Assigning AI: Seven Approaches for Students, with Prompts

• URL: http://arxiv.org/abs/2306.10052v1
• Date: Tue, 13 Jun 2023 03:36:36 GMT
• Title: Assigning AI: Seven Approaches for Students, with Prompts
• Authors: Ethan Mollick, Lilach Mollick
• Abstract summary: This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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

Related papers

• "From Unseen Needs to Classroom Solutions": Exploring AI Literacy Challenges & Opportunities with Project-based Learning Toolkit in K-12 Education [0.3994567502796064]
  There is a growing need to equip K-12 students with AI literacy skills that extend beyond computer science. This paper explores the integration of a Project-Based Learning (PBL) AI toolkit into diverse subject areas, aimed at helping educators teach AI concepts more effectively.
  arXiv  Detail & Related papers  (2024-12-23T03:31:02Z)
• Who is Helping Whom? Student Concerns about AI- Teacher Collaboration in Higher Education Classrooms [8.888004194396643]
  This paper aims to understand how students perceive the implications of AI in Education in terms of classroom collaborative dynamics. We analyzed narratives from 65 participants, highlighting three challenges: AI decontextualizing of the educational context; AI-teacher cooperation with bias concerns and power disparities. We argue that for effective and ethical AI-facilitated cooperative education, future AIEd design must factor in the situated nature of implementation.
  arXiv  Detail & Related papers  (2024-12-19T02:35:01Z)
• AI in Education: Rationale, Principles, and Instructional Implications [0.0]
  Generative AI, like ChatGPT, can create human-like content, prompting questions about its educational role. The study emphasizes deliberate strategies to ensure AI complements, not replaces, genuine cognitive effort.
  arXiv  Detail & Related papers  (2024-12-02T14:08:07Z)
• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• Human-Centric eXplainable AI in Education [0.0]
  This paper explores Human-Centric eXplainable AI (HCXAI) in the educational landscape. It emphasizes its role in enhancing learning outcomes, fostering trust among users, and ensuring transparency in AI-driven tools. It outlines comprehensive frameworks for developing HCXAI systems that prioritize user understanding and engagement.
  arXiv  Detail & Related papers  (2024-10-18T14:02:47Z)
• Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
  We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
  arXiv  Detail & Related papers  (2024-07-03T15:38:57Z)
• Is AI Changing the Rules of Academic Misconduct? An In-depth Look at Students' Perceptions of 'AI-giarism' [0.0]
  This study explores students' perceptions of AI-giarism, an emergent form of academic dishonesty involving AI and plagiarism. The findings portray a complex landscape of understanding, with clear disapproval for direct AI content generation. The study provides pivotal insights for academia, policy-making, and the broader integration of AI technology in education.
  arXiv  Detail & Related papers  (2023-06-06T02:22:08Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.