Designing Safe and Accountable GenAI as a Learning Companion with Women Banned from Formal Education

• URL: http://arxiv.org/abs/2604.07253v1
• Date: Wed, 08 Apr 2026 16:14:26 GMT
• Title: Designing Safe and Accountable GenAI as a Learning Companion with Women Banned from Formal Education
• Abstract summary: In gender-restrictive contexts, women often turn to online self-learning and generative AI (GenAI) to pursue educational and career aspirations.<n>We present a remote participatory design study with 20 women in Afghanistan to examine how participants envision GenAI for learning and employability.<n>We show that accountable and safe GenAI is not only about harm reduction but can also actively enable women to imagine and pursue viable learning and employment futures.
• Score: 1.4337580941451356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In gender-restrictive and surveilled contexts, where access to formal education may be restricted for women, pursuing education involves safety and privacy risks. When women are excluded from schools and universities, they often turn to online self-learning and generative AI (GenAI) to pursue their educational and career aspirations. However, we know little about what safe and accountable GenAI support is required in the context of surveillance, household responsibilities, and the absence of learning communities. We present a remote participatory design study with 20 women in Afghanistan, informed by a recruitment survey (n = 140), examining how participants envision GenAI for learning and employability. Participants describe using GenAI less as an information source and more as an always-available peer, mentor, and source of career guidance that helps compensate for the absence of learning communities. At the same time, they emphasize that this companionship is constrained by privacy and surveillance risks, contextually unrealistic and culturally unsafe support, and direct-answer interactions that can undermine learning by creating an illusion of progress. Beyond eliciting requirements, envisioning the future with GenAI through participatory design was positively associated with significant increases in participants' aspirations (p=.01), perceived agency (p=.01), and perceived avenues (p=.03). These outcomes show that accountable and safe GenAI is not only about harm reduction but can also actively enable women to imagine and pursue viable learning and employment futures. Building on this, we translate participants' proposals into accountability-focused design directions that center on safety-first interaction and user control, context-grounded support under constrained resources, and offer pedagogically aligned assistance that supports genuine learning rather than quick answers.

Related papers

• Quick Build, Careful Check? Generative AI Use in Hackathons [83.0493885475068]
  We report an exploratory interview study with participants at an AI-themed hackathon in central Europe.<n>We found that participants used GenAI for purposes beyond coding, including learning unfamiliar topics, brainstorming, and preparing documentation.<n>We also found that all participants converged on an unwritten practice of checking GenAI output before using it, yet their ability to actually verify was constrained by time pressure and limited domain knowledge.
  arXiv  Detail & Related papers  (2026-07-31T08:59:18Z)
• Brainrot: Deskilling and Addiction are Overlooked AI Risks [48.33701084123215]
  The scope of AI safety and alignment work in generative artificial intelligence (GenAI) has so far mostly been limited to harms related to discrimination and hate speech.<n>The public conversation around AI has also been focusing on threats to our cognition, mental health, and welfare at large.<n>Examples include deskilling associated with cognitive offloading and the atrophy of critical thinking as a result of over-reliance on GenAI systems.
  arXiv  Detail & Related papers  (2026-05-05T08:49:51Z)
• Exploring Student Perception on Gen AI Adoption in Higher Education: A Descriptive Study [1.978884131103313]
  This study examines how students perceive, use, and evaluate Generative Artificial Intelligence (GenAI)<n>Data were collected through a questionnaire administered to 436 postgraduate Computer Science students at the University of Hertfordshire.
  arXiv  Detail & Related papers  (2026-03-29T17:19:39Z)
• Whose Knowledge Counts? Co-Designing Community-Centered AI Auditing Tools with Educators in Hawai`i [47.69971984043272]
  generative AI is being deployed into classrooms with promises of aiding teachers.<n> educators caution that these tools can have unintended pedagogical repercussions.<n>We investigate these challenges in Hawaii, where public schools operate under a statewide mandate to integrate Hawaiian language and culture into education.
  arXiv  Detail & Related papers  (2026-03-17T15:17:08Z)
• Generative AI in Saudi Arabia: A National Survey of Adoption, Risks, and Public Perceptions [0.6010778467667774]
  Generative Artificial Intelligence (GenAI) is rapidly becoming embedded in Saudi Arabia's digital transformation under Vision 2030.<n>This study provides an early snapshot of GenAI engagement among Saudi nationals.
  arXiv  Detail & Related papers  (2026-01-26T07:40:41Z)
• Generative AI for Biosciences: Emerging Threats and Roadmap to Biosecurity [56.331312963880215]
  generative artificial intelligence (GenAI) in the biosciences is transforming biotechnology, medicine, and synthetic biology.<n>This Perspective outlines the current state of GenAI in the biosciences and emerging threat vectors ranging from jailbreak attacks and privacy risks to the dual-use challenges posed by autonomous AI agents.<n>We advocate a multi-layered approach to GenAI safety, including rigorous data filtering, alignment with ethical principles during development, and real-time monitoring to block harmful requests.
  arXiv  Detail & Related papers  (2025-10-13T00:24:41Z)
• The GenAI Generation: Student Views of Awareness, Preparedness, and Concern [1.5709900716890133]
  Outpacing the development of uniform policies and structures, GenAI has heralded a unique era and given rise to the GenAI Generation.<n>This study examines students' perceptions of GenAI through a concise survey with optional open-ended questions.<n>Students with greater curricular exposure to GenAI tend to feel more prepared, while those without it more often express vulnerability and uncertainty.
  arXiv  Detail & Related papers  (2025-05-04T19:37:13Z)
• Generative AI in Education: Student Skills and Lecturer Roles [0.0]
  This study aims to identify and evaluate the key competencies students need to effectively engage with GenAI in education.<n>The literature review identified 14 essential student skills for GenAI engagement, with AI literacy, critical thinking, and ethical AI practices emerging as the most critical.<n>In our study of lecturer strategies, we identified six key areas, with GenAI Integration and Curriculum Design being the most emphasised.
  arXiv  Detail & Related papers  (2025-04-28T10:58:30Z)
• Computational Safety for Generative AI: A Signal Processing Perspective [65.268245109828]
  computational safety is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI.<n>We show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts.<n>We discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.
  arXiv  Detail & Related papers  (2025-02-18T02:26:50Z)
• 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.<n>The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations.<n>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)
• Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
  generative AI, particularly large language models (LLMs), become increasingly integrated into production applications. New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
  arXiv  Detail & Related papers  (2024-09-23T10:18:10Z)
• Innovating Computer Programming Pedagogy: The AI-Lab Framework for Generative AI Adoption [0.0]
  We introduce "AI-Lab," a framework for guiding students in effectively leveraging GenAI within core programming courses. By identifying and rectifying GenAI's errors, students enrich their learning process. For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.
  arXiv  Detail & Related papers  (2023-08-23T17:20:37Z)

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.