arXiv subjects

Gennie Mansi

Publications and source records attributed to Gennie Mansi.

12 recordsLinked to original sources

arXiv2026

Concept Catalyst: Exploring Scrutable Interfaces to Structure K-12 Teacher Interactions with Generative AI

Purpose: This paper explores how to align AI-based tools with teachers' classroom needs by using scrutable interfaces -- interfaces that link an easily manipulable knowledge representation to an underlying AI model, so users can change the system's outputs without understanding its details. It provides an in-depth discussion and example of a scrutable interface that structures teachers' interactions with generative AI. This study aims to expand how and where scrutable interfaces are used in AI-based tools to support teachers, who have not been historically targeted in the design of scrutable systems. Design/Methodology/Approach: This paper presents the design and evaluation of Concept Catalyst, an AI-based tool with a scrutable interface, created to support teachers' reflection while using generative AI for curriculum development. It presents the findings from an exploratory study using Wizard-of-Oz testing with middle and high school engineering teachers, resulting in 10 depth interviews lasting 55 minutes on average. Screen/audio recordings and the classroom content teachers produced during the session were also collected. Findings: The paper provides empirical insights about how scrutable interfaces can positively structure teachers' interactions with generative AI models when creating classroom content. Findings suggest that scrutable interfaces can help teachers reflect on their teaching practices while improving efficacy, efficiency, and motivation when using AI. What is original/value of the paper: This paper explores an identified need to support teachers' classroom practices and needs when using generative AI. It extends the consideration of scrutable interfaces in two ways: to support teachers as users (not just students) and to structure interactions with generative AI models.

cs.HC↗

arXiv2026

Drawing Out Legal Risks: Co-Designing with Lawyers to Predict and Manage Legal Uncertainties of Medical AI Tools

While there's optimism around medical AI tools due to their abilities to adapt from user-to-user and across environments, these new abilities complicate how people and organizations are able to predict and manage risk based on existing laws and regulations. Lawyers are trained to identify potential legal outcomes, but they lack technical AI knowledge, making it difficult to translate their expertise to creators and users of AI tools. We contribute insights from our co-design process with U.S. lawyers to identify and translate ways to predict and manage risks of medical AI tools. We present the visualizations we developed through two years of cross-disciplinary efforts and thereby illustrate our findings about how legal risks are determined and our strategies for people and organizations to predict and manage these risks. We offer insights about leveraging lawyers' expertise to understand, predict, and manage legal risks.

cs.HC↗

arXiv2026

Chameleon Clippers: A Tool for Developing Fine Motor Skills in Remote Education Settings

Art education plays a significant role in K-2 learners' physical and cognitive development. However, teachers struggle to translate in-person activities to remote settings and to give necessary feedback to help learners develop fine motor skills. Previous research shows the benefits of tangible technology and real-time system feedback for supporting teachers and students in digital environments, but little research explores their affordances for remote art education. We developed Chameleon Clippers: interactive scissors that give real-time feedback to learners as they cut along a line. In preliminary tests, learners felt engaged and responded to feedback, enjoying their experience. Our low-cost design augments existing classroom artifacts and practices, supporting classroom integration. Testing also revealed directions for future study, including the frequency of feedback and assimilation into a broader, art education platform. Through our study, we demonstrate the potential for tangible technology to create more interactive, engaging, and supportive remote K-2 learning experiences.

cs.HC↗

arXiv2026

Creating Learning Scaffolds for Engineering Design Using Concept Catalyst

K-12 teachers employ Engineering Design Challenges to help students learn about the Engineering Design Process hands-on. They use techniques like hard scaffolding questions to guide the students as they think through the different stages of the engineering design process. While useful, the creation of these questions adds to the teacher's preparation time for their classes. Concept Catalyst uses Large Language Models to assist teachers with the rapid creation of scaffold questions for engineering design challenges. Unlike open-ended chat, Concept Catalyst uses LLMs to summarize and decompose an engineering design challenge into the concepts that students will engage with, allow the teacher to visually manipulate and link related concepts, and to propose scaffolding questions for the teacher to modify or accept.

