<link rel="stylesheet" href="styles.f3b1fba60ec7970c.css">

ASSIGNING AI: SEVEN APPROACHES FOR STUDENTS WITH PROMPTS

Autordc.contributor.authorMollick, Ethan
Autordc.contributor.authorMollick, Lilach
Data de entradadc.date.accessioned2026-08-07T10:17:47Z
Data de disponibilizaçãodc.date.available2026-08-07T10:17:47Z
Data de publicaçãodc.date.issued2023
Descriçãodc.description46p.pt_PT
Resumodc.description.abstractThis 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, AIsimulator, 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 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 proposed framework offers a guide for educators navigating the integration of AI-assisted learning in classrooms.pt_PT
URIdc.identifier.urihttps://biblioteca.unisced.edu.mz/handle/123456789/779
Editoradc.publisherPennsylvania & Wharton Interactivept_PT
Assuntodc.subjectInteligencia artificialpt_PT
Assuntodc.subjectInteligencia artificial na educacaopt_PT
Títulodc.titleASSIGNING AI: SEVEN APPROACHES FOR STUDENTS WITH PROMPTSpt_PT
Tipodc.typeArticlept_PT

Ficheiros

Pacote original

A mostrar 1 - 1 de 1
A carregar...
Miniatura
Nome:
ASSIGNING AI SEVEN APPROACHES FOR STUDENTS WITH PROMPTS.pdf
Tamanho:
2,74 MB
Formato:
Adobe Portable Document Format
Descrição:

Licença do pacote

A mostrar 1 - 1 de 1
A carregar...
Miniatura
Nome:
license.txt
Tamanho:
1,71 KB
Formato:
Item-specific license agreed upon to submission
Descrição: