  66. Linnenbrink-Garcia, L., Patall, E.A., Messersmith, E.E.: Antecedents and consequences of situational interest. Br. J. Educ. Psychol. 83(4), 591–614 (2013)

    Google Scholar 

  67. Tsai, Y.-M., et al.: What makes lessons interesting? the role of situational and individual factors in three school subjects. J. Educ. Psychol. 100(2), 460 (2008)

    Google Scholar 

  68. Ramkissoon, H., Nunkoo, R.: More than just biological sex differences: examining the structural relationship between gender identity and information search behavior. J. Hospitality Tourism Res. 36(2), 191–215 (2012)

    Google Scholar 

  69. Lynott, P.P., McCandless, N.J.: The impact of age vs. life experience on the gender role attitudes of women in different cohorts. J. Women Aging, 12(1–2), 5-21 (2000)

    Google Scholar 

  70. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manage. Sci. 35(8), 982–1003 (1989)

    Google Scholar 

  71. Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991)

    Google Scholar 

  72. Park, C., et al.: Adoption of multimedia technology for learning and gender difference. Comput. Hum. Behav. 92, 288–296 (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Honam University, Gwangju, 62399, Korea

    Jie Sun & Chao Gu

  2. Jiangnan University, Wuxi, 214013, China

    Jiangjie Chen, Chun Yang & Qianling Jiang

  3. Changshu Institute of Technology, Changshu, 215500, China

    Wei Wei

Authors
  1. Jie Sun
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  2. Chao Gu
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  3. Jiangjie Chen
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  4. Wei Wei
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  5. Chun Yang
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  6. Qianling Jiang
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Corresponding author

Correspondence to Chao Gu .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Qin Gao

  2. Chongqing University, Chongqing, China

    Jia Zhou

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Sun, J., Gu, C., Chen, J., Wei, W., Yang, C., Jiang, Q. (2022). A Study of the Effects of Interactive AI Image Processing Functions on Children’s Painting Education. In: Gao, Q., Zhou, J. (eds) Human Aspects of IT for the Aged Population. Design, Interaction and Technology Acceptance. HCII 2022. Lecture Notes in Computer Science, vol 13330. Springer, Cham. https://doi.org/10.1007/978-3-031-05581-2_8

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  • DOI: https://doi.org/10.1007/978-3-031-05581-2_8

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