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

arXiv: https://arxiv.org/abs/2507.21654 · HTML: https://mlsanity.com/paper/2507.21654/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 73 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [MAILS -- Meta AI Literacy Scale: Development and Testing of an AI Literacy Questionnaire Based on Well-Founded Competency Models and Psychological Change- and Meta-Competencies](https://mlsanity.com/paper/2302.09319/) (2023)
- [Chatting with a Learning Analytics Dashboard: The Role of Generative AI Literacy on Learner Interaction with Conventional and Scaffolding Chatbots](https://mlsanity.com/paper/2411.15597/) (2024)
- [Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person](https://mlsanity.com/paper/2305.14580/) (2023)

## Built on by (most cited first, 1 of 1)

- [Socratic Mind: Impact of a Novel GenAI-Powered Assessment Tool on Student Learning and Higher-Order Thinking](https://mlsanity.com/paper/2509.16262/) (2025)

## Abstract (arXiv)

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.