Author(s)

Zhuohuang Zhang ¹

Affiliation(s)

¹ School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen, China

Corresponding Author

Zhuohuang Zhang

ABSTRACT

The integration of large language models into higher education has necessitated structural adjustments in traditional coding instruction. However, applying these generative tools to complex, cross-disciplinary subjects like "Intelligent Data Analysis" within vocational settings remains a practical challenge. This paper outlines a pedagogical framework that transitions from syntax-focused memorization to Visual Logic Verification. Rather than treating generative AI as an automated coding assistant, this approach reframes the technology as an object of critical analysis, intentionally utilizing it to generate plausible but statistically flawed outputs that stimulate student inquiry. By examining a specific financial time-series case study involving Bollinger Bands calculations, we illustrate how analyzing AI-generated visual anomalies can foster deep algorithmic comprehension. The paper also discusses strategies for code-free conceptual assessment and diagnostic scaffolding. In summary, this framework offers a practical model for modernizing vocational data science curricula, balancing the utility of AI tools with the necessity of rigorous academic standards.

KEYWORDS

Generative AI, Data Science Course, Visual Debugging, Vocational Education, Teaching Reform

CITE THIS PAPER

Zhuohuang Zhang. Cultivating Analytical Intuition: An AI-Integrated Pedagogical Framework for Vocational Data Science Courses. Curriculum and Teaching Methodology (2026). Vol. 9, No.1, 87-91. DOI: http://dx.doi.org/10.23977/curtm.2026.090112.

REFERENCES

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[2] Guzman, L. M., Pennell, M. W., Nikelski, E., & Srivastava, D. S. (2019). Successful integration of data science in undergraduate biostatistics courses using cognitive load theory. CBE—Life Sciences Education, 18(4), ar49.
[3] Franklin, D., Denny, P., Gonzalez-Maldonado, D. A., & Tran, M. (2025). Generative AI in computer science education: Challenges and opportunities. Cambridge University Press.
[4] Mazumder, S. F. (2024). Investigating Novice Programmers’ Mental Models (Doctoral dissertation, The University of North Carolina at Charlotte).
[5] Jalalitabar, M., Wang, Y., & Hu, Q. (2024, March). Teaching Programming Concepts with Visualization: A Systematic Overview and Lessons Learned. In 2024 IEEE integrated STEM education conference (ISEC) (pp. 1-7). IEEE.
[6] Yan, K., Wang, Y., & Li, Y. (2023). Enhanced Bollinger Band Stock Quantitative Trading Strategy Based on Random Forest. Artificial Intelligence Evolution, 22-33.

Sponsors, Associates, and Links

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