Article Sidebar

Download
PDF
Statistic
Read Counter : 49 Download : 23

Downloads

Download data is not yet available.

Main Article Content

Abstract

This study aims to develop and evaluate a deep learning model for the comprehensive assessment of divergent and convergent creativity dimensions. The dataset comprised 102 digital drawings obtained from Indonesian children aged 4 to 6 years using the Test for Creative Thinking - Drawing Production (TCT-DP). This study employed a quantitative model development approach, where ground-truth labels were derived from the 14 TCT-DP scoring criteria aggregated into divergent and convergent scores through label engineering. Using a Multi-Task Convolutional Neural Network (MT-CNN) based on MobileNetV2 architecture, the study analyzed extracted visual features to predict expert-rated scores. The results revealed a strong positive correlation (r = +0.51) between divergent and convergent thinking scores, challenging the traditional view of these processes as antagonistic and supporting an integrated model of creative cognition. From a technical perspective, the model demonstrated satisfactory predictive capability as a proof-of-concept, achieving a lower error rate for convergent scores (RMSE = 1.52) compared to divergent scores (RMSE = 1.97). It indicates that while structured convergent features are more machine-learnable, the abstract nature of divergent thinking remains a complex challenge. In conclusion, this study validates the feasibility of automated creativity assessment while offering empirical evidence for the interplay between generative and evaluative thinking in early childhood.

Keywords

creativity assessment deep learning early childhood multi-task learning computational creativity

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
How to Cite
Indarwati, A., Tupamahu, F., Suharti, S., Galih, F. H. E., & Joshi, S. R. (2025). Assessing Divergent and Convergent Creativity in Early Childhood Drawings: A Multi-Task Deep Learning Approach. Atfaluna Journal of Islamic Early Childhood Education, 8(2), 1-18. https://doi.org/10.32505/atfaluna.v8i2.12434

