arXiv subjects

Jichen Zhu

Publications and source records attributed to Jichen Zhu.

At least 19 recordsLinked to original sources

arXiv2026

"It became a self-fulfilling prophecy": How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Apps

In menstrual cycle tracking apps (MCTAs), AI-based predictions and insights have become increasingly popular. These features enable users to receive personalized information about their bodies and mental states. However, there is currently little research on how these predictive AI features and explanations affect users' lived experiences. This paper examines human-AI entanglement in MCTAs through 14 semi-structured user interviews and a group autoethnography. These methods uncover the processes leading to this phenomenon. Our results reveal that: (1) users understand their lived experiences in light of AI predictions, although these predictions can be faulty due to imperfect logging practices, (2) the user interface features and AI explanations do not support awareness or critical engagement with this entanglement and meaning-making, and (3) non-normative MCTA users report a sense of isolation in this entangled interaction. Based on our findings, we propose design implications for predictive AI features and explanations.

cs.HC↗

arXiv2026

Unlocking Open-Player-Modeling-enhanced Game-Based Learning: The Open Player Socially Analytical Intelligence Architecture

Game-Based Learning (GBL) is a learner-engaging pedagogical methodology, yet adapting games to heterogeneous learners requires transparent, real-time Open Player Models (OPMs). We contribute to the community Open Player Socially Analytical Intelligence (OPSAI), an architecture implementing OPM beyond conceptual frameworks and validated in a GBL application. It decouples gameplay telemetry and analysis from the game engine and automatically derives pedagogically actionable insights, supporting the transparency of computational player models while making them accessible to players. OPSAI comprises three logical layers: a Frontend that both provides the GBL experience and collects information needed for analytics; a stateless Backend that hosts transparent analytics services producing reflective prompts, recommendations, and visualization guides; and a two-tier Log Storage that balances heavy raw gameplay data with lightweight reference indices for low-latency queries. By feeding analytics outputs back into the game interface, OPSAI closes the feedback loop between play and learning, empowering teachers, researchers, and learners alike. We further showcase OPSAI with a full deployment on the Parallel GBL environment, featuring live play traces, peer comparisons, and personalized suggestions, demonstrating a reusable blueprint for future educational games.

cs.HC↗

arXiv2026

Results-Actionability Gap: Understanding How Practitioners Evaluate LLM Products in the Wild

How do product teams evaluate LLM-powered products? As organizations integrate large language models (LLMs) into digital products, their unpredictable nature makes traditional evaluation approaches inadequate, yet little is known about how practitioners navigate this challenge. Through interviews with nineteen practitioners across diverse sectors, we identify ten evaluation practices spanning informal 'vibe checks' to organizational meta-work. Beyond confirming four documented challenges, we introduce a novel fifth we call the results-actionability gap, in which practitioners gather evaluation data but cannot translate findings into concrete improvements. Drawing on patterns from successful teams, we contribute strategies to bridge this gap, supporting practitioners' formalization journey from ad-hoc interpretive practices (e.g., vibe checks) toward systematic evaluation. Our analysis suggests these interpretive practices are necessary adaptations to LLM characteristics rather than methodological failures. For HCI researchers, this presents a research opportunity to support practitioners in systematizing emerging practices rather than developing new evaluation frameworks.

cs.SE↗

arXiv2025

The Centers and Margins of Modeling Humans in Well-being Technologies: A Decentering Approach

This paper critically examines the machine learning (ML) modeling of humans in three case studies of well-being technologies. Through a critical technical approach, it examines how these apps were experienced in daily life (technology in use) to surface breakdowns and to identify the assumptions about the "human" body entrenched in the ML models (technology design). To address these issues, this paper applies agential realism to decenter foundational assumptions, such as body regularity and health/illness binaries, and speculates more inclusive design and ML modeling paths that acknowledge irregularity, human-system entanglements, and uncertain transitions. This work is among the first to explore the implications of decentering theories in computational modeling of human bodies and well-being, offering insights for more inclusive technologies and speculations toward posthuman-centered ML modeling.

cs.HC↗

arXiv2024

How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey

Despite its technological breakthroughs, eXplainable Artificial Intelligence (XAI) research has limited success in producing the {\em effective explanations} needed by users. In order to improve XAI systems' usability, practical interpretability, and efficacy for real users, the emerging area of {\em Explainable Interfaces} (EIs) focuses on the user interface and user experience design aspects of XAI. This paper presents a systematic survey of 53 publications to identify current trends in human-XAI interaction and promising directions for EI design and development. This is among the first systematic survey of EI research.

