arXiv subjects

Christopher Sanchez

Publications and source records attributed to Christopher Sanchez.

4 recordsLinked to original sources

arXiv2026

Thinking Less, Trusting More: GenAI's Impacts on Students' Cognitive Habits

Objectives: When students use generative AI in coursework, what are its persistent effects on their intellectual development? We investigate (RQ1-How) how students' trust in and routine use of genAI affect their cognitive engagement habits in STEM coursework, and (RQ2-Who) which students are particularly vulnerable to cognitive disengagement. Method: Drawing on dual-process, cognitive offloading, and automation bias theories, we developed a statistical model explaining how and to what extent students' trust-driven routine genAI use affected their cognitive engagement -- specifically, reflection, the need for understanding, and critical thinking in coursework, and how these effects differed across students' cognitive styles. We empirically evaluated this model using Partial Least Squares Structural Equation Modeling on survey data from 299 STEM students across five North American universities. Results: Students who trusted and routinely used genAI reported significantly lower cognitive engagement. Unexpectedly, students with higher technophilic motivations, risk tolerance, and computer self-efficacy -- traits often celebrated in STEM -- were more prone to these effects. Interestingly, students' prior experience with genAI or academia did not protect them from cognitively disengaging. Implications: Our findings suggest a potential cognitive debt cycle where routine genAI use weakens students' intellectual habits, potentially driving and escalating over-reliance. This poses challenges for curricula and genAI system design, requiring interventions that actively support cognitive engagement.

cs.HC↗

arXiv2025

What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI

Generative AI (genAI) tools promise productivity gains, yet miscalibrated trust and usage friction still hinder adoption. Moreover, genAI can be exclusionary, failing to adequately support diverse users. One such aspect of diversity is cognitive diversity, which leads to diverging interaction styles (e.g., a risk-averse developer may gate genAI outputs behind tests/review; a risk-tolerant one may prototype directly/fix issues post-hoc). When an individual's cognitive styles are unsupported, it creates additional usability barriers. Thus, to design tools that developers trust and use, we must first understand which factors shape their trust and intentions to use genAI at work? We developed a theoretical model of developers' trust and adoption of genAI through a large-scale survey (N = 238) conducted at GitHub and Microsoft. Using Partial Least Squares-Structural Equation Modeling (PLS-SEM), we found aspects related to genAI's system/output quality (e.g., presentation, safety/security, performance), functional value (e.g., educational/practical benefits), and goal maintenance (ability to sustain alignment with task goals) significantly influence trust, which, alongside developers' cognitive styles (i.e., risk tolerance, technophilic motivations, computer self-efficacy), affect adoption. An Importance-Performance Matrix Analysis (IPMA) identified high-importance factors where genAI underperforms, revealing targets for design improvement. We bolster these findings by qualitatively analyzing developers' reported challenges and risks of genAI use to uncover why these gaps persist in development contexts. We offer practical guidance for designing genAI tools that support effective, trustworthy, and inclusive developer-AI interactions.

cs.HC↗

arXiv2024

What Guides Our Choices? Modeling Developers' Trust and Behavioral Intentions Towards GenAI

Generative AI (genAI) tools, such as ChatGPT or Copilot, are advertised to improve developer productivity and are being integrated into software development. However, misaligned trust, skepticism, and usability concerns can impede the adoption of such tools. Research also indicates that AI can be exclusionary, failing to support diverse users adequately. One such aspect of diversity is cognitive diversity -- variations in users' cognitive styles -- that leads to divergence in perspectives and interaction styles. When an individual's cognitive style is unsupported, it creates barriers to technology adoption. Therefore, to understand how to effectively integrate genAI tools into software development, it is first important to model what factors affect developers' trust and intentions to adopt genAI tools in practice? We developed a theoretically grounded statistical model to (1) identify factors that influence developers' trust in genAI tools and (2) examine the relationship between developers' trust, cognitive styles, and their intentions to use these tools in their work. We surveyed software developers (N=238) at two major global tech organizations: GitHub Inc. and Microsoft; and employed Partial Least Squares-Structural Equation Modeling (PLS-SEM) to evaluate our model. Our findings reveal that genAI's system/output quality, functional value, and goal maintenance significantly influence developers' trust in these tools. Furthermore, developers' trust and cognitive styles influence their intentions to use these tools in their work. We offer practical suggestions for designing genAI tools for effective use and inclusive user experience.

cs.HC↗

arXiv2020

Developing and Validating an Interactive Training Tool for Inferring 2D Cross-Sections of Complex 3D Structures

Understanding 2D cross-sections of 3D structures is a crucial skill in many disciplines, from geology to medical imaging. Cross-section inference in the context of 3D structures requires a complex set of spatial/visualization skills including mental rotation, spatial structure understanding, and viewpoint projection. Prior studies show that experts differ from novices in these, and other, skill dimensions. Building on a previously developed model that hierarchically characterizes the specific spatial sub-skills needed for this task, we have developed the first domain-agnostic, computer-based training tool for cross-section understanding of complex 3D structures. We demonstrate, in an evaluation with 60 participants, that this interactive tool is effective for increasing cross-section inference skills for a variety of structures, from simple primitive ones to more complex biological structures.

cs.HC↗