The Algorithmic Turn in K-12 Online and Blended Learning: Is AI a Pedagogical Mirage or a New Foundation for Student Agency?
By: Aras Bozkurt
References
- *Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431–440. 10.1007/s43681-021-00096-7
- Apetorgbor, M., Narad, S., Akpabio, E., Dhawale, C., Konto, A., & Verma, P. (2024). Leveraging artificial intelligence for effective assessment and evaluation in education: A comprehensive review. In: 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI) (pp. 1–6).
IEEE . 10.1109/IDICAIEI61867.2024.10842940 - Artsın, M., Bozkurt, A., & Sani-Bozkurt, S. (2025). Safe havens or silent traps? A critical quest for security policies, measures and practices for generative AI in the educational settings. In Proceedings of 17th International Conference on Education and New Learning Technologies Conference (EDULEARN25) (p.
3355 ).30 June-2 July, 2025 . Palma, Mallorca, Spain. 10.21125/edulearn.2025.0884 - *Arriola-Mendoza, J., & Valerio-Ureña, G. (2024). Essential elements for implementing AI tools in elementary school: A systematic literature review. Education Sciences, 14(12), Article
1292 . 10.3390/educsci14121292 - Bandura, A. (2006). Toward a psychology of human agency. Perspectives on Psychological Science, 1(2), 164–180. 10.1111/j.1745-6916.2006.00011.x
- *Bărbulescu, R., Munteanu, P., & Costache, B. (2025).
Empowering educators through AI-driven personalized learning in K-12 education . In EDULEARN25 Proceedings (pp. 4197–4206). IATED. 10.21125/edulearn.2025.1093 - *Boulhrir, T., Ghreir, H., Hamash, M., & Robert, M. (2026). Artificial Intelligence in Education: Mapping Adaptive Learning and Learning Analytics in K–12 Online, Virtual, and Distance Learning. The International Review of Research in Open and Distributed Learning, 27(2), 122–148. 10.19173/irrodl.v27i2.9370
- Bozkurt, A. (2024). GenAI et al.: Cocreation, Authorship, Ownership, Academic Ethics and Integrity in a Time of Generative AI. Open Praxis, 16(1), 1–10. 10.55982/openpraxis.16.1.654
- Bozkurt, A. (2025). Algorithmically Manufactured Minds: Generative and Agentic AI in a Time of Post-Truth, Reconfiguration of Student Agency and Death of Critical Pedagogy. Open Praxis, 17(2), 206–210. 10.55982/openpraxis.17.2.792
- Bozkurt, A., Xiao, J., Farrow, R., Bai, J. Y. H., Nerantzi, C., Moore, S., Dron, J., Stracke, C. M., Singh, L., Crompton, H., Koutropoulos, A., Terentev, E., Pazurek, A., Nichols, M., Sidorkin, A. M., Costello, E., Watson, S., Mulligan, D., Honeychurch, S., Hodges, C. B., Sharples, M., Swindell, A., Frumin, I., Tlili, A., Slagter van Tryon, P. J., Bond, M., Bali, M., Leng, J., Zhang, K., Cukurova, M., Chiu, T. K. F., Lee, K., Hrastinski, S., Garcia, M. B., Sharma, R. C., Alexander, B., Zawacki-Richter, O., Huijser, H., Jandrić, P., Zheng, C., Shea, P., Duart, J. M., Themeli, C., Vorochkov, A., Sani-Bozkurt, S., Moore, R. L., & Asino, T. I. (2024). The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the Future. Open Praxis, 16(4), 487–513. 10.55982/openpraxis.16.4.777
- Bozkurt, A., & Sharma, R. C. (2021). In pursuit of the right mix: Blended learning for augmenting, enhancing, and enriching flexibility. Asian Journal of Distance Education, 16(2), i–vi. 10.5281/zenodo.5827159
- *Cheng, Y. M., & Chen, P. F. (2012). Autonomous pedagogical agents to e-learning in elementary school. Journal of Information Hiding and Multimedia Signal Processing, 3(4), 370–380.
https://bit.kuas.edu.tw/~jihmsp/2012/vol3/JIH-MSP-2012-04-006.pdf - Chiu, T. K. F. (2025).
