Increases in the use of artificial intelligence (AI) in education provide teachers with more practical guidance functions and new ways of teaching (Zhang and Aslan, 2021). The rapid rise in the use of AI has created many challenges and problems in higher education, and teachers, thus, must rethink their pedagogy and role in the use of AI technology to enhance student learning (Popenici and Kerr, 2017). AI is more difficult to learn than programming because it requires students to have a specific level of programming skills and complex knowledge. These challenges make it difficult to extend the learning of AI to higher education. Furthermore, the implementation of visual programming (e.g., Scratch) at higher education levels is no longer appropriate for students with specific programming skills and high levels of knowledge (Hu et al., 2021). First-time students encounter a variety of programming difficulties and challenges that make it difficult for them to develop programming skills (Yukselturk and Altiok, 2017). In the face of difficult learning challenges in traditional programming environments, it is vital for teachers to use visual programming environments to help students learn programming (Lye and Koh, 2014; Yukselturk and Altiok, 2017). Likewise, the use of a visual programming block approach in the AI curriculum can help students understand programming concepts and reduce misunderstandings.

In response to the current demand for AI learning and visual programming in higher education, in this study, an AI curriculum intended to address the current challenges of AI learning in higher education is implemented. Some studies have shown that the implementation of AI courses can enhance specific aspects of learning (e.g., cognitive abilities, skills) (Chai et al., 2021; Huang, 2021; Kong et al., 2021). This AI course uses an AI platform with a visual interface to increase students’ understanding of AI and lower the threshold and pressure related to machine learning. However, some studies have shown that humanities and science students have different learning abilities in different subjects (Billington et al., 2007; Katai, 2020). It is important to investigate the differences between students’ prior knowledge and cognitive styles to implement an AI curriculum. In particular, Kong et al. (2021) indicated that prior knowledge is not an important consideration in developing AI literacy in students. In summary, implementation of an AI curriculum requires an understanding of the impact of different aspects of student learning (e.g., cognitive style, computational thinking), which is crucial to improving the value of feedback related to the AI curriculum.

Cognitive style is often defined as different ways in which individuals process information (Papanikolaou et al., 2006). There are three different cognitive styles: creative, knowing, and planning (Cools and Van den Broeck, 2007). Cognitive style is an important learning factor because students have different learning preferences and adopt different learning strategies (Papanikolaou et al., 2006; Chen, 2010; Bouckenooghe et al., 2016). The learning strategies adopted by students are very important to their learning situation and learning needs (Rodrigues et al., 2019). Students in different educational departments will adopt different learning strategies and will, in turn, have different learning needs. It is necessary to understand what cognitive styles students use to solve problems in AI courses. In particular, students with different cognitive styles may have different computational thinking skills. A deeper understanding of students’ cognitive styles can provide teachers with information that will help enhance learning outcomes.

Since Wing (2006) promoted computational thinking in education and made it popular, and many teachers have begun to incorporate computational thinking into their courses. A common computational thinking problem-solving process has five basic elements (abstraction, decomposition, algorithmic thinking, evaluation, and generalization) (Selby and Woollard, 2013; Anderson, 2016; Tsai et al., 2021). It is crucial for teachers to use technology-based teaching and assessment tools to develop computational thinking skills in their students. For example, Tsai et al. (2021) developed a computational thinking assessment tool to assess students’ learning preferences and habits in the subject. Students enrolled in AI courses do not necessarily have extensive programming experience and have to learn mathematical and programming concepts in an easily explained manner (Kolachalama and Garg, 2018). Visual programming blocks may provide a convenient way for teachers to teach computational thinking, thus developing students’ computational thinking skills (Lye and Koh, 2014; Hsu et al., 2018; Xu et al., 2019). Teaching tools that reflect students’ learning levels are essential (Wong et al., 2020) and can help students understand AI concepts and acquire computational thinking skills through visual programming blocks (Lye and Koh, 2014). To help students face difficult AI courses, it is important to use visual programming blocks that will help them become familiar with AI skills and concepts.

However, there is no relevant research on implementation of a visual AI curriculum, and further exploration of students’ computational thinking skills and their cognitive styles is needed. This study implements an AI course that provides students with the ability to learn an AI course content and to share their models to enhance their computational thinking skills. Kong et al. (2021) indicated that the implementation of an AI course can enhance the acquisition of AI concepts among students at all levels and can reduce gaps for students with different educational backgrounds and different skill levels. Therefore, university students in different disciplines from two different universities served as participants in this study, and a visual AI platform was used to conduct an AI course. A teaching experiment was conducted to analyze the cognitive styles and computational thinking skills of university students from two different academic departments. The study was aimed at understanding the cognitive styles and computational thinking skills of the students from different departments in the AI course. Therefore, the following research questions were proposed in this study:

1. Does an AI visual programming course have different impacts on cognitive style among students from different departments?