cs.HC↗

arXiv2024

Machine Learning Processes as Sources of Ambiguity: Insights from AI Art

Ongoing efforts to turn Machine Learning (ML) into a design material have encountered limited success. This paper examines the burgeoning area of AI art to understand how artists incorporate ML in their creative work. Drawing upon related HCI theories, we investigate how artists create ambiguity by analyzing nine AI artworks that use computer vision and image synthesis. Our analysis shows that, in addition to the established types of ambiguity, artists worked closely with the ML process (dataset curation, model training, and application) and developed various techniques to evoke the ambiguity of processes. Our finding indicates that the current conceptualization of ML as a design material needs to reframe the ML process as design elements, instead of technical details. Finally, this paper offers reflections on commonly held assumptions in HCI about ML uncertainty, dependability, and explainability, and advocates to supplement the artifact-centered design perspective of ML with a process-centered one.

cs.HC↗

arXiv2023

Towards Co-Creative Generative Adversarial Networks for Fashion Designers

Originating from the premise that Generative Adversarial Networks (GANs) enrich creative processes rather than diluting them, we describe an ongoing PhD project that proposes to study GANs in a co-creative context. By asking How can GANs be applied in co-creation, and in doing so, how can they contribute to fashion design processes? the project sets out to investigate co-creative GAN applications and further develop them for the specific application area of fashion design. We do so by drawing on the field of mixed-initiative co-creation. Combined with the technical insight into GANs' functioning, we aim to understand how their algorithmic properties translate into interactive interfaces for co-creation and propose new interactions.

cs.HC↗

arXiv2023

Hidden Layer Interaction: A Co-Creative Design Fiction for Generative Models

This paper presents a speculation on a fictive co-creation scenario that extends classical interaction patterns with generative models. While existing interfaces are restricted to the input and output layers, we suggest hidden layer interaction to extend the horizonal relation at play when co-creating with a generative model's design space. We speculate on applying feature visualization to manipulate neurons corresponding to features ranging from edges over textures to objects. By integrating visual representations of a neural network's hidden layers into co-creation, we aim to provide humans with a new means of interaction, contributing to a phenomenological account of the model's inner workings during generation.

cs.HC↗

arXiv2023

Improving Fairness in Adaptive Social Exergames via Shapley Bandits

Algorithmic fairness is an essential requirement as AI becomes integrated in society. In the case of social applications where AI distributes resources, algorithms often must make decisions that will benefit a subset of users, sometimes repeatedly or exclusively, while attempting to maximize specific outcomes. How should we design such systems to serve users more fairly? This paper explores this question in the case where a group of users works toward a shared goal in a social exergame called Step Heroes. We identify adverse outcomes in traditional multi-armed bandits (MABs) and formalize the Greedy Bandit Problem. We then propose a solution based on a new type of fairness-aware multi-armed bandit, Shapley Bandits. It uses the Shapley Value for increasing overall player participation and intervention adherence rather than the maximization of total group output, which is traditionally achieved by favoring only high-performing participants. We evaluate our approach via a user study (n=46). Our results indicate that our Shapley Bandits effectively mediates the Greedy Bandit Problem and achieves better user retention and motivation across the participants.

cs.AI↗

arXiv2022

Towards Better User Requirements: How to Involve Human Participants in XAI Research

Human-Center eXplainable AI (HCXAI) literature identifies the need to address user needs. This paper examines how existing XAI research involves human users in designing and developing XAI systems and identifies limitations in current practices, especially regarding how researchers identify user requirements. Finally, we propose several suggestions on how to derive better user requirements.

cs.HC↗

arXiv2022

Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space

This paper presents a novel approach for guiding a Generative Adversarial Network trained on the FashionGen dataset to generate designs corresponding to target fashion styles. Finding the latent vectors in the generator's latent space that correspond to a style is approached as an evolutionary search problem. A Gaussian mixture model is applied to identify fashion styles based on the higher-layer representations of outfits in a clothing-specific attribute prediction model. Over generations, a genetic algorithm optimizes a population of designs to increase their probability of belonging to one of the Gaussian mixture components or styles. Showing that the developed system can generate images of maximum fitness visually resembling certain styles, our approach provides a promising direction to guide the search for style-coherent designs.

