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<p>This descriptive and exploratory computational qualitative study investigated the stance of college students towards their intention to use artificial intelligence educational (AIEd) tools. Toward this goal, 1,971 college students participated in the study. They completed an open-ended online survey form that elicited their stance on using the AIEd tools. Descriptive statistics revealed that over 50% of the students supported using AIEd tools, but they had reservations or preferred certain limitations. The most occurring word in the corpus is “tool”, confirming that the object of discussion was viewed as a tool supporting educational tasks. The corpus contained diverse words expressing the students’ stance, with no single word appearing overwhelmingly prominent. Topic modeling revealed that students shared similar positions with teachers regarding the use of AIEd tools. The Support Vector Machine (SVM) prediction model achieved a good weighted F1 and accuracy scores of 77% and 79%, respectively. Additional testing revealed that the model correctly identified over one-half of student stances in a new dataset. Therefore, the students’ stances are mostly mixed, their positions are similar to those of the teachers, and the model can reasonably predict the students’ stances. Implications are also discussed.</p>

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“How about our stance?”: college students’ stance on using artificial intelligence educational tools

  • 期刊论文

  • 发表日期：

    2025年12月9日

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  • Rex Bringula,
  • Arlene Trillanes,
  • Annaliza Catacutan-Bangit

摘要

This descriptive and exploratory computational qualitative study investigated the stance of college students towards their intention to use artificial intelligence educational (AIEd) tools. Toward this goal, 1,971 college students participated in the study. They completed an open-ended online survey form that elicited their stance on using the AIEd tools. Descriptive statistics revealed that over 50% of the students supported using AIEd tools, but they had reservations or preferred certain limitations. The most occurring word in the corpus is “tool”, confirming that the object of discussion was viewed as a tool supporting educational tasks. The corpus contained diverse words expressing the students’ stance, with no single word appearing overwhelmingly prominent. Topic modeling revealed that students shared similar positions with teachers regarding the use of AIEd tools. The Support Vector Machine (SVM) prediction model achieved a good weighted F1 and accuracy scores of 77% and 79%, respectively. Additional testing revealed that the model correctly identified over one-half of student stances in a new dataset. Therefore, the students’ stances are mostly mixed, their positions are similar to those of the teachers, and the model can reasonably predict the students’ stances. Implications are also discussed.