Empowering K-12 education with AI: Preparing for the future of education and work . In Empowering K-12 Education with AI: Preparing for the Future of Education and Work. Routledge. 10.4324/9781003498377-8 - Christensen, C. M., Horn, M. B., & Staker, H. (2013). Is K–12 blended learning disruptive? An introduction to the theory of hybrids. Clayton Christensen Institute.
- Christyodetaputri, J. H., & Marwa, N. (2024). Realizing ethical and equitable assessment in global education through artificial intelligence. Sinergi International Journal of Education, 2(3), 170–186. 10.61194/education.v2i3.590
- Cope, B., & Kalantzis, M. (2026).
Platformed learning: Reshaping education in the era of learning management systems . In D. A. Thomas & V. Laterza (Eds.), Critical perspectives on EdTech in higher education (pp. 129–173). Springer. 10.1007/978-3-031-88173-2_7 - *Crompton, H., & Burke, D. (2022). Artificial intelligence in K-12 education. SN Social Sciences, 2(7). 10.1007/s43545-022-00425-5
- *Crompton, H., Burke, D., Bozkurt, A., Nickel, C., Miao, F., Pegrum, M., Curry, J., Parsons, D., Edmett, A., Lindsey, L., Garcia, M. B., Chigona, A., Bonk, C. J., Bellas, F., Benali, M., Yongpradit, P., Palalas, A., de Bem Machado, A., Tinmaz, H., … Yu, S. (2026a). A framework for generative AI policy and guidelines in K-12. Journal of Research on Technology in Education, 1–22. 10.1080/15391523.2026.2661641
- Crompton, H., Burke, D., Nickel, C., Bozkurt, A., Miao, F., Sharples, M., Greene, J. A., Parsons, D., Gill-Simmen, L., Edmett, A., Pegrum, M., de Waard, I., Bonk, C. J., Garcia, M. B., Curry, J. H., Lindsey, L., Yang, M., Marshall, S., Bali, M., … Yu, S. (2026b). Governing generative AI in higher education: A global Delphi study on policy and practice. International Journal of Educational Technology in Higher Education, 23(1), Article
21 . 10.1186/s41239-026-00602-z - *Crompton, H., Jones, M. V., & Burke, D. (2024). Affordances and challenges of artificial intelligence in K-12 education: a systematic review. Journal of Research on Technology in Education, 56(3), 248–268. 10.1080/15391523.2022.2121344
- Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024). Impact of AI assistance on student agency. Computers & Education, 210,
104967 . 10.1016/j.compedu.2023.104967 - Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. 10.1016/j.jbusres.2021.04.070
- Fayyad, U., Grinstein, G. G., & Wierse, A. (Eds.). (2002). Information visualization in data mining and knowledge discovery. Morgan Kaufmann.
- Fedders, B. (2019). The constant and expanding classroom: Surveillance in K–12 public schools. North Carolina Law Review, 97(6), 1673–1726.
