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This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and limitations. The authors propose seven approaches for utilizing AI in classrooms: AI-tutor, AI-coach, AI-mentor, AI-teammate, AI-tool, AI-simulator, and AI-student, each with distinct pedagogical benefits and risks. Prompts are included for each of these approaches. The aim is to help students learn with and about AI, with practical strategies designed to mitigate risks such as complacency about the AI output, errors, and biases. These strategies promote active oversight, critical assessment of AI outputs, and complementarity of AI's capabilities with the students' unique insights. By challenging students to remain the "human in the loop," the authors aim to enhance learning outcomes while ensuring that AI serves as a supportive tool rather than a replacement. The proposed framework offers a guide for educators navigating the integration of AI-assisted learning in classrooms.
Study at a glance
- Question: How can educators use artificial intelligence in classrooms to support student learning while managing risks?
- Setting and data: Not stated in the abstract
- Method: Not stated in the abstract
Why it matters
Large language models offer potential as learning tools, but carry inherent risks like errors, biases, and student complacency. Integrating AI into classrooms requires addressing these limitations so that AI acts as a supportive tool rather than a replacement.
Key findings
- In classroom settings, AI can fulfill seven distinct educational roles—including tutor, coach, teammate, and simulator—each presenting unique pedagogical benefits and risks.
- Active student oversight and critical assessment of generated text help mitigate major risks, including factual errors, algorithmic bias, and learner complacency.
- Keeping learners actively in control ensures artificial intelligence functions as a supportive educational tool rather than a replacement for students.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
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Study design
