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Chatting with an LLM-based AI elicits affective and cognitive processes in education for sustainable development
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  • Published: 21 February 2026

Chatting with an LLM-based AI elicits affective and cognitive processes in education for sustainable development

  • Pia Spangenberger1,
  • Georg Felix Reuth1,
  • Jule M. Krüger1,
  • Lena Baumann1 &
  • …
  • Steve Nebel1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Computer science
  • Environmental social sciences
  • Psychology and behaviour

Abstract

Personalized interactions have been discussed as beneficial for learning for decades. Now, with the rise of generative artificial intelligence (GenAI), personalized artificial human-like conversations may impact the quality of learning. Manipulating system prompts to design personalities has the potential to enhance the quality of conversation with Large Language Model (LLM)-based AI. However, it is still uncertain exactly to what extent the emotional tone of a generative AI chatbot is relevant for learning. Hence, the current study evaluates the impact of a chat-based conversation with an LLM-based AI on relevant affective (empathy, compassion, distress) and cognitive (perspective-taking, reflection, knowledge) processes in education for sustainable development. Here, the focus is on both the general impact and the particular impact of two different system prompts that assign the AI’s specific personality traits (empathic vs. compassionate). Comparing these two groups and one control group reading a text (N = 122) indicates that chatting with an empathic AI can elicit stronger emotions (e.g., empathy, compassion, distress) compared to chatting with a compassionate AI, and compared to the control. Although all groups gained knowledge, we found no group differences. Further research is necessary to ensure reliable and contextually appropriate conversations in the context of education.

Data availability

As additional source data, we have uploaded a zip-file containing the code used, a read-me file with requirements and an installation guide, and the icons used for the chat interface. The code consists of a simple python script and is includes comments to ensure easy accessibility. We made this code available via a public osf-link (https://osf.io/u3vm4/overview) and uploaded it as supplementary material 2.The source data used in this research is currently unavailable for public sharing due to strict data safety and confidentiality protocols mandated by our university. These restrictions are in place to ensure compliance with ethical standards, privacy regulations, and institutional policies.Access to the data may be granted under specific circumstances, subject to appropriate data use agreements and ethical approvals. For more information about the data, please contact the corresponding author.

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Authors and Affiliations

  1. Department of Educational Sciences, University of Potsdam, Potsdam, Germany

    Pia Spangenberger, Georg Felix Reuth, Jule M. Krüger, Lena Baumann & Steve Nebel

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Contributions

Pia Spangenberger was responsible for concept and design of the work, statistical analysis, the interpretation of data as well as the first draft. Georg F. Reuth programmed the LLM-based AIs , and contributed to the theoretical background, and revised the first draft. Jule M. Krüger contributed substantively to the statistical analysis, and revised the first drafts. Lena Baumann contributed to the theoretical background, and provided feedback to the first draft. Pia Spangenberger and Georg F. Reuth did the acquisition of the original data. Steve Nebel revised the design of the study, and contributed to the first draft.

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Correspondence to Pia Spangenberger.

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The authors declare no competing interests.

Ethics 

The experiment was part of a teaching unit on Artificial Intelligence in Education at a German university, which did not extend beyond standard educational practices. We conducted the experiment in line with the Declaration of Helsinki (2013). All experimental protocols were carried out in accordance with the guidelines of the German Educational Research Association Society provided as a self-assessment via the German Data Forum (2024), which is endorsed by the German Research Foundation (DFG). All experimental protocols were reviewed and approved internally by the participating lecturers and researchers.

All participants were informed about the purpose of the experiment, and we obtained informed consent from all participants. The informed consent was obtained electronically. The dataset was fully anonymized, containing only non-identifying demographic variables (age and gender). Participation in the study and allowing the data to be used for the analysis was entirely voluntary, and there were no disadvantages for those who chose not to participate or who decided to drop out at any point during the study.

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Spangenberger, P., Reuth, G.F., Krüger, J.M. et al. Chatting with an LLM-based AI elicits affective and cognitive processes in education for sustainable development. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39317-6

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  • Received: 20 December 2024

  • Accepted: 04 February 2026

  • Published: 21 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-39317-6

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