cs.HC↗

arXiv2026

Evaluating Actionability in Explainable AI

A core assumption of Explainable AI (XAI) is that explanations are useful to users -- that is, users will do something with the explanations. Prior work, however, does not clearly connect the information provided in explanations to user actions to evaluate effectiveness. In this paper, we articulate this connection. We conducted a formative study through 14 interviews with end users in education and medicine. We contribute a catalog of information and associated actions. Our catalog maps 12 categories of information that participants described relying on to take 60 different actions. We show how AI Creators can use the catalog's specificity and breadth to articulate how they expect information in their explanations to lead to user actions and test their assumptions. We use an exemplar XAI system to illustrate this approach. We conclude by discussing how our catalog expands the design space for XAI systems to support actionability.

cs.HC↗

arXiv2025

Explainable Reinforcement Learning Agents Using World Models

Explainable AI (XAI) systems have been proposed to help people understand how AI systems produce outputs and behaviors. Explainable Reinforcement Learning (XRL) has an added complexity due to the temporal nature of sequential decision-making. Further, non-AI experts do not necessarily have the ability to alter an agent or its policy. We introduce a technique for using World Models to generate explanations for Model-Based Deep RL agents. World Models predict how the world will change when actions are performed, allowing for the generation of counterfactual trajectories. However, identifying what a user wanted the agent to do is not enough to understand why the agent did something else. We augment Model-Based RL agents with a Reverse World Model, which predicts what the state of the world should have been for the agent to prefer a given counterfactual action. We show that explanations that show users what the world should have been like significantly increase their understanding of the agent policy. We hypothesize that our explanations can help users learn how to control the agents execution through by manipulating the environment.

cs.AI↗

arXiv2025

Implications of Current Litigation on the Design of AI Systems for Healthcare Delivery

Many calls for explainable AI (XAI) systems in medicine are tied to a desire for AI accountability--accounting for, mitigating, and ultimately preventing harms from AI systems. Because XAI systems provide human-understandable explanations for their output, they are often viewed as a primary path to prevent harms to patients. However, when harm occurs, laws, policies, and regulations also shape AI accountability by impacting how harmed individuals can obtain recourse. Current approaches to XAI explore physicians' medical and relational needs to counter harms to patients, but there is a need to understand how XAI systems should account for the legal considerations of those impacted. We conduct an analysis of 31 legal cases and reported harms to identify patterns around how AI systems impact patient care. Our findings reflect how patients' medical care relies on a complex web of stakeholders--physicians, state health departments, health insurers, care facilities, among others--and many AI systems deployed across their healthcare delivery negatively impact their care. In response, patients have had no option but to seek legal recourse for harms. We shift the frame from physician-centered to patient-centered accountability approaches by describing how lawyers and technologists need to recognize and address where AI harms happen. We present paths for preventing or countering harm (1) by changing liability structures to reflect the role of many stakeholders in shaping how AI systems impact patient care; and (2) by designing XAI systems that can help advocates, such as legal representatives, who provide critical legal expertise and practically support recourse for patients.

cs.HC↗

arXiv2025

Understanding the Impact of Physicians' Legal Considerations on XAI Systems

Physicians are--and feel--ethically, professionally, and legally responsible for patient outcomes, buffering patients from harmful AI determinations from medical AI systems. Many have called for explainable AI (XAI) systems to help physicians incorporate medical AI recommendations into their workflows in a way that reduces the potential of harms to patients. While prior work has demonstrated how physicians' legal concerns impact their medical decision making, little work has explored how XAI systems should be designed in light of these concerns. In this study, we conducted interviews with 10 physicians to understand where and how they anticipate errors that may occur with a medical AI system and how these anticipated errors connect to their legal concerns. In our study, physicians anticipated risks associated with using an AI system for patient care, but voiced unknowns around how their legal risk mitigation strategies may change given a new technical system. Based on these findings, we describe the implications for designing XAI systems that can address physicians' legal concerns. Specifically, we identify the need to provide AI recommendations alongside contextual information that guides their risk mitigation strategies, including how non-legally related aspects of their systems, such as medical documentation and auditing requests, might be incorporated into a legal case.

cs.HC↗

arXiv2025

Experiential Explanations for Reinforcement Learning

Reinforcement learning (RL) systems can be complex and non-interpretable, making it challenging for non-AI experts to understand or intervene in their decisions. This is due in part to the sequential nature of RL in which actions are chosen because of their likelihood of obtaining future rewards. However, RL agents discard the qualitative features of their training, making it difficult to recover user-understandable information for "why" an action is chosen. We propose a technique Experiential Explanations to generate counterfactual explanations by training influence predictors along with the RL policy. Influence predictors are models that learn how different sources of reward affect the agent in different states, thus restoring information about how the policy reflects the environment. Two human evaluation studies revealed that participants presented with Experiential Explanations were better able to correctly guess what an agent would do than those presented with other standard types of explanation. Participants also found that Experiential Explanations are more understandable, satisfying, complete, useful, and accurate. Qualitative analysis provides information on the factors of Experiential Explanations that are most useful and the desired characteristics that participants seek from the explanations.

cs.AI↗

arXiv2025

Legally-Informed Explainable AI

Explanations for artificial intelligence (AI) systems are intended to support the people who are impacted by AI systems in high-stakes decision-making environments, such as doctors, patients, teachers, students, housing applicants, and many others. To protect people and support the responsible development of AI, explanations need to be actionable--helping people take pragmatic action in response to an AI system--and contestable--enabling people to push back against an AI system and its determinations. For many high-stakes domains, such as healthcare, education, and finance, the sociotechnical environment includes significant legal implications that impact how people use AI explanations. For example, physicians who use AI decision support systems may need information on how accepting or rejecting an AI determination will protect them from lawsuits or help them advocate for their patients. In this paper, we make the case for Legally-Informed Explainable AI, responding to the need to integrate and design for legal considerations when creating AI explanations. We describe three stakeholder groups with different informational and actionability needs, and provide practical recommendations to tackle design challenges around the design of explainable AI systems that incorporate legal considerations.

cs.HC↗

arXiv2024

Recognizing Lawyers as AI Creators and Intermediaries in Contestability

Laws play a key role in the complex socio-technical system impacting contestability: they create the regulations shaping the way AI systems are designed, evaluated, and used. Despite their role in the AI value chain, lawyers' impact on contestability has gone largely unrecognized in the design of AI systems. In this paper, we highlight two main roles lawyers play that impact contestability: (1) as AI Creators because the regulations they create shape the design and evaluation of AI systems before they are deployed; and (2) as Intermediaries because they interpret regulations when harm occurs, navigating the gap between stakeholders, instutions, and harmful outcomes. We use these two roles to illuminate new opportunities and challenges for including lawyers in the design of AI systems, contributing a significant first step in practical recommendations to amplify the power to contest systems through cross-disciplinary design.

cs.HC↗

arXiv2023

Why Don't You Do Something About It? Outlining Connections between AI Explanations and User Actions

A core assumption of explainable AI systems is that explanations change what users know, thereby enabling them to act within their complex socio-technical environments. Despite the centrality of action, explanations are often organized and evaluated based on technical aspects. Prior work varies widely in the connections it traces between information provided in explanations and resulting user actions. An important first step in centering action in evaluations is understanding what the XAI community collectively recognizes as the range of information that explanations can present and what actions are associated with them. In this paper, we present our framework, which maps prior work on information presented in explanations and user action, and we discuss the gaps we uncovered about the information presented to users.

cs.HC↗