ArticleBMC psychology2026
University students' acceptance of generative artificial intelligence tools: a mixed-methods study on opinions, attitudes, and behavioral intentions.
Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
3 authors.
Funding
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Abstract
This study examined university students' acceptance of generative artificial intelligence tools. For this purpose, the research was designed with a sequential explanatory model from mixed research methods. The first phase was patterned in the survey model, and the study participants consisted of 601 undergraduate students studying at a state university in Türkiye. Demographic variables, a questionnaire, and generative artificial intelligence tools, an acceptance scale, were used as data collection tools. First, assumptions were evaluated, and tests were decided to analyze the data. Within the scope of the research, Structural Equation Modelling was performed between the sub-dimensions of the acceptance scale. The case study was designed for the second phase, and interviews were conducted with 80 undergraduate students. As a result of the results, it was revealed that perceived usefulness, perceived ease of use, gender, duration of use, frequency, and purpose of use play a holistic role in shaping opinions, usage attitudes, and behavioral intentions toward generative artificial intelligence tools.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.