https://scholarship.law.unc.edu/cgi/viewcontent.cgi?article=6749&context=nclr - Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023). Adaptive learning using artificial intelligence in e-learning: A literature review. Education Sciences, 13(12),
1216 . 10.3390/educsci13121216 - González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021). Artificial intelligence for student assessment: A systematic review. Applied Sciences, 11(12),
5467 . 10.3390/app11125467 - Graham, C. R., Borup, J., Short, C. R., & Archambault, L. (2019). K-12 blended teaching: A guide to personalized learning and online integration. EdTech Books. 10.59668/2
- Hangartner, J., Hürzeler, D., & Aebli, N. (2025). Everyday approaches to platform-mediated personalized learning in secondary schools. Learning, Media and Technology, 50(4), 467–481. 10.1080/17439884.2024.2367051
- Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526. 10.1007/s40593-021-00239-1
- Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial Intelligence-Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194,
104684 . 10.1016/j.compedu.2022.104684 - *Huang, R., Yin, Y., Zhou, N., & Lang, F. (2026). Artificial intelligence in K-12 education: An umbrella review. Computers and Education: Artificial Intelligence, 10,
100519 . 10.1016/j.caeai.2025.100519 - *Hung, J.-L., Rice, K., & Zhang, M. (2025). Interpretable AI techniques unveil the factors and types of at-risk early warning: A case study in K-12 online learning. Data Technologies and Applications, 59(3), 516–538. 10.1108/DTA-11-2023-0795
- Janahi, Y., & Obeidat, M. (2024). Predictive analytics and AI in education: A systematic literature review on identifying and supporting at-risk students. SSRN. 10.2139/ssrn.5240920
- *Jiali, S., Dayo, F., Jun, G., Shuangyao, L., & Najam, S. (2024). The impact of artificial intelligence on personalized learning in education: A systematic review. Pakistan Journal of Life and Social Sciences, 22(2), 7412–7428. 10.57239/PJLSS-2024-22.2.00560
- Lo, C. K., Hew, K. F., & Jong, M. S.-y. (2024). The influence of ChatGPT on student engagement: A systematic review and future research agenda. Computers & Education, 219,
105100 . 10.1016/j.compedu.2024.105100 - *Martin, F., Zhuang, M., & Schaefer, D. (2024). Systematic review of research on artificial intelligence in K-12 education (2017–2022). Computers and Education: Artificial Intelligence, 6,
100195 . 10.1016/j.caeai.2023.100195 - Mingfeng, L., & Wang, Y. (2026). Artificial intelligence in the classroom: Implications for pedagogical design and student agency in secondary education (Version 1) [Preprint]. Research Square. 10.21203/rs.3.rs-8166344/v1
- *Mintz, J., Holmes, W., Liu, L., & Perez-Ortiz, M. (2023). Artificial intelligence and K-12 education: Possibilities, pedagogies and risks. Computers in the Schools, 40(4), 325–333. 10.1080/07380569.2023.2279870
- *Ni, K., Dai, H. M., & Brown, C. (2025). Secondary school students’ learning persistence in human-AI hybrid learning: The supplementary role of parental mediation. British Journal of Educational Technology, 56(6), 2227–2250. 10.1111/bjet.13563
- Ogunsakin, I. (2024). Unlocking The Potential of Artificial Intelligence: A New Paradigm for Assessment in 21st Century Education. International Journal of Research in STEM Education, 6(2), 37–49. 10.33830/ijrse.v6i2.1698
- Okoh, C. (2024, May). What communities should know about artificial intelligence, the school-to-prison pipeline, and school surveillance. Center for Law and Social Policy.
- *Ong, A. K. S., Cuales, J. C., Custodio, J. P. F., Gumasing, E. Y. J., Pascual, P. N. A., & Gumasing, M. J. J. (2023). Investigating preceding determinants affecting primary school students online learning experience utilizing deep learning neural network. Sustainability, 15(4),
3517 . 10.3390/su15043517 - Öncü, S. E., Bozkurt, A., & Sharma, R. C. (2026). Who Steers Odysseus’s Ship? The Nostos from Generative to Agentic AI in Distance Learning. Asian Journal of Distance Education, 21(1), i–vii. 10.5281/zenodo.21302129
- Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., & Bassey, B. A. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia Journal of Mathematics, Science and Technology Education, 19(8),
em2307 . 10.29333/ejmste/13428 - 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., Hrobjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. The BMJ, 372(1). 10.1136/bmj.n71
- Paludi, M. (2024). Safeguarding privacy and data protection rights in AI-enhanced education and learning analytics: An interdisciplinary approach in secondary high school educational settings. In Proceedings of the Doctoral Consortium of the 19th European Conference on Technology Enhanced Learning (EC-TEL 2024).
https://ceur-ws.org/Vol-3927/paper12.pdf - Puvvadi, M., Arava, S. K., Santoria, A., Chennupati, S. S. P., & Puvvadi, H. V. (2025). Generative AI for Personalized Learning and Education. In 7th International Conference on Intelligent Sustainable Systems (ICISS) (pp. 1621–1627).