References

  1. Adinda, W. N., Wahyuni, S., & Majidah, K. (2020). Penilaian autentik pada pembelajaran kreativitas anak usia dini di Annur I Sleman Yogyakarta. Jurnal Raudhah, 8(1), 92–104. https://doi.org/10.30829/raudhah.v8i1.589
  2. Beghetto, R. A., & Kaufman, J. C. (2014). Classroom contexts for creativity. High Ability Studies, 25(1), 53–69. https://doi.org/10.1080/13598139.2014.905247
  3. Castellano, G., & Vessio, G. (2021). Deep learning approaches to pattern extraction and recognition in paintings and drawings: An overview. Neural Computing and Applications, 33(19), 12263–12282. https://doi.org/10.1007/s00521-021-05893-z
  4. Chandrasekera, T., Hosseini, Z., & Perera, U. (2025). Can artificial intelligence support creativity in early design processes? International Journal of Architectural Computing, 23(1), 122–136. https://doi.org/10.1177/14780771241254637
  5. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
  6. Cheng, Y., & Li, B. (2021). Image segmentation technology and its application in digital image processing. In 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) (pp. 1174–1177). IEEE. https://doi.org/10.1109/IPEC51340.2021.9421223
  7. Cong, T. (2025). The art of positivity in drawing: Unveiling the impact of positive mood states on visual creativity via deep learning. Preprint. arXiv:2501.00000
  8. Crawshaw, M. (2020). Multi-task learning with deep neural networks: A survey. arXiv preprint arXiv:2009.09796. https://arxiv.org/abs/2009.09796
  9. de Vink, I. C., Willemsen, R. H., Lazonder, A. W., & Kroesbergen, E. H. (2022). Creativity in mathematics performance: The role of divergent and convergent thinking. British Journal of Educational Psychology, 92(2), 484–501. https://doi.org/10.1111/bjep.12451
  10. Duval, P. E., Frick, A., & Denervaud, S. (2023). Divergent and convergent thinking across the school years: A dynamic perspective on creativity development. The Journal of Creative Behavior, 57(2), 186–198. https://doi.org/10.1002/jocb.545
  11. Gao, S. (2025). Creative generation and evaluation system of art design based on artificial intelligence. Discover Artificial Intelligence, 5(1), 118. https://doi.org/10.1007/s44247-024-00118-w
  12. Gerwig, A., Miroshnik, K., Forthmann, B., Benedek, M., Karwowski, M., & Holling, H. (2021). The relationship between intelligence and divergent thinking: A meta-analytic update. Journal of Intelligence, 9(2), 23. https://doi.org/10.3390/jintelligence9020023
  13. Hilmiah, H., & Salehudin, M. (2024). Peran TIK pada pembelajaran abad 21 dalam keterampilan kritis, kreatif, dan kolaboratif anak usia dini. Journal of Instructional and Development Researches, 4(6), 609–618. https://doi.org/10.53621/jider.v4i6.449
  14. Kellogg, R. (1970). Analyzing children's art. Mayfield Publishing Company.
  15. Lee, M., Kim, Y., & Kim, Y.-K. (2024). Generating psychological analysis tables for children’s drawings using deep learning. Data & Knowledge Engineering, 149, 102266. https://doi.org/10.1016/j.datak.2023.102266
  16. Marwiyati, S. (2021). Pembelajaran saintifik pada anak usia dini dalam pengembangan kreativitas di taman kanak-kanak. Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini, 5, 135–149. https://doi.org/10.31004/obsesi.v5i1.518
  17. Nath, S. S., & Stevenson, C. E. (2025). Pencils to pixels: A systematic study of creative drawings across children, adults and AI. arXiv preprint arXiv:2502.05999. https://arxiv.org/abs/2502.05999
  18. Oh, D., & Shin, B. (2022). Improving evidential deep learning via multi-task learning. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7895–7903. https://doi.org/10.1609/aaai.v36i7.20713
  19. Panfilova, A. S., Valueva, E. A., & Ilyin, I. Y. (2024). The application of explainable artificial intelligence methods to models for automatic creativity assessment. Frontiers in Artificial Intelligence, 7, 1310518. https://doi.org/10.3389/frai.2024.1310518
  20. Putri, U. L. N., Muhid, A., & Pratitis, N. T. (2025). Systematic literature review: Meningkatkan kreativitas siswa sekolah dasar melalui mind mapping dengan menggunakan artificial intelligence. Pendas: Jurnal Ilmiah Pendidikan Dasar, 10(1), 294–313. https://doi.org/10.23969/pendas.v10i1.5847
  21. Rawlings, S. B., Chetwynd-Talbot, D., Husband, E., Nuttall, A., Quinn, E., Taggart, R., & Roome, H. E. (2025). Divergent thinking is linked with convergent thinking: Implications for models of creativity. Thinking & Reasoning, 1–23. https://doi.org/10.1080/13546783.2025.2286125
  22. Rebelo, A. D. P., Inês, G. D. O., & Damion, D. E. V. (2022). The impact of artificial intelligence on the creativity of videos. ACM Transactions on Multimedia Computing, Communications, and Applications, 18(1), 1–27. https://doi.org/10.1145/3495000
  23. Retnaningsih, L. E., & Khairiyah, U. (2022). Kurikulum Merdeka pada pendidikan anak usia dini. SELING: Jurnal Program Studi PGRA, 8(2), 143–158. https://doi.org/10.29062/seling.v8i2.360
  24. Rofi’ah, U. A., Khotimah, N., & Lestari, P. I. (2023). Pengukuran kreativitas anak usia dini menurut EP Torrance. Alzam: Journal of Islamic Early Childhood Education, 3(1), 40–55. https://doi.org/10.37567/alzam.v3i1.2141
  25. Robinson, K. (2015). Creative schools: The grassroots revolution that’s transforming education. Penguin.
  26. Saretzki, J., Forthmann, B., & Benedek, M. (2024). A systematic quantitative review of divergent thinking assessments. Psychology of Aesthetics, Creativity, and the Arts. Advance online publication. https://doi.org/10.1037/aca0000691
  27. Seli, P., Ragnhildstveit, A., Orwig, W., Bellaiche, L., Spooner, S., & Barr, N. (2025). Beyond the brush: Human versus artificial intelligence creativity in the realm of generative art. Psychology of Aesthetics, Creativity, and the Arts. https://doi.org/10.1037/aca0000719
  28. Su, J., & Yang, W. (2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence, 3, 100049. https://doi.org/10.1016/j.caeai.2022.100049
  29. Urban, K. K., & Jellen, H. G. (1996). Test for Creative Thinking–Drawing Production (TCT–DP). Scholastic Testing Service.
  30. Vandenhende, S., Georgoulis, S., Van Gansbeke, W., Proesmans, M., Dai, D., & Van Gool, L. (2021). Multi-task learning for dense prediction tasks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7), 3614–3633. https://doi.org/10.1109/TPAMI.2021.3056206
  31. Wang, X., Hommel, B., Colzato, L., He, D., Ding, K., Liu, C., Qiu, J., & Chen, Q. (2023). The contribution of divergent and convergent thinking to visual creativity. Thinking Skills and Creativity, 49, 101372. https://doi.org/10.1016/j.tsc.2023.101372
  32. Wulandari, I. Y., Indroasyoko, N., Alti, R. M., Asri, Y. N., & Hidayat, R. (2022). Pengenalan sistem deteksi objek untuk anak usia dini menggunakan pemrograman Python. REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer, 6(4), 664–673.
  33. Xia, T., Kang, M., Chen, M., Ouyang, J., & Hu, F. (2021). Design training and creativity: Students develop stronger divergent but not convergent thinking. Frontiers in Psychology, 12, 695002. https://doi.org/10.3389/fpsyg.2021.695002
  34. Zhang, Y., & Yang, Q. (2021). A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering, 34(12), 5586–5609. https://doi.org/10.1109/TKDE.2021.3078523
  35. Zhao, F. (2024). Deep reinforcement learning enhances artistic creativity: A case study of program art students integrating computer deep learning. Journal of Intelligent Systems, 33(1), 20230292. https://doi.org/10.1515/jisys-2023-0292