Assigning AI: Seven Approaches for Students, with Prompts
It addresses the problem of effectively using AI as a supportive tool in classrooms for educators and students, but it is incremental as it builds on existing educational frameworks with AI.
The paper tackles the integration of Large Language Models in education by proposing seven AI approaches (e.g., AI-tutor, AI-coach) to enhance learning while mitigating risks like errors and biases, aiming to keep students as the 'human in the loop' for improved outcomes.
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. The aim is to help students learn with and about AI, with practical strategies designed to mitigate risks such as complacency about the AI's 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