Investigating Youth's Technical and Ethical Understanding of Generative Language Models When Engaging in Construction and Deconstruction Activities

• URL: http://arxiv.org/abs/2504.15132v1
• Date: Mon, 21 Apr 2025 14:30:16 GMT
• Title: Investigating Youth's Technical and Ethical Understanding of Generative Language Models When Engaging in Construction and Deconstruction Activities
• Authors: Luis Morales-Navarro,
• Abstract summary: This study investigates how engaging young people in the design and auditing of generative language models (GLMs) may foster the development of their understanding of how these systems work from both technical and ethical perspectives.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The widespread adoption of generative artificial intelligence/machine learning (AI/ML) technologies has increased the need to support youth in developing AI/ML literacies. However, most work has centered on preparing young people to use these systems, with less attention to how they can participate in designing and evaluating them. This study investigates how engaging young people in the design and auditing of generative language models (GLMs) may foster the development of their understanding of how these systems work from both technical and ethical perspectives. The study takes an in-pieces approach to investigate novices' conceptions of GLMs. Such an approach supports the analysis of how technical and ethical conceptions evolve and relate to each other. I am currently conducting a series of participatory design workshops with sixteen ninth graders (ages 14-15) in which they will (a) build GLMs from a data-driven perspective that glassboxes how data shapes model performance and (b) audit commercial GLMs by repeatedly and systematically querying them to draw inferences about their behaviors. I will analyze participants' interactions to identify ethical and technical conceptions they may exhibit while designing and auditing GLMs. I will also conduct clinical interviews and use microgenetic knowledge analysis and ordered network analysis to investigate how participants' ethical and technical conceptions of GLMs relate to each other and change after the workshop. The study will contribute (a) evidence of how engaging youth in design and auditing activities may support the development of ethical and technical understanding of GLMs and (b) an inventory of novice design and auditing practices that may support youth's technical and ethical understanding of GLMs.

Related papers

• Building babyGPTs: Youth Engaging in Data Practices and Ethical Considerations through the Construction of Generative Language Models [0.0]
  Youth are increasingly using generative language models (GLMs) in their everyday lives.<n>Most research has centered on supporting youth as users of GLM-powered systems.<n>This paper contributes a case study that demonstrates the feasibility of engaging youth in building GLMs.
  arXiv  Detail & Related papers  (2025-04-20T23:40:29Z)
• A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
  Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making.<n>With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems.<n>We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
  arXiv  Detail & Related papers  (2025-04-12T01:27:49Z)
• Large Language Model Agent: A Survey on Methodology, Applications and Challenges [88.3032929492409]
  Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence.<n>This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy.<n>Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time.
  arXiv  Detail & Related papers  (2025-03-27T12:50:17Z)
• LLM Agents for Education: Advances and Applications [49.3663528354802]
  Large Language Model (LLM) agents have demonstrated remarkable capabilities in automating tasks and driving innovation across diverse educational applications. This survey aims to provide a comprehensive technological overview of LLM agents for education, fostering further research and collaboration to enhance their impact for the greater good of learners and educators alike.
  arXiv  Detail & Related papers  (2025-03-14T11:53:44Z)
• Decoding Large-Language Models: A Systematic Overview of Socio-Technical Impacts, Constraints, and Emerging Questions [1.1970409518725493]
  The article highlights the application areas that could have a positive impact on society along with the ethical considerations. It includes responsible development considerations, algorithmic improvements, ethical challenges, and societal implications.
  arXiv  Detail & Related papers  (2024-09-25T14:36:30Z)
• Youth as Peer Auditors: Engaging Teenagers with Algorithm Auditing of Machine Learning Applications [0.44998333629984877]
  This paper positions youth as auditors of their peers' machine learning (ML)-powered applications. In a two-week workshop, 13 youth (ages 14-15) designed and audited ML-powered applications.
  arXiv  Detail & Related papers  (2024-04-08T21:15:26Z)
• Large Language Models for Education: A Survey and Outlook [69.02214694865229]
  We systematically review the technological advancements in each perspective, organize related datasets and benchmarks, and identify the risks and challenges associated with deploying LLMs in education. Our survey aims to provide a comprehensive technological picture for educators, researchers, and policymakers to harness the power of LLMs to revolutionize educational practices and foster a more effective personalized learning environment.
  arXiv  Detail & Related papers  (2024-03-26T21:04:29Z)
• A Survey on Knowledge Distillation of Large Language Models [99.11900233108487]
  Knowledge Distillation (KD) emerges as a pivotal methodology for transferring advanced capabilities to open-source models. This paper presents a comprehensive survey of KD's role within the realm of Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2024-02-20T16:17:37Z)
• Generative Artificial Intelligence in Learning Analytics: Contextualising Opportunities and Challenges through the Learning Analytics Cycle [0.0]
  Generative artificial intelligence (GenAI) holds significant potential for transforming education and enhancing human productivity. This paper delves into the prospective opportunities and challenges GenAI poses for advancing learning analytics (LA) We posit that GenAI can play pivotal roles in analysing unstructured data, generating synthetic learner data, enriching multimodal learner interactions, advancing interactive and explanatory analytics, and facilitating personalisation and adaptive interventions.
  arXiv  Detail & Related papers  (2023-11-30T07:25:34Z)
• The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice [64.29355073494125]
  This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation. We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
  arXiv  Detail & Related papers  (2023-10-02T05:30:42Z)
• Instruction Tuning for Large Language Models: A Survey [52.86322823501338]
  We make a systematic review of the literature, including the general methodology of supervised fine-tuning (SFT)<n>We also review the potential pitfalls of SFT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies.
  arXiv  Detail & Related papers  (2023-08-21T15:35:16Z)
• Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review [5.329514340780243]
  Large language models (LLMs) have the potential to automate the laborious process of generating and analysing textual content. There are concerns regarding the practicality and ethicality of these innovations. We conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research.
  arXiv  Detail & Related papers  (2023-03-17T18:14:46Z)
• Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" [76.44130385507894]
  This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
  arXiv  Detail & Related papers  (2021-11-01T17:57:04Z)

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.