IEEE . 10.1109/ICISS63372.2025.11076239 - Queiroga, E. M., Batista Machado, M. F., Paragarino, V. R., Primo, T. T., & Cechinel, C. (2022). Early prediction of at-risk students in secondary education: A countrywide K-12 learning analytics initiative in Uruguay. Information, 13(9),
401 . 10.3390/info13090401 - *Rizvi, S., Waite, J., & Sentance, S. (2023). Artificial intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review. Computers and Education: Artificial Intelligence, 4, Article
100145 . 10.1016/j.caeai.2023.100145 - *Rukadikar, A. V., & Khandelwal, K. (2023). Adoption of artificial intelligence in personalising e-learning: A helping hand for K-12 teachers. In: Proceedings of the 2023 7th International Conference on New Media Studies (CONMEDIA) (pp. 94–98).
IEEE . 10.1109/CONMEDIA60526.2023.10428508 - Sadegh-Zadeh, S.-A., Movahhedi, T., & Bahonar, F. (2026). Emotion AI in the classroom: Ethics of monitoring student affect through facial and vocal analytics. AI and Ethics, 6(1),
37 . 10.1007/s43681-025-00897-0 - *Selvakumar, V., Venkata, T. P., Venkata, T. P., & Singh, S. (2023). Predicting primary and middle-school students’ preferences for online learning with machine learning. South African Journal of Childhood Education, 13(1), 1–6. 10.4102/sajce.v13i1.1324
- Selwyn, N. (2011). Education and technology: Key issues and debates. Bloomsbury Academic
- *Silva, P. H. D. D., Sudasinghe, S. A. V. D., Hansika, P. D. U., Gamage, M. P., & Gamage, M. P. A. W. (2021). AI base e-learning solution to motivate and assist primary school students. In Proceedings of the 2021 3rd International Conference on Advancements in Computing (ICAC) (pp. 294–299).
IEEE . 10.1109/ICAC54203.2021.9671209 - Smith, A. E., & Humphreys, M. S. (2006). Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping. Behavior Research Methods, 38(2), 262–279. 10.3758/BF03192778
- Stenbom, S., Garrison, D. R., & Bozkurt, A. (2026). Augmenting Inquiry, Preserving the Core: Stenbom and Garrison on AI’s Role and Human-Centered Learning Within the Community of Inquiry (CoI) Framework. Open Praxis, 18(1), 181–191. 10.55982/openpraxis.18.1.1042
- Thurmond, V. A. (2001). The point of triangulation. Journal of Nursing Scholarship, 33(3), 253–258. 10.1111/j.1547-5069.2001.00253.x
- Topping, K. J., Douglas, W., Robertson, D., & Ferguson, N. (2022). Effectiveness of online and blended learning from schools: A systematic review. Review of Education, 10(2),
e3353 . 10.1002/rev3.3353 - Üstün, A. G., Kayali, B., Yavuz, M., & Bozkurt, A. (2026b). Surveillance or Support? A Systematic Literature Review of Artificial Intelligence Supported Proctored Online Exams. Turkish Online Journal of Distance Education, 27(2), 1–24. 10.17718/tojde.1674490
- Üstün, A. G., Yavuz, M., Kayalı, B., Uçar, H., Erdoğdu, E., & Bozkurt, A. (2026a). Trust in AI: scale development and validation for online distance learners. BMC Psychology, 14, 938(2026). 10.1186/s40359-026-04679-z
- Vahedian Movahed, S., & Martin, F. G. (2025). Ask Me Anything: Exploring children’s attitudes toward an age-tailored AI-powered chatbot. International Journal of Artificial Intelligence in Education, 35, 3979–4001. 10.1007/s40593-025-00523-4
- van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(2008), 2579–2605.
http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf - *Wang, N., & Lester, J. (2023). K-12 education in the age of AI: A call to action for K-12 AI literacy. International Journal of Artificial Intelligence in Education, 33(2), 228–232. 10.1007/s40593-023-00358-x
- *Wang, Z., Mei, Y., & Ouyang, J. (2025).
