Author(s)

Hongchang Li ¹

Affiliation(s)

¹ The High School of Guiyang Affiliated to Beijing Normal University, Guiyang, Guizhou, China

Corresponding Author

Hongchang Li

ABSTRACT

Generative learning analytics (GLA) is an emerging paradigm that embeds generative artificial intelligence into learning analytics, aiming to move from mere description toward actionable intervention. Instructional Design-Driven Generative Learning Analytics (IDD-GLA) refers to a systematic practice in which teachers, guided by instructional design, predefine the problem framework, data dimensions, and intervention strategies for learning analytics, and then rely on generative AI to interpret data, generate reports, and recommend pedagogical actions. This approach organically links analytics with instruction. IDD-GLA consists of five phases: diagnosing instructional problems, designing the analytical framework, AI-supported data interpretation, generating intervention strategies, and instructional reflection with iteration. The paper illustrates the value of this model through three typical application scenarios—personalized learning pathways, critical thinking cultivation, and data-informed teacher research—so as to offer a practical reference for researchers and practitioners.

KEYWORDS

"AI + Education," generative learning analytics, instructional design-driven, human–AI collaborative assessment, data-driven instruction

CITE THIS PAPER

Hongchang Li. Instructional Design-Driven Generative Learning Analytics: A New Field under the "AI + Education" Initiative. Curriculum and Teaching Methodology (2026). Vol. 9, No. 3, 44-49. DOI: http://dx.doi.org/10.23977/curtm.2026.090306.

REFERENCES

[1] Yang, X.M., Zeng, J.Y., & Li, X. (2025). Refined scenarios and practical paths for the deep integration of artificial intelligence and education: An exploratory multi-case analysis. Open Education Research, 31(1), 82-92.
[2] Gu, X.Q., Liu, Y., & Hu, Y.L. (2017). The application of learning analytics technology: Seeking data-supported solutions for learning improvement. Open Education Research, 23(2), 104-112.
[3] Zhu, Z.T., & Hu, J. (2024). Internal logic and practical paths of generative artificial intelligence empowering evidence-based teaching. e-Education Research, 45(5), 32-39.
[4] Lockyer, L., Heathcote, E., and Dawson, S. (2013) Informing Pedagogical Action: Aligning Learning Analytics with Learning Design. American Behavioral Scientist, 57(10), 1439-1459.
[5] Gu, X.Q., Du, H., Peng, H.C., & Zhu, Z.T. (2021). Theoretical framework, practical paths, development context and future prospect of smart education. Journal of East China Normal University (Educational Sciences), 39(8), 1-18.
[6] Bozkurt, A., Xiao, J., Lambert, S., et al. (2025) Machine Learning and Generative AI in Learning Analytics for Higher Education: A Systematic Review of Models, Trends, and Challenges. Applied Sciences, 15(15), 8679.
[7] Scherer, R., Siddiq, F., & Tondeur, J. (2023). Preparing teachers for artificial intelligence: Validating an AI-TPACK scale for preservice educators. Teaching and Teacher Education, 144, 104368.
[8] Lim, L., & Gasevic, D. (2020). Learning analytics dashboards for writing: How visualisation supports students' self-regulated academic writing. The Internet and Higher Education, 56, 100761.
[9] Zhang, Y., Yang, Z.H., & Qin, F. (2025). Risk research, attribution analysis and ecological governance of artificial intelligence empowering educational transformation. e-Education Research, 46(7), 22-29.
[10] Ng, S., & Carless, D. (2024). Student engagement with AI-generated analytic feedback: A feedback literacy lens on generative learning analytics. Assessment & Evaluation in Higher Education, 49(8), 1124-1140.
[11] Mandinach, E.B., and Gummer, E.S. (2016) What Does It Mean for Teachers to Be Data Literate: Laying Out the Skills, Knowledge, and Dispositions. Teaching and Teacher Education, 60, 366-376.
[12] Celik, I. (2023) Towards Intelligent-TPACK: An Empirical Study on Teachers' Professional Knowledge to Ethically Integrate Artificial Intelligence (AI)-Based Tools into Education. Computers in Human Behavior, 138, 107468.

Sponsors, Associates, and Links

---

• Advances in Educational Technology and Psychology
  
  
• Transactions on Comparative Education
  
  
• Applied & Educational Psychology
  
  
• Advances in Vocational and Technical Education
  
  
• Adult and Higher Education