AI Literacy as a Key Driver of User Experience in AI-Powered Assessment: Insights from Socratic Mind

Meryem Yilmaz Soylu, Jeonghyun Lee, Jui-Tse Hung, Christopher Zhang Cui, David A. Joyner
7/29/2025
cs.HCcs.AI

No confident code implementation found for this paper.

Abstract

As Artificial Intelligence (AI) tools become increasingly embedded in higher education, understanding how students interact with these systems is essential to supporting effective learning. This study examines how students' AI literacy and prior exposure to AI technologies shape their perceptions of Socratic Mind, an interactive AI-powered formative assessment tool. Drawing on Self-Determination Theory and user experience research, we analyze relationships among AI literacy, perceived usability, satisfaction, engagement, and perceived learning effectiveness. Data from 309 undergraduates in Computer Science and Business courses were collected through validated surveys. Partial least squares structural equation modeling showed that AI literacy - especially self-efficacy, conceptual understanding, and application skills - significantly predicts usability, satisfaction, and engagement. Usability and satisfaction, in turn, strongly predict perceived learning effectiveness, while prior AI exposure showed no significant effect. These findings highlight that AI literacy, rather than exposure alone, shapes student experiences. Designers should integrate adaptive guidance and user-centered features to support diverse literacy levels, fostering inclusive, motivating, and effective AI-based learning environments.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{soylu2025literacy,
  title  = {AI Literacy as a Key Driver of User Experience in AI-Powered Assessment: Insights from Socratic Mind},
  author = {Meryem Yilmaz Soylu and Jeonghyun Lee and Jui-Tse Hung and Christopher Zhang Cui and David A. Joyner},
  year   = {2025},
  eprint = {2507.21654},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.21654v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2507.21654.svg)](https://codeofpaper.com/paper/2507.21654)

Discussion