AI in Lesson Planning: Improving Teacher Efficiency and Instructional Design
DOI:
https://doi.org/10.55606/jurripen.v4i2.5560Keywords:
AI, educational ethics, instructional planning, learning technology, teacher autonomyAbstract
Teachers are under increasing pressure to deliver personalized, standards-aligned instruction while managing time constraints and rising workloads. Traditional lesson planning often limits creativity and adaptability due to its complexity and repetitive demands. In response, Artificial Intelligence (AI) has emerged as a promising tool to support instructional planning. This study highlights how AI enhances teacher efficiency, simplifies administrative tasks, and supports differentiated, data-driven instruction. However, these benefits require thoughtful and responsible integration. AI adoption must include safeguards for data privacy, ensure algorithmic transparency so teachers understand the basis of system recommendations, and actively mitigate systemic bias that may disadvantage certain learner groups. Most importantly, teachers should remain actively involved in reviewing and adapting AI-generated content to preserve professional judgment and uphold pedagogical integrity.
Downloads
References
Aleven, V., Roll, I., McLaren, B. M., & Koedinger, K. R. (2018). Help helps, but only so much: Research on help-seeking with intelligent tutoring systems. International Journal of Artificial Intelligence in Education, 28(4), 593–618. https://doi.org/10.1007/s40593-017-0156-6
Beardsley, M., Wood, C., & Hansen, J. (2021). Universal Design for Learning: An AI-supported approach to inclusive instruction. Journal of Inclusive Education Research, 13(1), 22–34. https://doi.org/10.1080/10494820.2021.1877572
Blaik-Hourani, R., Rubaie, R. A., & Husseini, A. M. (2022). Educators’ perceptions of AI-based lesson planning in under-resourced schools: A qualitative exploration. Technology, Pedagogy and Education, 31(3), 343–359. https://doi.org/10.1080/1475939X.2022.2041239
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modeling. Education and Information Technologies, 25(5), 3443–3463. https://doi.org/10.1007/s10639-020-10125-9
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
Cheng, X., Wang, M., & Liu, Y. (2021). Teachers’ attitudes toward AI in education: A case study of smart lesson planning tools. British Journal of Educational Technology, 52(5), 2035–2050. https://doi.org/10.1111/bjet.13113
Chounta, I. A., & Avouris, N. (2019). Orchestrating learning analytics awareness: Aligning tools and pedagogical goals. British Journal of Educational Technology, 50(6), 3117–3134. https://doi.org/10.1111/bjet.12853
Dalton, B., & Proctor, C. P. (2021). Design for all learners: Universal Design for Learning. Educational Psychologist, 56(3), 163–180. https://doi.org/10.1080/00461520.2021.1916499
Delgado, A., Wardle, F., & Tissenbaum, M. (2022). Teachers as designers: Exploring the use of AI-powered tools for instructional planning. Computers in Human Behavior Reports, 8, 100216. https://doi.org/10.1016/j.chbr.2022.100216
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
Fischer, C., Pardos, Z. A., & Baker, R. S. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 44(1), 130–165. https://doi.org/10.3102/0091732X20903304
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Holmes, W., Bialik, M., Fadel, C., & Wang, X. (2022). Artificial Intelligence and the Future of Teaching and Learning. U.S. Department of Education, Office of Educational Technology.
Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68, 1961–1990. https://doi.org/10.1007/s11423-020-09795-5
Kizilcec, R. F. (2022). Algorithmic transparency and its effect on user trust in educational technology. Educational Technology Research and Development, 70, 1457–1475. https://doi.org/10.1007/s11423-021-10020-5
König, J., Jäger-Biela, D. J., & Glutsch, N. (2020). Adapting to online teaching during COVID-19 school closure: Teacher education and teacher competence effects. European Journal of Teacher Education, 43(4), 608–622. https://doi.org/10.1080/02619768.2020.1809650
Kose, H., & Ozturk, M. (2022). The effectiveness of AI-powered personalized learning systems: A meta-analysis. Computers & Education, 183, 104504. https://doi.org/10.1016/j.compedu.2022.104504
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2016). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633–706. https://scholarship.law.upenn.edu/penn_law_review/vol165/iss3/3
Lu, J., & Churchill, D. (2022). Teacher decision-making in the context of AI recommendations: Agency, ethics, and trust. Educational Media International, 59(2), 115–130. https://doi.org/10.1080/09523987.2022.2053467
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
Martinez-Maldonado, R., Clayphan, A., Kay, J., & Yacef, K. (2020). Supporting collaborative learning design using AI: Teachers as orchestrators. British Journal of Educational Technology, 51(5), 1586–1602. https://doi.org/10.1111/bjet.12938
Molenaar, I., Knoop-van Campen, C., & Hassler, B. (2021). Teacher dashboards in AI-supported classrooms: Opportunities and challenges for professional practice. Computers & Education, 166, 104143. https://doi.org/10.1016/j.compedu.2021.104143
Raji, I. D., Binns, R., Veale, M., Kleek, M., & Shadbolt, N. (2020). The fallacy of AI function without fairness. Communications of the ACM, 63(9), 139–144. https://doi.org/10.1145/3375627
Regan, P. M., & Jesse, J. (2019). Ethical challenges of edtech, big data and personalized learning: Twenty-first century student sorting and tracking. Ethics and Information Technology, 21(3), 167–179. https://doi.org/10.1007/s10676-019-09515-0
Schildkamp, K. (2022). The digital turn in education: Opportunities and challenges for teacher professional development. Teaching and Teacher Education, 111, 103625. https://doi.org/10.1016/j.tate.2021.103625
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
Sergis, S., & Sampson, D. G. (2020). Teaching and learning analytics to support teacher inquiry: A systematic literature review. IEEE Transactions on Learning Technologies, 13(4), 782–795. https://doi.org/10.1109/TLT.2020.2996311
Slade, S., & Prinsloo, P. (2020). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 64(5), 643–664. https://doi.org/10.1177/0002764218756950
Southgate, E., Reynolds, R., & Howley, M. (2019). Artificial intelligence and education: Perceptions of teachers in Australia. Australian Educational Researcher, 46(3), 341–361. https://doi.org/10.1007/s13384-019-00325-w
Suárez, Á., Salmerón, L., & Rodríguez, E. (2023). The impact of AI lesson planners on teaching innovation in bilingual education. Educational Technology & Society, 26(1), 54–68. https://doi.org/10.30191/ETS.v26i1.1234
Suri, H., & Clarke, D. (2009). Advancements in research synthesis methods: From a methodologically inclusive perspective. Review of Educational Research, 79(1), 395–430. https://doi.org/10.3102/0034654308326349
Sweller, J., Ayres, P., & Kalyuga, S. (2019). Cognitive load theory (2nd ed.). Springer.
Tsai, H. C., Hwang, G. J., & Hsu, C. C. (2021). Development of a personalized mobile English vocabulary learning app based on learner interest and learning performance. Educational Technology Research and Development, 69(1), 1–21. https://doi.org/10.1007/s11423-020-09819-4
Tsai, T. H., Chou, C. Y., & Lai, K. R. (2022). Human–AI collaboration in education: Toward trust-enhanced pedagogical support. Educational Technology & Society, 25(2), 25–37. https://doi.org/10.30191/ETS.v25i2.1201
Veletsianos, G., & Houlden, S. (2020). Radical flexibility and relationality as responses to education in times of crisis. Postdigital Science and Education, 2, 849–862. https://doi.org/10.1007/s42438-020-00120-3
Williamson, B. (2021). Education governance and datafication: AI ethics and algorithmic accountability in education policy. Educational Philosophy and Theory, 53(10), 962–978. https://doi.org/10.1080/00131857.2020.1739411
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2023). Trends and development in technology-enhanced adaptive/personalized learning: A review of top 100 highly cited articles. Computers & Education, 197, 104659. https://doi.org/10.1016/j.compedu.2023.104659
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16, 1–27. https://doi.org/10.1186/s41239-019-0171-0
Zhou, L., & Gao, Y. (2022). Understanding algorithmic opacity in intelligent education systems: Implications for teacher agency. AI & Society, 37(2), 751–762. https://doi.org/10.1007/s00146-021-01173-w
Zhou, X., Yu, Y., & Zeng, Z. (2021). Personalized learning: An AI-driven solution for learning diversity. Interactive Learning Environments, 29(6), 827–839. https://doi.org/10.1080/10494820.2019.1610456
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 JURNAL RISET RUMPUN ILMU PENDIDIKAN
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
_001.jpg)
