Generative AI Confidence Modeling in Nursing Education Based on Interpretable Machine Learning: An Analytical Framework for Structured Questionnaire Data
Volume 19, Issue 2, 2025
Download| Author(s): |
Basit Raza Shah Abdul Latif University, Khairpur, Pakistan, basit.dharejo@salu.edu.pk Samina Rajper Shah Abdul Latif University, Khairpur, Pakistan, samina.rajper@salu.edu.pk Hidayat Shaikh Shah Abdul Latif University, Khairpur , Pakistan, hidayat.shaikh@salu.edu.pk Zahid Hussain Shar Shah Abdul Latif University, Khairpur , Pakistan, zahidhussain@gmai.com Sadaf Bibi Aror University of Art, Architecture, Design & Heritage, Sukkur , Pakistan, sadaf.bibi.dev@gmail.com Iqra Hyder Shah Abdul Latif University, Khairpur, Pakistan, iqrahyder.cs42@gmail.com Sadia Bibi Sukkur IBA University, Pakistan, sadiashah059@gmail.com |
|---|---|
| Abstract | With the rapid penetration of Generative Artificial Intelligence (GenAI) into education and healthcare practice, learners' "confidence in using" these tools is gradually becoming a key variable influencing effective application, sustained adoption, and risk control. Existing research largely remains at the level of descriptive statistics or qualitative discussions, lacking quantitative modeling methods that can simultaneously characterize the combined effects of "training experience—familiarity—confidence in using" and have a reproducible process. Therefore, this paper proposes an interpretable machine learning analysis framework for nursing education scenarios: variables such as training exposure, GenAI familiarity, tool usage, learning experience, and self-rated confidence are collected based on an online questionnaire; after data cleaning and privacy protection, feature engineering is used to unify key variables into modelable inputs, and "confidence" is predicted and explained through both regression and classification paths. This paper emphasizes using transparent and interpretable models as a strong baseline, and combines cross-validation and visualization analysis to output actionable educational recommendations, providing data-driven evidence for curriculum design, tiered training, and capacity building. |
| Keywords | Generative Artificial Intelligence; AI Literacy; Nursing Education; Questionnaire Data Analysis; Learning Analytics; Explainable Machine Learning. |
| Year | 2025 |
| Volume | 19 |
| Issue | 2 |
| Type | Research paper, manuscript, article |
| Journal Name | Journal of Information & Communication Technology | Publisher Name | ILMA University | Jel Classification | - | DOI | - | ISSN no (E, Electronic) | 2075-7239 | ISSN no (P, Print) | 2415-0169 | Country | Pakistan | City | Karachi | Institution Type | University | Journal Type | Open Access | Manuscript Processing | Blind Peer Reviewed | Format | Paper Link | https://jict.ilmauniversity.edu.pk/journal/jict/19.2/4.pdf | Page | 73-76 |