Trusting AI to Increase Productivity: Perspectives Across the Global North and South

Jul 16, 2026

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Generative AI tools are widely used in academia and software development, yet productivity benefits depend not only on model capabilities but also on users’ trust and contextual factors. An exploratory study examines how trust in GenAI relates to perceived productivity with a motivation grounded in Global South contexts. The work combines a systematic literature review, grey literature analysis, and a survey (36 valid responses). Preliminary results indicate Global South-born respondents trust AI more but report less clear productivity gains, while non-Global-South respondents report stronger gains despite lower trust. The findings emphasize that access, task type, and verification needs shape outcomes beyond trust alone.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

Introduction

Motivation and empirical gap

Background

AI in knowledge-intensive workflows

Methods and Study Design

Systematic literature review and grey literature analysis

Survey study across Global North and South

Preliminary Findings

Trust versus perceived productivity patterns

arXiv :2607 . 10488v 1 [ cs . SE] 11 Jul 2026 Trusting AI to increase productivity? Perspectives Across the Global North and South Adam Bokun \# University of Bayreuth, Germany Shalini Chakraborty \# University of Bayreuth, Germany ~~ Abstract ~~ Generative AI (GenAI) tools are widely used in academia and software development, where productivity gains may depend not only on technical capabilities but also on users’ trust and contextual factors. This paper presents emerging results from an exploratory study investigating the relationship between trust in GenAI and perceived productivity, motivated by Global South contexts. We conducted a systematic literature review, complemented by a grey literature analysis and a survey study. The literature review identified no peer-reviewed evidence at the intersection of GenAI trust, productivity, and Global South settings, while the grey literature revealed only limited insights. Atthe time of writing, the survey has received 36 valid responses from participants across both the Global North and Global South, including individuals with cross-regional experiences. Preliminary results suggest that respondents born and working in the Global South tended to trust AI more, but did not usually report clear productivity gains from using it. In contrast, respondents born and working outside the Global South reported stronger productivity gains and greater time savings, even though they showed less trust in generative AI. These findings suggest that trusting AI is not enough on its own; productivity also depends on access, the type of task, and how much users need to check the output. 2012 ACM Subject Classification Software and its engineering → Software design engineering Keywords and phrases Trust, GenAI, Productivity, Global South Digital Object Identifier 10.4230/LIPIcs... 1 Introduction Generative artificial intelligence (GenAI) tools are increasingly embedded in academic and software development workflows, supporting activities such as writing, coding, data analysis, and research assistance [3] . As a result, GenAI is often presented as a productivity-enhancing technology [14, 20] . However, AI-driven productivity gains are not determined solely by the technical capabilities of large language models (LLMs) . Rather, they are strongly influenced by user trust in these systems [23] . Whether individuals adopt, rely on, and meaningfully integrate AI-assisted tools into their work depends on the extent to which they perceive these systems as reliable, useful, and beneficial [4] . Understanding the relationship between trust and productivity is therefore essential for assessing the impact of GenAI across different contexts. Existing research has examined trust in AI [2, 22], AI adoption [25], and productivity-related outcomes [17, 19 , 24] in human-AI interaction. However, much of the available evidence has been produced in contexts where access to digital infrastructure, paid AI tools, institutional support, and training is relatively stable. These assumptions may not hold equally in global contexts. In particular, there is limited systematic evidence on how academics and developers working in the Global South perceive and trust AI as a productivity-enhancing tool. Structural constraints such as unequal access to computational resources, data, training, and institutional support can shape both trust in AI and its realized productivity benefits. © Adam Bokun and Shalini Chakraborty; licensed under Creative Commons License CC-BY 4 .0 Leibniz International Proceedings in Informatics Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germany XX:2 Trusting AI to increase Productivity? This creates an important empirical gap. While GenAI is increasingly discussed asa productivity-enhancing technology, less is known about how trust in GenAI relates to perceived productivity among users working under different infrastructural, economic, and instit

FAQs

How does the study define the relationship between trust in GenAI and productivity outcomes?

It investigates how users’ perceived productivity varies with their trust in generative AI tools, emphasizing that productivity gains are shaped by more than technical model performance.

What methods were used to explore trust and productivity across Global North and South contexts?

The study uses a systematic literature review, a grey literature analysis, and a survey with participants from both Global North and Global South, including cross-regional experiences.

What preliminary patterns were observed regarding trust and perceived productivity?

Respondents born and working in the Global South tended to trust AI more but usually did not report clear productivity gains, while respondents outside the Global South reported stronger productivity gains and time savings with less trust.

Jul 16, 2026

Upload by Levi