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Titre :	Assigning AI: Seven Approaches for Students, with Prompts
Auteurs :	Ethan R. Mollick ; Lilach Mollick
Type de document :	texte imprimé
Année de publication :	2023
Index. décimale :	240 (Evaluation des apprentissages)
Catégories :	Évaluation des apprentissages ; Intelligence artificielle
Mots-clés:	Apprentissages ; Evaluation des apprentissages ; Intelligence artificielle
Résumé :	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’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.
En ligne :	https://papers.ssrn.com/abstract=4475995

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