Inside Out 2: Make Room for New Emotions & LLM: A Reproducibility Study of the Emotional Side of Search in the Classroom

In an existing study, the InsideOut Framework is used to produce and explore the emotional profiles of search engines (SE) in response to queries formulated by children aged 9 to 11 in the classroom context, revealing the emotional diversity of SE responses. Since then, there have been significant technological advances in emotion detection and information access. In this work, we conduct a comprehensive reproducibility study where we probe today's emotional profile of SE using both a lexicon-based and a language-model based approach tailored to the Italian language, thus addressing an acknowledged limitation of the original study. Additionally, considering the prevalence of agents based on Large Language Models (LLM) as information access systems among children, we extend the analysis to capture the emotional undertones of LLM responses and juxtapose them to those of SE. Our findings emphasize the importance of leveraging the appropriate emotion detection technique to produce and explore emotional profiles and lead us to reflect on the interplay of emotions on children's search-as-learning experience.

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Publication Details

Published
2025-07-13
DOI
https://doi.org/10.1145/3726302.3730315
Citations
2
Primary Topic
Online and Blended Learning
Type
article
Field-Weighted Citation Impact
17.85
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article

Inside Out 2: Make Room for New Emotions & LLM: A Reproducibility Study of the Emotional Side of Search in the Classroom

Monica Landoni, Maria Soledad Pera, Diletta Micol Tobia, Hrishita Chakrabarti
2 citations
Online and Blended Learning
17.85
article

Inside Out 2: Make Room for New Emotions & LLM: A Reproducibility Study of the Emotional Side of Search in the Classroom

Monica Landoni, Maria Soledad Pera, Diletta Micol Tobia, Hrishita Chakrabarti
article en
2 citations

Abstract

In an existing study, the InsideOut Framework is used to produce and explore the emotional profiles of search engines (SE) in response to queries formulated by children aged 9 to 11 in the classroom context, revealing the emotional diversity of SE responses. Since then, there have been significant technological advances in emotion detection and information access. In this work, we conduct a comprehensive reproducibility study where we probe today's emotional profile of SE using both a lexicon-based and a language-model based approach tailored to the Italian language, thus addressing an acknowledged limitation of the original study. Additionally, considering the prevalence of agents based on Large Language Models (LLM) as information access systems among children, we extend the analysis to capture the emotional undertones of LLM responses and juxtapose them to those of SE. Our findings emphasize the importance of leveraging the appropriate emotion detection technique to produce and explore emotional profiles and lead us to reflect on the interplay of emotions on children's search-as-learning experience.

Università della Svizzera italiana (CH), Delft University of Technology (NL)
Gender equality
Openalex Percentile: Top 1%
Online and Blended Learning
17.85
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