cs.NE↗

arXiv2021

Open Player Modeling: Empowering Players through Data Transparency

Data is becoming an important central point for making design decisions for most software. Game development is not an exception. As data-driven methods and systems start to populate these environments, a good question is: can we make models developed from this data transparent to users? In this paper, we synthesize existing work from the Intelligent User Interface and Learning Science research communities, where they started to investigate the potential of making such data and models available to users. We then present a new area exploring this question, which we call Open Player Modeling, as an emerging research area. We define the design space of Open Player Models and present exciting open problems that the games research community can explore. We conclude the paper with a case study and discuss the potential value of this approach.

cs.HC↗

arXiv2021

Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning

Applying neural network (NN) methods in games can lead to various new and exciting game dynamics not previously possible. However, they also lead to new challenges such as the lack of large, clean datasets, varying player skill levels, and changing gameplay strategies. In this paper, we focus on the adversarial player strategy aspect in the game iNNk, in which players try to communicate secret code words through drawings with the goal of not being deciphered by a NN. Some strategies exploit weaknesses in the NN that consistently trick it into making incorrect classifications, leading to unbalanced gameplay. We present a method that combines transfer learning and ensemble methods to obtain a data-efficient adaptation to these strategies. This combination significantly outperforms the baseline NN across all adversarial player strategies despite only being trained on a limited set of adversarial examples. We expect the methods developed in this paper to be useful for the rapidly growing field of NN-based games, which will require new approaches to deal with unforeseen player creativity.

cs.LG↗

arXiv2021

Understanding Mental Models of AI through Player-AI Interaction

Designing human-centered AI-driven applications require deep understandings of how people develop mental models of AI. Currently, we have little knowledge of this process and limited tools to study it. This paper presents the position that AI-based games, particularly the player-AI interaction component, offer an ideal domain to study the process in which mental models evolve. We present a case study to illustrate the benefits of our approach for explainable AI.

cs.HC↗

arXiv2021

The Personalization Paradox: the Conflict between Accurate User Models and Personalized Adaptive Systems

Personalized adaptation technology has been adopted in a wide range of digital applications such as health, training and education, e-commerce and entertainment. Personalization systems typically build a user model, aiming to characterize the user at hand, and then use this model to personalize the interaction. Personalization and user modeling, however, are often intrinsically at odds with each other (a fact some times referred to as the personalization paradox). In this paper, we take a closer look at this personalization paradox, and identify two ways in which it might manifest: feedback loops and moving targets. To illustrate these issues, we report results in the domain of personalized exergames (videogames for physical exercise), and describe our early steps to address some of the issues arisen by the personalization paradox.

cs.HC↗

arXiv2021

Player-Centered AI for Automatic Game Personalization: Open Problems

Computer games represent an ideal research domain for the next generation of personalized digital applications. This paper presents a player-centered framework of AI for game personalization, complementary to the commonly used system-centered approaches. Built on the Structure of Actions theory, the paper maps out the current landscape of game personalization research and identifies eight open problems that need further investigation. These problems require deep collaboration between technological advancement and player experience design.

cs.AI↗

arXiv2021

Regression Oracles and Exploration Strategies for Short-Horizon Multi-Armed Bandits

This paper explores multi-armed bandit (MAB) strategies in very short horizon scenarios, i.e., when the bandit strategy is only allowed very few interactions with the environment. This is an understudied setting in the MAB literature with many applications in the context of games, such as player modeling. Specifically, we pursue three different ideas. First, we explore the use of regression oracles, which replace the simple average used in strategies such as epsilon-greedy with linear regression models. Second, we examine different exploration patterns such as forced exploration phases. Finally, we introduce a new variant of the UCB1 strategy called UCBT that has interesting properties and no tunable parameters. We present experimental results in a domain motivated by exergames, where the goal is to maximize a player's daily steps. Our results show that the combination of epsilon-greedy or epsilon-decreasing with regression oracles outperforms all other tested strategies in the short horizon setting.

cs.LG↗

arXiv2021

Player Modeling via Multi-Armed Bandits

This paper focuses on building personalized player models solely from player behavior in the context of adaptive games. We present two main contributions: The first is a novel approach to player modeling based on multi-armed bandits (MABs). This approach addresses, at the same time and in a principled way, both the problem of collecting data to model the characteristics of interest for the current player and the problem of adapting the interactive experience based on this model. Second, we present an approach to evaluating and fine-tuning these algorithms prior to generating data in a user study. This is an important problem, because conducting user studies is an expensive and labor-intensive process; therefore, an ability to evaluate the algorithms beforehand can save a significant amount of resources. We evaluate our approach in the context of modeling players' social comparison orientation (SCO) and present empirical results from both simulations and real players.

cs.AI↗