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Pedagogical Impacts of Block-Based Artificial Intelligence Applications: A Systematic Review

Yıl 2026, Cilt: 34 Sayı: 1, 186 - 206, 31.01.2026
https://doi.org/10.24106/kefdergi.1878127

Öz

Purpose: The purpose of this systematic review is to methodically investigate the pedagogical effects of visual and block-based programming tools used in K–12 artificial intelligence (AI) education.
Method: A systematic review was conducted following the PRISMA 2020 guidelines, analyzing 91 articles published between 2019 and 2024 that met the inclusion criteria. The studies were evaluated considering the block-based tools used at various educational levels (elementary, middle, high school, K–12), the AI topics taught, their pedagogical effects, and recommendations for future research.
Results: The results show that visual and block-based programming tools are useful for improving students' comprehension of AI principles, problem-solving ability, computational thinking skills, and ethical awareness. The use of these technologies varies across different countries and educational levels; specifically, more advanced applications are observed in OECD member countries due to their superior infrastructure and resources.
Highlights: To increase the efficacy of AI education, the study emphasizes the need to strengthen teacher preparation, integrate ethical and social concerns into the curriculum, and develop real-world learning environments. It concludes that block-based and visual programming tools are essential components of K–12 AI instruction, holding significant potential to enhance students' cognitive, affective, and psychomotor skills.

