Evaluation of attitudes of university students towards artificial intelligence using data mining methods.
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| Titel: | Evaluation of attitudes of university students towards artificial intelligence using data mining methods. |
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| Autoren: | Sulak SA; Ahmet Kelesoglu Educational Faculty, Necmettin Erbakan University, Konya, Türkiye. sulak@erbakan.edu.tr. |
| Quelle: | Scientific reports [Sci Rep] 2025 Nov 25; Vol. 15 (1), pp. 41941. Date of Electronic Publication: 2025 Nov 25. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| MeSH-Schlagworte: | Artificial Intelligence* , Data Mining*/methods , Students*/psychology , Attitude*, Humans ; Universities ; Female ; Male ; Young Adult ; Algorithms ; Adult ; Decision Trees |
| Abstract: | Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethics declarations: Prior to the commencement of data collection, ethical approval was obtained from the Ethics Committee for Social and Behavioral Sciences at Necmettin Erbakan University (Protocol No: 2025 − 280). During the administration of the questionnaire, participants were clearly informed that their involvement was entirely voluntary. They were also assured that the data collected would be used strictly for scholarly purposes, particularly in the context of disseminating research findings through academic publications. All methods were performed in accordance with the relevant guidelines and regulations, as well as the principles of the Declaration of Helsinki. This study analyzes university students' attitudes towards artificial intelligence. Within the scope of the research, the data obtained from 1379 students through scale application were classified into three classes as "Insufficient", "Sufficient" and "Strongly Sufficient" according to their attitudes towards artificial intelligence. The data was classified by data mining methods. For this purpose, MLP, Decision Tree, KNN, XGBoost, Random Forest, CatBoost and SVM algorithms were used. The performance of the model was evaluated with a 5-fold cross-validation method. For each algorithm, basic metrics such as accuracy, precision, recall and F1 score were calculated and the classification performance was compared. According to the results, the highest F1-Score accuracy rate was 95.52% for the SVM algorithm. This was followed by CatBoost (93.66%), Random Forest (92.56%) and XGBoost (92.36%). The lowest success rates were observed in MLP (81.87%) and Decision Tree (82.72%) models. Confusion matrices revealed a tendency for frequent confusion with other classes, especially in the Strongly Sufficient class. The study concluded that advanced classification algorithms provide powerful and reliable tools for analyzing students' attitudes towards artificial intelligence. These findings may contribute to the development of educational policies and strategies for AI literacy. (© 2025. The Author(s).) |
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| Contributed Indexing: | Keywords: Artificial intelligence; Attitude; CatBoost; Data mining; Random forrest; SVM |
| Entry Date(s): | Date Created: 20251125 Date Completed: 20251125 Latest Revision: 20251128 |
| Update Code: | 20251128 |
| PubMed Central ID: | PMC12647754 |
| DOI: | 10.1038/s41598-025-25748-0 |
| PMID: | 41290803 |
| Datenbank: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethics declarations: Prior to the commencement of data collection, ethical approval was obtained from the Ethics Committee for Social and Behavioral Sciences at Necmettin Erbakan University (Protocol No: 2025 − 280). During the administration of the questionnaire, participants were clearly informed that their involvement was entirely voluntary. They were also assured that the data collected would be used strictly for scholarly purposes, particularly in the context of disseminating research findings through academic publications. All methods were performed in accordance with the relevant guidelines and regulations, as well as the principles of the Declaration of Helsinki.<br />This study analyzes university students' attitudes towards artificial intelligence. Within the scope of the research, the data obtained from 1379 students through scale application were classified into three classes as "Insufficient", "Sufficient" and "Strongly Sufficient" according to their attitudes towards artificial intelligence. The data was classified by data mining methods. For this purpose, MLP, Decision Tree, KNN, XGBoost, Random Forest, CatBoost and SVM algorithms were used. The performance of the model was evaluated with a 5-fold cross-validation method. For each algorithm, basic metrics such as accuracy, precision, recall and F1 score were calculated and the classification performance was compared. According to the results, the highest F1-Score accuracy rate was 95.52% for the SVM algorithm. This was followed by CatBoost (93.66%), Random Forest (92.56%) and XGBoost (92.36%). The lowest success rates were observed in MLP (81.87%) and Decision Tree (82.72%) models. Confusion matrices revealed a tendency for frequent confusion with other classes, especially in the Strongly Sufficient class. The study concluded that advanced classification algorithms provide powerful and reliable tools for analyzing students' attitudes towards artificial intelligence. These findings may contribute to the development of educational policies and strategies for AI literacy.<br /> (© 2025. The Author(s).) |
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| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-025-25748-0 |