Educational Modules & Artifacts
Module 1: Prompting Foundations
Explore the fundamental principles of prompt engineering, including persona assignment and iterative refinement for pedagogical applications.
Module 2: Ethics & Accuracy
Analyzing the ethical dimensions of AI in education, focusing on hallucination detection and bias mitigation in automated artifacts.
Module 3: Advanced Frameworks
A deep dive into cross-platform integration and technical infrastructure optimization for large-scale educational utilities.
Advanced Strategies for Prompting and AI Frameworks
Cultivating the skill to interact with intelligent agents is vital for modern operations. This segment evaluates the methodology of linguistic engineering, detailing how to frame instructions that secure reliable, virtuous, and useful outcomes. Participants analyze the technical infrastructure of automation utilities, identifying ways to optimize productivity while maintaining strictly high standards of excellence. Contact dbengts2@hgtc.edu.
Module 1 Assignment Commentary-Â Task and Prompt Revision Summary
For my Module 1 artifact, I chose the task of creating a study plan for my CPT-128 course. I selected this because I needed a project plan format to help balance my personal, professional, and schoolwork. My first prompt was weak because it was too general and lacked a defined audience. Consequently, the AI only generated a schedule for a single week and failed to include adult learning theory or strategy tips.
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To improve the results, I revised my prompt by assigning the AI a specific persona a professional educator specializing in supporting autistic students. I also explicitly requested adult learning best practices, a concise tone, a table format, and added a visual reference file. The improved output was vastly better; it provided a master course schedule covering the entire term and included actionable study tips, like execution guidelines.
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However, human judgment was still required. While the AI successfully created a detailed table, it failed to correctly match the dates with my provided source documents because of the added context example. I had to manually verify the weeks, correct the dates, and align the deliverables accurately. This artifact shows that while a detailed prompt drastically improves structure, human oversight is essential for verifying factual accuracy.