Parental perceptions and acceptance of intelligent control in AI-enhanced K-12 digital education platforms: A technology acceptance model approach . In G. Dou (Ed.), Design Studies and Intelligence Engineering: Proceedings of DSIE 2024 (pp. 672–680). SAGE Publications. 10.3233/FAIA250320 - *Wijanto, M. C., Karnalim, O., & Tan, R. (2022). Work in progress: High school students’ perspective on assessment question types during online learning — Preliminary study for automated assessments of open-ended questions. In Proceedings of the 2022 IEEE World Engineering Education Conference (EDUNINE) (pp. 1–4).
IEEE . 10.1109/EDUNINE53672.2022.9782327 - *Worden, E., Vanacore, K., Haim, A., & Heffernan, N. (2025, July). Scaling effective AI-generated explanations for middle school mathematics in online learning platforms. In Proceedings of the Twelfth ACM Conference on Learning @ Scale (pp. 40–49).
ACM . 10.1145/3698205.3729546 - *Wu, E. H.-K., Lin, C.-H., Ou, Y.-Y., Liu, C.-Z., Wang, W.-K., & Chao, C.-Y. (2020). Advantages and constraints of a hybrid model K-12 e-learning assistant chatbot. IEEE Access, 8, 77788–77801. 10.1109/ACCESS.2020.2988252
- Xia, Q., Liu, Q., Tlili, A., & Chiu, T. K. F. (2026). A systematic literature review on designing self-regulated learning using generative artificial intelligence and its future research directions. Computers & Education, 240,
105465 . 10.1016/j.compedu.2025.105465 - *Yim, I. H. Y., & Su, J. (2025). Artificial intelligence (AI) learning tools in K-12 education: A scoping review. Journal of Computers in Education, 12(1), 93–131. 10.1007/s40692-023-00304-9
- *Yue, M., Jong, M. S.-Y., & Dai, Y. (2022). Pedagogical design of K-12 artificial intelligence education: A systematic review. Sustainability, 14(23),
15620 . 10.3390/su142315620 - *Zayet, T. M. A., Ismail, M. A., Almadi, S. H. S., Zawia, J. M. H., & Mohamad Nor, A. (2023). What is needed to build a personalized recommender system for K-12 students’ E-Learning? Recommendations for future systems and a conceptual framework. Education and Information Technologies, 28(6), 7487–7508. 10.1007/s10639-022-11489-4
- Zhang, Q., & Fan, J. (2026). Beyond substitution: How teacher interpersonal behaviours empower students to communicate with AI—A dual-mediation model of enjoyment and trust. European Journal of Education, 61,
e70499 . 10.1111/ejed.70499 - Zhang, S., Davis Jaldi, C., Schroeder, N. L., López, A. A., Gladstone, J. R., & Heidig, S. (2024). Pedagogical agent design for K-12 education: A systematic review. Computers & Education, Article
105177 . 10.1016/j.compedu.2024.105165 - *Zhao, J., Sitthiworachart, J., & Ratanaolarn, T. (2025). The impact of AI-integrated sport blended learning on primary school students’ sports skills and attitudes. The Open Sports Sciences Journal, 18(1). 10.2174/011875399X397619250721071324
- Zhou, X., Li, Y., Chai, C. S., & Chiu, T. K. F. (2025). Defining, enhancing, and assessing artificial intelligence literacy and competency in K-12 education from a systematic review. Interactive Learning Environments, 33(10), 5766–5788. 10.1080/10494820.2025.2487538
- Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. 10.1207/s15430421tip4102_2
DOI: https://doi.org/10.55982/openpraxis.18.3.1378 | Journal eISSN: 2304-070X
Language: English
Page range: 408 - 427
Submitted on: Jul 17, 2026
Accepted on: Jul 20, 2026
Published on: Aug 4, 2026
Published by: International Council for Open and Distance Education (ICDE)
In partnership with: Paradigm Publishing Services
Keywords:
© 2026 Aras Bozkurt, published by International Council for Open and Distance Education (ICDE)
This work is licensed under the Creative Commons Attribution 4.0 License.