In a new paper, Assigning AI: Seven Approaches for Students, with Prompts, Ethan Mollick and Lilach Mollick from the University of Pennsylvania Wharton School, examine the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and limitations. They propose seven approaches for utilizing AI in classrooms. These strategies, they say “promote active oversight, critical assessment of AI outputs, and complementation 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 seven strategies are:

⛏1. AI-tutor: This approach uses AI to increase knowledge by providing personalized instruction and feedback to students.

👨🏾‍🤝‍👨🏼 2. AI-coach: This approach uses AI to increase metacognition by helping students develop self-awareness and self-regulation skills.

👨‍🎓 3. AI-mentor: This approach uses AI to provide balanced, ongoing feedback to students, helping them to identify areas of strength and weakness.

🤹‍♂️ 4. AI-teammate: This approach uses AI to increase collaborative intelligence by facilitating group work and communication among students.

👩‍🍳 5. AI-tool: This approach uses AI to extend student performance by providing tools and resources that enhance learning and problem-solving.

👩‍🔬 6. AI-simulator: This approach uses AI to help with practice by providing simulations and scenarios that allow students to apply their knowledge and skills in a safe and controlled environment.

🧚‍♂️ 7. AI-student: This approach uses AI to check for understanding by assessing student learning and providing feedback in real-time.