Human-Centric eXplainable AI in Education

• URL: http://arxiv.org/abs/2410.19822v1
• Date: Fri, 18 Oct 2024 14:02:47 GMT
• Title: Human-Centric eXplainable AI in Education
• Authors: Subhankar Maity, Aniket Deroy,
• Abstract summary: 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.
• Score: 0.0
• License:
• Abstract: As artificial intelligence (AI) becomes more integrated into educational environments, how can we ensure that these systems are both understandable and trustworthy? The growing demand for explainability in AI systems is a critical area of focus. This paper explores Human-Centric eXplainable AI (HCXAI) in the educational landscape, emphasizing its role in enhancing learning outcomes, fostering trust among users, and ensuring transparency in AI-driven tools, particularly through the innovative use of large language models (LLMs). What challenges arise in the implementation of explainable AI in educational contexts? This paper analyzes these challenges, addressing the complexities of AI models and the diverse needs of users. It outlines comprehensive frameworks for developing HCXAI systems that prioritize user understanding and engagement, ensuring that educators and students can effectively interact with these technologies. Furthermore, what steps can educators, developers, and policymakers take to create more effective, inclusive, and ethically responsible AI solutions in education? The paper provides targeted recommendations to address this question, highlighting the necessity of prioritizing explainability. By doing so, how can we leverage AI's transformative potential to foster equitable and engaging educational experiences that support diverse learners?

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)
• 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)
• Generative AI Literacy: Twelve Defining Competencies [48.90506360377104]
  This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI. The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations. These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly.
  arXiv  Detail & Related papers  (2024-11-29T14:55:15Z)
• 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)
• Generative AI and Its Impact on Personalized Intelligent Tutoring Systems [0.0]
  Generative AI enables personalized education through dynamic content generation, real-time feedback, and adaptive learning pathways. Report explores key applications such as automated question generation, customized feedback mechanisms, and interactive dialogue systems. Future directions highlight the potential advancements in multimodal AI integration, emotional intelligence in tutoring systems, and the ethical implications of AI-driven education.
  arXiv  Detail & Related papers  (2024-10-14T16:01:01Z)
• The Responsible Development of Automated Student Feedback with Generative AI [6.008616775722921]
  Recent advancements in AI, particularly with large language models (LLMs), present new opportunities to deliver scalable, repeatable, and instant feedback. However, implementing these technologies also introduces a host of ethical considerations that must thoughtfully be addressed. One of the core advantages of AI systems is their ability to automate routine and mundane tasks, potentially freeing up human educators for more nuanced work. However, the ease of automation risks a tyranny of the majority'', where the diverse needs of minority or unique learners are overlooked.
  arXiv  Detail & Related papers  (2023-08-29T14:29:57Z)
• Assigning AI: Seven Approaches for Students, with Prompts [0.0]
  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.
  arXiv  Detail & Related papers  (2023-06-13T03:36:36Z)
• Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
  Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches. We explore this process with 43 AI practitioners and real end-users.
  arXiv  Detail & Related papers  (2022-11-12T21:54:05Z)
• 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.