The User-In-Context Framework Understanding Variation in How Users Respond to AI Chatbots

Jul 16, 2026

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AI chatbots (AICs) prompt highly variable responses across users and even within the same user over time. This paper adapts Bronfenbrenner’s bioecological systems theory into a user-in-context heuristic that centers the human user in repeated, reciprocal, and coadaptive interactions with a personalized, stateful AIC. Surrounding systems capture contextual factors that shape interpretation, response, and mutual change. Because memory-bearing AICs learn from prior exchanges, users respond to both the system and an AIC version co-constructed through earlier interactions, guiding researchers, practitioners, and designers.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

Abstract

Introduction

Existing Explanations and Gaps

Proposed Heuristic Framework

Extending Ecology to Digital Contexts

Current Accounts of Variation in User Response to AI

The User-In-Context Framework: Understanding Variation in How Users Respond to AI Chatbots Richard A. Fabes 1, Carol Lynn Martin 1, and Philip Costanzo2 1Arizona State University 2Duke University Abstract People respond to artificial intelligence chatbots (AICs) in highly variable ways. In this paper, we adapt Bronfenbrenner's theory into a heuristic framework for understanding this variation. The framework places the human user at the center while also placing the AI there and reconceptualizing the proximal processes as the repeated, reciprocal, and coadaptive interactions between the user and a personalized AIC. The surrounding systems identify the contextual factors that shape how the user experiences, interprets, responds to, and is changed by these interactions. Because stateful AICs learn from accumulated exchanges with their users and have memory, users are responding not only to an AIC but also to a version of the AIC that their own prior interactions have helped create. This extension preserves Bronfenbrenner's emphasis on proximal processes while accounting for the unique dynamics of personalized AICs. The resulting framework provides a structured map of where and how variation in human–AIC relationships arises, as well as having implications for researchers, practitioners, and AIC designers. Introduction People respond to an artificial intelligence chatbot (AIC) in highly variable ways (we use AI chatbot throughout this paper to refer broadly to text-based conversational AI systems, including AI companions, assistants, and related dialogue-based agents) . Some users respond with trust, warmth, and growing attachment, others with suspicion, fear, and unease. Some employ AICs as a tool. Others turn to them for companionship. The factors that contribute to these differences are not well understood. Human-AIC relationships are now a fact of life and will likely only spread as AI increases its roles and influence. Despite this increased pervasiveness, until very recently, how people respond to AICs has not been the subject of systematic research and theorizing, despite the need to understand these systems because increasingly they shape how people think, feel, learn, and connect, making them part of the social environments that influence human development and well-being. In this paper, we begin to fill this gap by presenting the user-in-context framework as a heuristic for theorizing and studying the user’s response to AICs. AICs are now not only useful for users’ work and information seeking efforts, but they now can provide sustained, emotionally textured relationships with their users. Many people confide in AICs, rely on them for support, form emotional and romantic attachments to them, and experience grief when those companions change or disappear (Laestadius et al., 2024; Mohanty et al., 2025; Zhang et al., 2025) . In contrast, other people use them only as a tool and some question how people can have healthy and meaningful relationships with a technology that does not feel and lacks awareness (Fröding & Peterson, 2012) . As such, AICs evoke significantly different responses in different people, and even in the same person at different times. Existing explanations for this variation in how users respond to AICs are fragmented. The human-computer interaction literature has catalogued individual difference moderators, such as personality, age, prior experience, anthropomorphic tendency (Epley et al., 2007; Pal et al., 2023) . The trust-in-automation literature has identified dimensions of system behavior that shape reliance and appropriate use (Lee & See, 2004; Schaefer et al., 2016) . The emerging literature on human-AIC relationships has documented the formation of attachments and the psychological consequences of longterm use (Brandtzaeg et al., 2022; Skjuve et al., 2022; Xie & Pentina, 2022) . These conceptualizations offer separate partial explanations, with each capturing a slice of the varian

FAQs

What does the user-in-context framework explain about users’ reactions to AI chatbots?

It explains why users respond to AICs in highly variable ways by treating responses as the result of repeated, reciprocal, coadaptive interactions between the user and a personalized AIC within layered contextual systems.

How does personalization and memory in stateful AICs change what users are responding to?

Users respond not only to the chatbot itself, but also to a version of the chatbot that their prior interactions helped shape through accumulated exchanges and learning.

Why does the paper adapt Bronfenbrenner’s bioecological systems theory for studying human–AIC relationships?

It preserves the emphasis on proximal processes while adding a structured way to account for both contextual influences on user experience and the dynamic ways AIC behavior changes within those interactions.

Jul 16, 2026

Upload by Maeve