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From Avoidance to Approach: LLM Agent Effects on Appraisals and Approach Motivation

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2026, 31(2), pp.17~29
  • DOI : 10.9708/jksci.2026.31.02.017
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : December 29, 2025
  • Accepted : January 21, 2026
  • Published : February 27, 2026

Hae-Ryung Lee 1 Ju-Hye Ha 1 Chang-Hoon Oh 1

1연세대학교

Accredited

ABSTRACT

This study examined how an interactive conversational AI agent’s emotion-regulation intervention shapes pre-judgment appraisals and approach motivation in high-anxiety decisions. Grounded in cognitive appraisal dimensions (certainty, perceived control, anticipated effort) and the Appraisal Tendency Framework (ATF), we presented a webtoon-based scenario and ran a 2×2 experiment (strategy: situation selection vs. attentional deployment; style: rational vs. empathic). Participants rated outcomes on 7-point scales. Two-way ANOVA showed a significant strategy × style interaction for approach motivation: under situation selection, rational expression increased approach motivation. Certainty showed significant main effects of strategy and style. Perceived control was not significant but showed a directional interaction pattern, and anticipated effort showed no differences. Overall, emotion regulation appears to alter appraisal conditions before judgment rather than only easing emotions after the fact. The findings suggest conversational AI can act as an external mediator in this appraisal process and that emotion-regulation mechanisms can be implemented and tested through technology-mediated interaction.

Citation status

* References for papers published after 2024 are currently being built.

This paper was written with support from the National Research Foundation of Korea.