Teaching technical subjects like database design and SQL requires more than just delivering answers—it’s about guiding students to think critically and solve problems independently. To achieve this, I used the Reflection approach, one of the four agentic AI design patterns highlighted by Andrew Ng.
In the Reflection pattern, the AI iteratively critiques and improves its own outputs, fostering deeper understanding and better results. This methodology allows my customized chatbot to engage students through open-ended questions, progressive hints, and thoughtful feedback. Instead of simply providing answers, the bot helps students reflect on their responses, refine their thinking, and arrive at solutions themselves.
This agentic approach ensures that students don’t just learn the “how” of database design and SQL but also the “why,” developing critical thinking and problem-solving skills essential for their growth.
In this guide, I’ll walk you through how to build a Socratic-style mentor using MyGPT and the Reflection framework to teach database design and SQL.
Scope: Determine the key concepts you want students to learn in database design and SQL. For example, topics could include entity-relationship diagrams (ERD), normalization, SQL queries (SELECT, JOIN, etc.), and database optimization.
Learning Goals: Clearly define what students should be able to do, such as "Design an ERD for a small database" or "Write SQL queries to retrieve data from multiple tables."
The Socratic method revolves around asking guiding questions to stimulate critical thinking. In MyGPT:
Open-Ended Questions: Instead of providing direct answers, ask open-ended questions to help students think through the problem. Example: "What do you think is the role of primary keys in a database?"
Follow-up Questions: Based on students’ responses, guide them deeper into the topic. Example: "Why do you think normalization might be necessary in database design? Can you think of any potential problems if it's not applied?"
Hints: Offer progressively detailed hints as students work through the problem, helping them to discover the answer. Example: "Have you considered how many relationships are required between these two tables?"
Use GPT’s customization options to tailor the model:
Fine-Tune GPT: If needed, fine-tune the model using a dataset containing examples of Socratic dialogue in database design and SQL.
Custom Prompts: Create custom prompts to simulate a dialogue for specific concepts in your lessons. For example: "The student is learning SQL joins. Begin by asking how they would retrieve data from two tables and guide them to discover how INNER JOIN works."
Provide real-world examples to demonstrate the relevance of concepts:
Database Design Cases: Offer a problem, such as creating a database for an e-commerce system, and ask questions that help students structure the database (e.g., "What entities do you think would be involved in this system?")
SQL Query Writing: Use interactive challenges where the AI prompts students to write queries step-by-step, correcting them along the way.
Interactive Feedback: Allow the AI to give feedback on incorrect answers without giving away the solution, prompting students to reflect. Example: "You’ve selected a Cartesian product with your JOIN. How might you modify this to only get related records?"
Progress Reports: Create a feedback mechanism where the AI summarizes the student’s progress. For example: "You've mastered basic SQL queries. Would you like to explore more advanced topics like subqueries?"
W3Schools, Oracle, or SQLZoo: Reference these resources within the dialogue. For example, if a student is stuck, the AI could suggest: "You might want to review W3Schools’ tutorial on SQL JOINs."
Simulated Environments: Integrate SQL playgrounds (like SQL Fiddle or W3School's SQL simulator) where students can test their queries.
Pilot the Bot: Test the chatbot with a small group of students, gathering feedback on the effectiveness of the Socratic dialogue.
Iterate: Based on feedback, tweak the question patterns, hints, and prompts to ensure they are both challenging and supportive.
OpenAI GPT Customization: Utilize OpenAI's API to create a customized bot that focuses on Socratic dialogue for specific tasks.
Dialogflow or Rasa: These platforms can help manage user intent, making the conversational experience more fluid and tailored to individual learning needs.
LMS Integration: If using Blackboard or other LMS systems, you could integrate the bot as an instructional tool in discussion forums or as a virtual assistant.
This approach will encourage critical thinking in students, helping them develop not just technical skills but problem-solving abilities that are essential for mastering database design and SQL.
Training MyGPT to use Socratic dialogue for teaching database design and SQL involves crafting a well-structured approach to both training the model and writing effective instructions or questions. Here’s how you can train MyGPT:
First, you’ll need a dataset that models the kind of interactions you want. This dataset should include conversations where the teacher (MyGPT) asks open-ended questions, gives progressive hints, and prompts students to reflect on their answers.
Example Dialogue:
Teacher: “What tables would you create to store information for a library management system?”
Student: “I would create a ‘Books’ table.”
Teacher: “Great start! What other entities might be involved in this system? Think about the relationships between books and borrowers.”
Building the Dataset:
Subject-Specific Dialogue: Use real-world SQL problems as the basis for dialogue. Include topics like normal forms, primary and foreign keys, and query optimization.
Corrective Prompts: Incorporate examples where the bot doesn’t directly tell the student they’re wrong, but instead asks follow-up questions. Example: “How do you think the lack of an index might affect query performance?”
MyGPT needs clear instructions to help it respond in a way that guides learning. Here’s how to set up prompts:
General Guidelines:
Instruct MyGPT to ask more questions than give direct answers.
Make the bot respond based on the student’s input by analyzing the problem or scenario.
Sample Instructions to MyGPT:
“When the student asks for help on a query, start by asking them how they might approach it before providing step-by-step hints.”
“If the student is confused about primary keys, ask them what they think a primary key should uniquely identify in a database.”
“Guide the student to troubleshoot their SQL errors by asking about the logic behind each clause they wrote.”
Below are examples of questions MyGPT should ask in a Socratic manner, based on different areas of database design and SQL.
Entity-Relationship Diagrams (ERD):
“What entities do you think need to be represented in this system? Why?”
“How do these entities relate to one another? Can you describe their relationship in terms of one-to-many or many-to-many?”
Normalization:
“What is the main reason for normalizing a database? Can you think of any potential issues if normalization is not applied?”
“How would you decide whether to split a table into two during normalization?”
SQL Queries:
“What tables would you include in a query to get the necessary data? What relationships do these tables have?”
“What might happen if you write a query without specifying the WHERE clause when joining tables? Can you think of a possible consequence?”
Indexes and Optimization:
“How might indexing affect the performance of your query?”
“Can you think of a scenario where an index might not improve performance?”
Instead of directly providing answers, MyGPT can offer hints. Start with general hints, and then become more specific as needed:
Example of Progressive Hints for Writing a SQL Query:
Hint 1: “What do you think you need to SELECT in this query to retrieve data from multiple tables?”
Hint 2: “Have you considered how you’ll JOIN these tables together? What key connects them?”
Hint 3: “Check your WHERE clause. Does it limit the results correctly?”
Feedback Loop:
After the student responds, MyGPT can follow up with questions like: “Why do you think this approach works?” or “Can you explain how you structured this query?”
Train MyGPT to address common errors in SQL and database design by asking reflective questions. For example:
Error: Missing a WHERE clause when joining tables.
Response: “I noticed your query returns more rows than expected. How does the absence of a WHERE clause affect the result set?”
Error: Using SELECT *.
Response: “Using SELECT * can retrieve unnecessary data. What might be the advantage of explicitly specifying the columns?”
MyGPT should motivate students to keep exploring. Examples of encouraging responses:
“You’re on the right track! What else could you consider to optimize this design?”
“Good thought process! Let’s build on that idea by considering how foreign keys might improve data integrity.”
After setting up these prompts and question sets, test MyGPT with actual students:
Gather feedback on how well it engages students in critical thinking.
Fine-tune the prompts and dialogue based on what works best to stimulate thought and problem-solving without frustration.
By following these guidelines and refining MyGPT with targeted instruction, you’ll develop a Socratic tutor that helps students master database design and SQL through deep understanding, rather than rote learning.