Exploring barriers to AI course assistant adoption: a mixed-methods study on student non-utilization
Abstract The integration of artificial intelligence (AI) in education has demonstrated significant potential in enhancing learning experiences, yet many students choose to not engage with AI tools available within their courses. This mixed-methods exploratory study investigates the barriers to the a...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-025-00312-x |
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| author | George Hanshaw Christopher Sullivan |
| author_facet | George Hanshaw Christopher Sullivan |
| author_sort | George Hanshaw |
| collection | DOAJ |
| description | Abstract The integration of artificial intelligence (AI) in education has demonstrated significant potential in enhancing learning experiences, yet many students choose to not engage with AI tools available within their courses. This mixed-methods exploratory study investigates the barriers to the adoption of Spark, the AI course assistant implemented at Los Angeles Pacific University (LAPU). Analyzing 602 student survey responses collected through End-of-Course (EOC) evaluations, this study investigates the reasons behind this non-utilization, utilizing combined sentiment analysis with thematic analysis. The findings reveal that perceived necessity, lack of familiarity, and minimal awareness of Spark are primary factors contributing to non-use, alongside external issues such as time constraints and preference for traditional study methods. Notably, the absence of negative sentiment suggests that resistance is not rooted in dissatisfaction with the tool itself, but in limited perceived value or awareness. The study highlights the critical role of communication strategies, faculty engagement, and integration of AI tools into coursework to improve adoption. Addressing these barriers through improved communication, faculty-led AI integration, and targeted training initiatives can significantly enhance student engagement and learning outcomes. Without intervention, non-utilization may limit students’ opportunity to build foundational AI literacy—an increasingly important competency in academic and professional environments shaped by AI technologies. These insights offer actionable guidance for educators and institutions seeking to maximize the benefits of AI powered learning tools, and foster AI literacy among students in the evolving technological landscape. |
| format | Article |
| id | doaj-art-bf4e38cacf6d4db4a1078f6cb5082562 |
| institution | DOAJ |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-bf4e38cacf6d4db4a1078f6cb50825622025-08-20T03:05:06ZengSpringerDiscover Artificial Intelligence2731-08092025-07-015112410.1007/s44163-025-00312-xExploring barriers to AI course assistant adoption: a mixed-methods study on student non-utilizationGeorge Hanshaw0Christopher Sullivan1Digital Learning Solutions, Los Angeles Pacific UniversityDepartment of Psychology, Los Angeles Pacific UniversityAbstract The integration of artificial intelligence (AI) in education has demonstrated significant potential in enhancing learning experiences, yet many students choose to not engage with AI tools available within their courses. This mixed-methods exploratory study investigates the barriers to the adoption of Spark, the AI course assistant implemented at Los Angeles Pacific University (LAPU). Analyzing 602 student survey responses collected through End-of-Course (EOC) evaluations, this study investigates the reasons behind this non-utilization, utilizing combined sentiment analysis with thematic analysis. The findings reveal that perceived necessity, lack of familiarity, and minimal awareness of Spark are primary factors contributing to non-use, alongside external issues such as time constraints and preference for traditional study methods. Notably, the absence of negative sentiment suggests that resistance is not rooted in dissatisfaction with the tool itself, but in limited perceived value or awareness. The study highlights the critical role of communication strategies, faculty engagement, and integration of AI tools into coursework to improve adoption. Addressing these barriers through improved communication, faculty-led AI integration, and targeted training initiatives can significantly enhance student engagement and learning outcomes. Without intervention, non-utilization may limit students’ opportunity to build foundational AI literacy—an increasingly important competency in academic and professional environments shaped by AI technologies. These insights offer actionable guidance for educators and institutions seeking to maximize the benefits of AI powered learning tools, and foster AI literacy among students in the evolving technological landscape.https://doi.org/10.1007/s44163-025-00312-xAI course assistantsEducational technologyStudent engagementMixed-methods researchTechnology adoptionLearning enhancement |
| spellingShingle | George Hanshaw Christopher Sullivan Exploring barriers to AI course assistant adoption: a mixed-methods study on student non-utilization Discover Artificial Intelligence AI course assistants Educational technology Student engagement Mixed-methods research Technology adoption Learning enhancement |
| title | Exploring barriers to AI course assistant adoption: a mixed-methods study on student non-utilization |
| title_full | Exploring barriers to AI course assistant adoption: a mixed-methods study on student non-utilization |
| title_fullStr | Exploring barriers to AI course assistant adoption: a mixed-methods study on student non-utilization |
| title_full_unstemmed | Exploring barriers to AI course assistant adoption: a mixed-methods study on student non-utilization |
| title_short | Exploring barriers to AI course assistant adoption: a mixed-methods study on student non-utilization |
| title_sort | exploring barriers to ai course assistant adoption a mixed methods study on student non utilization |
| topic | AI course assistants Educational technology Student engagement Mixed-methods research Technology adoption Learning enhancement |
| url | https://doi.org/10.1007/s44163-025-00312-x |
| work_keys_str_mv | AT georgehanshaw exploringbarrierstoaicourseassistantadoptionamixedmethodsstudyonstudentnonutilization AT christophersullivan exploringbarrierstoaicourseassistantadoptionamixedmethodsstudyonstudentnonutilization |