Kaynakça

  • Alam, A. (2022). A digital game based learning approach for effective curriculum transaction for teaching-learning of artificial intelligence and machine learning. In 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 473–478). IEEE. https://doi.org/10.1109/ICSCDS54817.2022.9760932
  • Alturayeif, N., Alturaief, N., & Alhathloul, Z. (2020). DeepScratch: Scratch programming language extension for deep learning education. International Journal of Advanced Computer Science and Applications, 11(7). https://doi.org/10.14569/IJACSA.2020.0110777
  • Andersen, R., Mørch, A. I., & Litherland, K. T. (2022). Collaborative learning with block-based programming: Investigating human-centered artificial intelligence in education. Behaviour & Information Technology, 41(9), 1830–1847. https://doi.org/10.1080/0144929x.2022.2083981
  • Baldoni, M., Baroglio, C., Bucciarelli, M., Capecchi, S., Gandolfi, E., Gena, C., Ianì, F., Marengo, E., Micalizio, R., Rapp, A., & Ras, I. N. (2024). Does any AI-based activity contribute to develop AI conception? A case study with Italian fifth and sixth grade classes. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 21, pp. 23060–23068). AAAI Press. https://doi.org/10.1609/aaai.v38i21.30350
  • Basu, S. (2019). Using rubrics integrating design and coding to assess middle school students' open-ended block-based programming projects. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 372–378). ACM. https://doi.org/10.1145/3287324.3287412
  • Broll, B., & Grover, S. (2023). Beyond black-boxes: Teaching complex machine learning ideas through scaffolded interactive activities. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 13, pp. 15990–15998). AAAI Press. https://doi.org/10.1609/aaai.v37i13.26898
  • Carrisi, M. C., Marras, M., & Vergallo, S. (2025). A structured unplugged approach for foundational AI literacy in primary education [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2505.21398
  • Casal-Otero, L., Català, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K–12: A systematic literature review. International Journal of STEM Education, 10(1), Article 29. https://doi.org/10.1186/s40594-023-00418-7
  • Chiu, T. K. F., Meng, H., Chai, C.-S., King, I., Wong, S., & Yam, Y. (2022). Creation and evaluation of a pre-tertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30–39. https://doi.org/10.48550/arXiv.2101.07570
  • Druga, S., & Ko, A. J. (2021). How do children’s perceptions of machine intelligence change when training and coding smart programs? In Proceedings of the 20th Annual ACM Interaction Design and Children Conference (pp. 416–427). ACM. https://doi.org/10.1145/3459990.3460712
  • Druga, S., Vu, S. T., Likhith, E., & Qiu, T. (2019). Inclusive AI literacy for kids around the world. In Proceedings of FabLearn 2019: The 8th Annual Conference on Creativity and Fabrication in Education (pp. 1–8). ACM. https://doi.org/10.1145/3311890.3311904
  • Emerson, A., Geden, M., Smith, A., Wiebe, E., Mott, B., Boyer, K. E., & Lester, J. (2020). Predictive student modeling in block-based programming environments with Bayesian hierarchical models. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 280–289). ACM. https://doi.org/10.1145/3340631.3394853
  • Estevez, J., Garate, G., & Grana, M. (2019). Gentle introduction to artificial intelligence for high-school students using Scratch. IEEE Access, 7, 179027–179036. https://doi.org/10.1109/access.2019.2956136
  • García, J. D. R., León, J. M., González, M. R., & Robles, G. (2019). Developing computational thinking at school with machine learning: An exploration. In 2019 International Symposium on Computers in Education (SIIE) (pp. 1–6). IEEE. https://doi.org/10.1109/SIIE48397.2019.8970124
  • Grover, S. (2024). Teaching AI to K-12 learners: Lessons, issues, and guidance. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (pp. 20–21). ACM. https://doi.org/10.1145/3626252.3630937
  • Grover, S., Broll, B., & Babb, D. (2023). Cybersecurity education in the age of AI: Integrating AI learning into cybersecurity high school curricula. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (pp. 121–127). ACM. https://doi.org/10.1145/3545945.3569750
  • Hsu, T.-C., Abelson, H., Lao, N., Tseng, Y.-H., & Lin, Y.-T. (2021). Behavioral-pattern exploration and development of an instructional tool for young children to learn AI. Computers and Education: Artificial Intelligence, 2, Article 100012. https://doi.org/10.1016/j.caeai.2021.100012
  • Hsu, T.-C., Abelson, H., & Van Brummelen, J. (2022). The effects on secondary school students of applying experiential learning to the conversational AI learning curriculum. The International Review of Research in Open and Distributed Learning, 23(1), 82–103. https://doi.org/10.19173/irrodl.v22i4.5474
  • Irgens, G. A., Vega, H., Adisa, I., & Bailey, C. (2022). Characterizing children’s conceptual knowledge and computational practices in a critical machine learning educational programme. International Journal of Child-Computer Interaction, 34, Article 100541. https://doi.org/10.1016/j.ijcci.2022.100541
  • Jayasuriya, S., Swisher, K., Rego, J. D., Chandran, S., Mativo, J., Kurz, T., Collins, C. E., Robinson, D. T., & Pidaparti, R. (2024). ImageSTEAM: Teacher professional development for integrating visual computing into middle school lessons. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23101–23109. https://doi.org/10.1609/aaai.v38i21.30355
  • Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016). Artificial intelligence and computer science in education: From kindergarten to university. In 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1–9). IEEE. https://doi.org/10.1109/FIE.2016.7757353
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Blok Tabanlı Yapay Zekâ Uygulamalarının Pedagojik Etkileri: Sistematik Bir Derleme

Yıl 2026, Cilt: 34 Sayı: 1, 186 - 206, 31.01.2026
https://doi.org/10.24106/kefdergi.1878127

