Student-Led Projects: Teaching Teens to Create Simple AI Agents

Artificial Intelligence (AI) is transforming the landscape of modern education, demanding not only technical skills from learners but also critical thinking, creativity, and ethical awareness. As educators, we are in a unique position to guide students in their first explorations of AI, helping them both understand its mechanisms and harness its power responsibly. One of the most impactful ways to do this is through student-led projects that invite teens to design and build their own simple AI agents. This approach not only demystifies AI but also empowers students to become creators rather than just consumers of technology.

Why Student-Led AI Projects Matter

Too often, technology education positions students as passive recipients. By contrast, student-led projects foster ownership, curiosity, and agency. When students design their own AI retrieval agents, they must define a problem, select appropriate data, and wrestle with the limitations and possibilities of AI tools. This process nurtures both technical proficiency and a nuanced understanding of AI’s societal implications.

The most meaningful learning happens when students feel empowered to ask their own questions and build their own solutions.

In this article, we outline a practical framework for running a project in which students build simple retrieval agents using Replit and OpenAI APIs. We provide a sample rubric, a safety and ethics checklist, and ideas for extending the project for more advanced learners.

Project Overview: Building a Simple AI Retrieval Agent

The objective is for each student (or small group) to create an AI agent that can retrieve information from a given dataset and respond to user queries in natural language. For example, students might build an agent that answers questions about European capitals, famous scientists, or environmental issues.

Learning Goals

• Understand the basics of how modern AI agents retrieve and process information.
• Develop hands-on skills in programming, using AI APIs, and managing data.
• Practice ethical reasoning and critical evaluation of AI outputs.
• Improve communication and collaboration skills through project work and presentations.

Required Tools and Resources

• Replit: A cloud-based coding platform suitable for collaborative Python projects.
• OpenAI API: Provides access to models like GPT-3.5 or GPT-4 for language processing tasks.
• A curated dataset for student agents to access (e.g., CSV file of facts, JSON of news articles).
• Basic internet access and student-friendly documentation on Python, APIs, and prompt engineering.

Step-by-Step Guide for Educators

1. Introduction and Context-Setting

Begin by exploring what an AI agent is and how it differs from traditional software. Use real-world examples, like chatbots or smart search tools, to illustrate the concept. Discuss the potential for both positive impact and misuse, emphasizing the importance of ethical design.

2. Problem Definition

Ask students to brainstorm potential topics or domains for their retrieval agent. Encourage them to think about what kinds of questions their agent should answer, and who the intended users might be. Possible themes include:

• European geography
• Famous figures in science and the arts
• Sustainable living tips
• Local history or culture

Guide students in narrowing down to a manageable scope, considering data availability and project timeline.

3. Data Collection and Preparation

Students select or are provided with a relevant dataset. This could be a teacher-curated CSV file or a small collection of text documents. Teach students the basics of data cleaning and organization, emphasizing the importance of data quality and representativeness.

*Remind students that “garbage in, garbage out” applies to AI—agents are only as good as the data they’re trained on or have access to.*

4. Coding the Retrieval Agent

Using Replit, students write a Python script that:

1. Accepts a user’s question as input (via chat or a simple form).
2. Searches the dataset for relevant information.
3. Uses OpenAI’s API to generate a natural-language response, possibly rephrasing or summarizing the retrieved data.

For beginners, provide scaffolded code snippets and clear documentation. More advanced students can experiment with prompt engineering, multi-step reasoning, or integrating multiple data sources.

5. Testing and Refinement

Students test their agents with a variety of questions, noting both successes and failure cases. Encourage peer testing: classmates can “break” each other’s agents, providing valuable feedback. Guide students to improve both the accuracy of retrieval and the clarity of responses.

6. Presenting and Reflecting

Each team shares their agent, demonstrating its capabilities and discussing challenges encountered. Facilitate a class discussion on:

• What worked well, and why?
• What limitations did students encounter?
• How could the project be extended or improved?
• What ethical issues arose?

Rubric for Assessment

Criteria	Excellent	Good	Developing
Technical Implementation	Agent retrieves and presents accurate information; code well-documented and functional.	Minor bugs or inaccuracies; mostly clear code.	Frequent errors; incomplete or unclear code.
Creativity and Problem-Solving	Unique topic, creative approach, thoughtful design decisions.	Solid topic and design; some innovative elements.	Conventional approach; limited originality.
Use of AI Tools	Effective use of OpenAI API; demonstrates understanding of prompt design.	API used correctly but with limited customization.	API use minimal or incorrect.
Ethical and Safety Awareness	Clear attention to data ethics, fairness, and user safety.	Some attention to ethical concerns.	Little or no evidence of ethical consideration.
Presentation and Reflection	Clear, engaging presentation; insightful reflection on process and challenges.	Competent presentation; some reflection.	Unclear or incomplete presentation; limited reflection.

Safety and Ethics Checklist

Any project involving AI, especially with students, must foreground safety and ethics. Use the following checklist to guide classroom discussions and student decisions:

• Data Privacy: Are students using data that contains personal information? If so, is it anonymized and used with permission?
• Bias and Fairness: Does the dataset represent a diverse range of perspectives? Are there risks of reinforcing stereotypes or misinformation?
• Transparency: Can users of the agent understand how its responses are generated and what data it uses?
• Safety: Could the agent give harmful or inappropriate answers? How are those risks mitigated (e.g., prompt engineering, content filters)?
• Legal Compliance: Are all software tools and datasets used in accordance with copyright and data protection laws such as GDPR?

Ethical AI is not just about following rules—it’s about developing a habit of mindful, critical questioning at every stage of creation.

Extension Ideas for Advanced Learners

For students or classes ready for deeper exploration, consider these extensions:

• Multi-Lingual Retrieval: Adapt the agent to work in multiple European languages, addressing translation challenges and cultural context.
• Emotionally Intelligent Agents: Use sentiment analysis to tailor responses based on user mood or intent.
• Explainability: Add a feature where the agent “explains” how it found its answer, promoting transparency.
• Responsible AI Scenarios: Ask students to imagine and address a misuse scenario (e.g., spreading misinformation), then design safeguards.
• Integration with Real-World Applications: Connect the retrieval agent to classroom tools, such as a school website FAQ or library catalog search.

Supporting European Educators: Legal and Contextual Considerations

For educators in Europe, understanding the legal and cultural context of AI use is essential. Discuss with students how regulations such as the General Data Protection Regulation (GDPR) shape data collection and sharing. Encourage students to think about the social impact of AI agents, especially when deployed in multilingual and multicultural environments.

Incorporate discussions about:

• Why privacy and consent matter in the digital age.
• How AI can amplify both positive and negative social dynamics.
• The importance of inclusivity in dataset selection and algorithm design.

Building AI agents is not just a technical exercise—it is a profound opportunity to engage students in the ethical stewardship of technology.

Fostering a Growth Mindset and Community of Inquiry

Perhaps the greatest value of student-led AI projects is in the habits of mind they cultivate. Mistakes become learning opportunities; uncertainty becomes an invitation to explore. Encourage students to work collaboratively, share their discoveries, and approach challenges with resilience and creativity.

Offer feedback that emphasizes process over perfection. Celebrate not only clever technical solutions but also thoughtful questions and ethical insights. Remind students—and ourselves—that the most powerful AI agents are those built with both intellectual rigor and human empathy.

In summary, by guiding students through the process of designing, building, and reflecting on simple AI agents, educators equip them with the skills, mindsets, and ethical foundations needed to thrive in an AI-driven future. The classroom becomes not only a site of technical training but also a laboratory for critical thinking, collaboration, and responsible innovation.