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Assigning AI: Seven Approaches for Students, with Prompts
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Assigning AI: Seven Approaches for Students, with Prompts
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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. 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
Forward citations
Cited by 7 Pith papers
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Curiosity as Linguistic Intervention: Using LLM Tutoring Dialogues to Influence Exploratory Learning Behavior
Curiosity-oriented linguistic interventions in LLM tutoring dialogues increased exploratory learner behaviors up to 2.4x across 270 conversations spanning multiple models and domains.
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Can the Recovery Mechanism Survive AI? Skill Formation, Labor, and What Current Measurement Misses
Generative AI may break the education-based recovery mechanism for technological displacement, as evidence shows performance gains without learning gains and current measurements miss the knowledge dimension of cognition.
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Scalable and Personalized Oral Assessments Using Voice AI
A deployed voice-AI oral exam graded by a deliberating three-LLM council is cheap and internally reliable (α=0.83–0.95), but its transferable value is the documented failure modes and the candidate patterns for fixing them.
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Characterizing Students' LLM Usage Behaviors and Their Association with Learning in Critical Thinking Tasks
Refined bottom-up categorization of LLM usage types in critical thinking homework, labeled by student initiative, shows associations with midterm performance across two course offerings.
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Characterizing Students' LLM Usage Behaviors and Their Association with Learning in Critical Thinking Tasks
Refined bottom-up categories of LLM usage in critical thinking homework, labeled by student initiative, are examined for associations with midterm performance across two course offerings.
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Can the Recovery Mechanism Survive AI? Skill Formation, Labor, and What Current Measurement Misses
Generative AI risks eroding the developmental process of learning by performing high-level cognitive work, creating a paradox where it helps current workers but may undermine future capacity building, requiring new ou...
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Scalable and Personalized Oral Assessments Using Voice AI
Viva conducts voice-based oral exams and grades transcripts with a multi-LLM panel; tested on two small NYU cohorts at under $1 per exam while surfacing five implementation patterns from observed failures.
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