Öz

Amaç: Bu çalışmanın amacı, K–12 düzeyinde yapay zekâ (YZ) öğretiminde kullanılan görsel ve blok tabanlı programlama araçlarının pedagojik etkilerini sistematik biçimde incelemektir.
Yöntem: PRISMA 2020 standartlarına uygun olarak yürütülen sistematik derleme kapsamında, 2019–2024 yılları arasında yayımlanan ve belirlenen ölçütleri karşılayan 91 makale analiz edilmiştir. İnceleme sürecinde farklı eğitim kademelerinde (ilkokul, ortaokul, lise, K–12) kullanılan araçlar, YZ ile öğretilen konular, pedagojik etkiler ve gelecekteki araştırmalara yönelik öneriler dikkate alınmıştır.
Bulgular: Bulgular, görsel ve blok tabanlı programlama araçlarının öğrencilerin YZ kavramlarına yönelik anlayışlarını, problem çözme becerilerini, hesaplamalı düşünme yetilerini ve etik farkındalıklarını geliştirmede etkili olduğunu göstermektedir. Bu teknolojilerin kullanım düzeyi ülkeler arasında ve eğitim kademelerine göre değişiklik göstermekte; özellikle OECD üyesi ülkelerde daha gelişmiş altyapı ve kaynaklar nedeniyle daha ileri düzeyde kullanılmaktadır.
Önemli Vurgular: Çalışma, YZ eğitimini daha etkili hâle getirmek için öğretmen eğitimlerinin güçlendirilmesi, müfredata etik ve sosyal boyutların dâhil edilmesi ve uygulamaya dayalı öğrenme ortamlarının geliştirilmesi gerektiğini vurgulamaktadır. Sonuç olarak, blok tabanlı ve görsel programlama araçlarının K–12 YZ öğretiminin önemli bileşenleri olduğu ve öğrencilerin bilişsel, duyuşsal ve psikomotor becerilerini geliştirmeye katkı sağlayabileceği belirtilmektedir.

Kaynakça

  • Alam, A. (2022). A digital game based learning approach for effective curriculum transaction for teaching-learning of artificial intelligence and machine learning. In 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 473–478). IEEE. https://doi.org/10.1109/ICSCDS54817.2022.9760932
  • Alturayeif, N., Alturaief, N., & Alhathloul, Z. (2020). DeepScratch: Scratch programming language extension for deep learning education. International Journal of Advanced Computer Science and Applications, 11(7). https://doi.org/10.14569/IJACSA.2020.0110777
  • Andersen, R., Mørch, A. I., & Litherland, K. T. (2022). Collaborative learning with block-based programming: Investigating human-centered artificial intelligence in education. Behaviour & Information Technology, 41(9), 1830–1847. https://doi.org/10.1080/0144929x.2022.2083981
  • Baldoni, M., Baroglio, C., Bucciarelli, M., Capecchi, S., Gandolfi, E., Gena, C., Ianì, F., Marengo, E., Micalizio, R., Rapp, A., & Ras, I. N. (2024). Does any AI-based activity contribute to develop AI conception? A case study with Italian fifth and sixth grade classes. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 21, pp. 23060–23068). AAAI Press. https://doi.org/10.1609/aaai.v38i21.30350
  • Basu, S. (2019). Using rubrics integrating design and coding to assess middle school students' open-ended block-based programming projects. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 372–378). ACM. https://doi.org/10.1145/3287324.3287412
  • Broll, B., & Grover, S. (2023). Beyond black-boxes: Teaching complex machine learning ideas through scaffolded interactive activities. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 13, pp. 15990–15998). AAAI Press. https://doi.org/10.1609/aaai.v37i13.26898
  • Carrisi, M. C., Marras, M., & Vergallo, S. (2025). A structured unplugged approach for foundational AI literacy in primary education [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2505.21398
  • Casal-Otero, L., Català, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K–12: A systematic literature review. International Journal of STEM Education, 10(1), Article 29. https://doi.org/10.1186/s40594-023-00418-7
  • Chiu, T. K. F., Meng, H., Chai, C.-S., King, I., Wong, S., & Yam, Y. (2022). Creation and evaluation of a pre-tertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30–39. https://doi.org/10.48550/arXiv.2101.07570
  • Druga, S., & Ko, A. J. (2021). How do children’s perceptions of machine intelligence change when training and coding smart programs? In Proceedings of the 20th Annual ACM Interaction Design and Children Conference (pp. 416–427). ACM. https://doi.org/10.1145/3459990.3460712
  • Druga, S., Vu, S. T., Likhith, E., & Qiu, T. (2019). Inclusive AI literacy for kids around the world. In Proceedings of FabLearn 2019: The 8th Annual Conference on Creativity and Fabrication in Education (pp. 1–8). ACM. https://doi.org/10.1145/3311890.3311904
  • Emerson, A., Geden, M., Smith, A., Wiebe, E., Mott, B., Boyer, K. E., & Lester, J. (2020). Predictive student modeling in block-based programming environments with Bayesian hierarchical models. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 280–289). ACM. https://doi.org/10.1145/3340631.3394853
  • Estevez, J., Garate, G., & Grana, M. (2019). Gentle introduction to artificial intelligence for high-school students using Scratch. IEEE Access, 7, 179027–179036. https://doi.org/10.1109/access.2019.2956136
  • García, J. D. R., León, J. M., González, M. R., & Robles, G. (2019). Developing computational thinking at school with machine learning: An exploration. In 2019 International Symposium on Computers in Education (SIIE) (pp. 1–6). IEEE. https://doi.org/10.1109/SIIE48397.2019.8970124
  • Grover, S. (2024). Teaching AI to K-12 learners: Lessons, issues, and guidance. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (pp. 20–21). ACM. https://doi.org/10.1145/3626252.3630937
  • Grover, S., Broll, B., & Babb, D. (2023). Cybersecurity education in the age of AI: Integrating AI learning into cybersecurity high school curricula. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (pp. 121–127). ACM. https://doi.org/10.1145/3545945.3569750
  • Hsu, T.-C., Abelson, H., Lao, N., Tseng, Y.-H., & Lin, Y.-T. (2021). Behavioral-pattern exploration and development of an instructional tool for young children to learn AI. Computers and Education: Artificial Intelligence, 2, Article 100012. https://doi.org/10.1016/j.caeai.2021.100012
  • Hsu, T.-C., Abelson, H., & Van Brummelen, J. (2022). The effects on secondary school students of applying experiential learning to the conversational AI learning curriculum. The International Review of Research in Open and Distributed Learning, 23(1), 82–103. https://doi.org/10.19173/irrodl.v22i4.5474
  • Irgens, G. A., Vega, H., Adisa, I., & Bailey, C. (2022). Characterizing children’s conceptual knowledge and computational practices in a critical machine learning educational programme. International Journal of Child-Computer Interaction, 34, Article 100541. https://doi.org/10.1016/j.ijcci.2022.100541
  • Jayasuriya, S., Swisher, K., Rego, J. D., Chandran, S., Mativo, J., Kurz, T., Collins, C. E., Robinson, D. T., & Pidaparti, R. (2024). ImageSTEAM: Teacher professional development for integrating visual computing into middle school lessons. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23101–23109. https://doi.org/10.1609/aaai.v38i21.30355
  • Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016). Artificial intelligence and computer science in education: From kindergarten to university. In 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1–9). IEEE. https://doi.org/10.1109/FIE.2016.7757353
