Summary of Pahi, K., et al. (2024)

Background & Existing Knowledge
Learning to code with AI tools can feel abstract or inaccessible to beginners. Active learning strategies grounded in visualization and feedback can make technical content more engaging and inclusive.

New Insights from This Study
✔ Codeopticon and VizProg supported personalized learning in AI education.
✔ Students using visual and feedback-rich tools showed stronger conceptual grasp.
✔ Collaborative and hands-on formats boosted motivation and understanding.

Practical Applications: How Educators Can Use This Research

Active Coding Environments
📌 Use real-time code visualization to reduce abstraction.
📌 Provide live feedback to guide students’ debugging and design.
📌 Let students explore multiple solutions to a task.

Inclusive AI Teaching
📌 Design tools that support non-linear and visual thinkers.
📌 Incorporate group work for peer learning and support.
📌 Offer flexible pacing with checkpoints and review loops.

Assessment Design
📌 Evaluate understanding through student-created examples.
📌 Use rubrics that value reasoning and creativity.
📌 Provide opportunities for reflective documentation of learning.

Conclusion
Teaching AI through personalized, active strategies enhances deep understanding, motivation, and accessibility—especially for diverse learners entering the coding world.

Key Words
AI education, active learning, feedback tools, personalized learning, student engagement

Reference
Pahi, K., Hawlader, S., Hicks, E., Zaman, A., & Phan, V. (2024). Enhancing Active Learning Through Collaboration Between Human Teachers and Generative AI. Computers and Education Open, 6, 100183. https://doi.org/10.1016/j.caeo.2024.100183

Suggested Citation
Ross, E. M., & Malar, D. B. J. (2025). Learning with Human Teachers and Generative AI. Excellent Educator, 2(12), 8.