  • Kaplan, R., & Meylani, R. (2025). Dimensions of artificial intelligence literacy: A qualitative synthesis of contemporary research literature. Journal of Computer and Education Research, 13(26), 790–812. https://doi.org/10.18009/jcer.1648380
  • Kaspersen, M. H., Bilstrup, K.-E. K., Van Mechelen, M., Hjorth, A., Bouvin, N. O., & Petersen, M. G. (2021). VotestratesML: A high school learning tool for exploring machine learning and its societal implications. In Proceedings of FabLearn Europe / MakeEd 2021 - An International Conference on Computing, Design and Making in Education (pp. 1–10). ACM. https://doi.org/10.1016/j.ijcci.2022.100539
  • Kazemitabaar, M., Chow, J., Ma, C. K. T., Ericson, B. J., Weintrop, D., & Grossman, T. (2023). Studying the effect of AI code generators on supporting novice learners in introductory programming. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1–16). ACM. https://doi.org/10.48550/arXiv.2302.07427
  • Kim, K., & Kwon, K. (2024). Tangible computing tools in AI education: Approach to improve elementary students' knowledge, perception, and behavioral intention towards AI. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12497-2
  • Kong, S.-C., Cheung, W. M.-Y., & Tsang, O. (2022). Evaluating an artificial intelligence literacy programme for empowering and developing concepts, literacy and ethical awareness in senior secondary students. Education and Information Technologies, 28(4), 4703–4724. https://doi.org/10.1007/s10639-022-11408-7
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  • Long, D., Teachey, A., & Magerko, B. (2022). Family learning talk in AI literacy learning activities. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1–13). ACM. https://doi.org/10.1145/3491102.3502091
  • McCarthy, J. (1987). Generality in artificial intelligence. Communications of the ACM, 30(12), 1029–1034. https://doi.org/10.1145/33447.33448 Memari, M., & Ruggles, K. (2025). Artificial intelligence in elementary STEM education: A systematic review of current applications and future challenges (arXiv:2511.00105). arXiv. https://doi.org/10.48550/arXiv.2511.00105
  • MoNE. (2023). FEYZA projesi [Republic of Türkiye, Ministry of National Education, FEYZA Project]. https://dogmprojeler.meb.gov.tr/www/feyza-projesi/icerik/4
  • Moreno-León, J., Vasco-González, M., Román-González, M., & Robles, G. (2024). Investigating the impact of programming activities on computational thinking and AI literacy in Spanish schools. In Proceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research (pp. 1–10). ACM. https://doi.org/10.1145/3677619.3678111
  • Ng, D. T. K., Su, J., & Chu, S. K. W. (2023). Fostering secondary school students’ AI literacy through making AI-driven recycling bins. Education and Information Technologies, 29(8), 9715–9746. https://doi.org/10.1007/s10639-023-12183-9
  • Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, J. K., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
  • Park, Y., & Shin, Y. (2021). Tooee: A novel Scratch extension for K-12 big data and artificial intelligence education using text-based visual blocks. IEEE Access, 9, 149630–149646. https://doi.org/10.1109/access.2021.3125060
  • Park, Y., & Shin, Y. (2022a). A block-based interactive programming environment for large-scale machine learning education. Applied Sciences, 12(24), Article 13008. https://doi.org/10.3390/app122413008
  • Park, Y., & Shin, Y. (2022b). Text processing education using a block-based programming language. IEEE Access, 10, 128484–128497. https://doi.org/10.1109/ACCESS.2022.3227765
  • Percival, N., Rayavaram, P., Narain, S., & Lee, C. S. (2022). CryptoScratch: Developing and evaluating a block-based programming tool for teaching K-12 cryptography education using Scratch. In 2022 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–6). IEEE. https://doi.org/10.48550/arXiv.2302.11606
  • Sabuncuoglu, A. (2020). Designing one year curriculum to teach artificial intelligence for middle school. In Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education (pp. 248–254). ACM. https://doi.org/10.1145/3341525.3387364
  • Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023). Developing middle school students’ understanding of machine learning in an African school. Computers and Education: Artificial Intelligence, 5, Article 100155. https://doi.org/10.1016/j.caeai.2023.100155
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  • Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching machine learning in K–12 classroom: Pedagogical and technological trajectories for artificial intelligence education. IEEE Access, 9, 110558–110572. https://doi.org/10.1109/access.2021.3097962
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  • Zhou, Z., Jin, J., Phadnis, V., Yuan, X., Jiang, J., Qian, X., He, J., & Du, R. (2024). Experiencing InstructPipe: Building multi-modal AI pipelines via prompting LLMs and visual programming. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1–8). ACM. https://doi.org/10.1145/3613905.3648656
Toplam 56 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Alan Eğitimleri (Diğer)
Bölüm Sistematik Derlemeler ve Meta Analiz
Yazarlar

Hüseyin Sıhat

Mehmet Akif Ocak

Gönderilme Tarihi 20 Kasım 2024
Kabul Tarihi 21 Kasım 2025
Yayımlanma Tarihi 31 Ocak 2026
Yayımlandığı Sayı Yıl 2026 Cilt: 34 Sayı: 1

Kaynak Göster

APA Sıhat, H., & Ocak, M. A. (2026). Pedagogical Impacts of Block-Based Artificial Intelligence Applications: A Systematic Review. Kastamonu Education Journal, 34(1), 186-206. https://doi.org/10.24106/kefdergi.1878127

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