2. Does an AI visual programming course have different impacts on computational thinking skills among students from different departments?

Between-group differences in cognitive style and computational thinking were observed in the Sci-student and the Hum-student groups after the implementation of the AI course. The differences in cognitive style and computational thinking between the two groups are explained in Tables 3, 4. The Mann-Whitney U-test was conducted to analyze the data in terms of cognitive style and computational thinking skills.

The findings of this study showed that the students in the two groups did not have significant differences in terms of creative style (Mann-Whitney U = 1,234, p > 0.05), planning style (Mann-Whitney U = 1,162.5, p > 0.05), and knowing style (Mann-Whitney U = 1,209.5, p > 0.05). No significant between-group differences are found in terms of cognitive style after the post-test, as shown in Table 3. The findings were not surprising, since cognitive styles have stable learning characteristics (Bouckenooghe et al., 2016), and students are unlikely to change their cognitive style in a short period of time after taking an AI course. Another possible explanation for this finding is that both groups of students had similar cognitive styles before the experiment. After the experiment, both groups still had similar cognitive styles. The findings of this study also imply that the cognitive styles of students from different disciplines are an important issue in this field. In particular, Billington et al. (2007) indicated that among students’ systemic and empathic characteristics, science students have higher systemic characteristics, and humanities students have higher empathic characteristics. Furthermore, Katai (2020) indicated that in two groups of students (humanities and science students), differences in academic achievement were reduced as their learning progressed in a suitable e-learning environment. In this study, understanding the cognitive styles of students in different subjects helped teachers design appropriate AI courses that incorporate, e.g., the use of visual tools to reduce learning difficulties and increase student engagement. An AI course was implemented for a relatively short period of time. Therefore, it was difficult to assess the changes in students’ cognitive style when using visual programming. However, according to computational thinking research, teachers should help students understand the concept of computational thinking in a meaningful way by explaining the programming process and using real-life examples to explain computational thinking (Voogt et al., 2015). Similarly, teachers can explain AI with specific examples and use appropriate teaching tools in AI courses. This approach may influence students’ cognitive styles by changing their computational thinking.

The study findings show that the university students in the two groups did not have significant differences in terms of abstraction (Mann-Whitney U = 1.175.5, p > 0.05), decomposition (Mann-Whitney U = 1,212.5, p > 0.05), algorithmic thinking (Mann-Whitney U = 1,228.5, p > 0.05), evaluation (Mann-Whitney U = 1,032.5, p > 0.05), and generalization (Mann-Whitney U = 1,156, p > 0.05) (Table 4). This study was limited by the short duration of the experiment and the fact that computational thinking is a high-level skill that is difficult to develop in a short period of time. To develop students’ computational thinking skills, it is necessary to design and adjust curricula to suit the learning level of students in different disciplines (Katai, 2020). However, it is important to note that the results of the study did show that the Hum-student group had higher scores in abstraction (Sci-student group = 3.61, Hum-student group = 3.69), algorithmic thinking (Sci-student group = 3.53, Hum-student group = 3.76), and generalization (Sci-student group = 3.67, Hum-student group = 3.76) than the Sci-student group. This may indicate that the Hum-student group was more perceptually influenced in the learning process during the AI course in terms of visual programming. In addition, Karalar and Alpaslan (2021) indicated that students with more experience in programming have higher computational thinking skills than those with no programming experience. The Sci-student group had more programming experience, and the Hum-student group had less, but the findings were not consistent with those of previous studies (Karalar and Alpaslan, 2021). This was because the students manipulated the visual programming block learning process (e.g., modeling), which made it possible for them to repeat computational thinking patterns that would enhance their computational thinking skills. For example, machine learning requires the construction of models, and students learn computational thinking skills by sequencing the types of models (algorithms) and designing model steps (modularization). That is to say, the learning benefits of using visual programming blocks (e.g., ease of use) can make it possible for two different groups of university students to acquire similar computational thinking skills. All students spent the same time learning about machine learning, and visual programming reduced the difficulty of learning machine learning and helped the students to effectively understand the learning process.

This study examines the differences in cognitive style and computational thinking during the implementation of AI courses among a sample of university students from different departments. AI courses present complex and difficult learning challenges. An AI platform with visual interfaces plays a key role to help students learn AI easily. However, teaching a one-size-fits-all approach for content delivery may increase failure and dropout rates. A student-centered AI content design and use of appropriate teaching tools and teaching strategies (e.g., visualized programming) can reduce learning difficulties and expand the learning of AI subjects in students with different learning backgrounds. As proposed in this study, the implementation of visual programming blocks in the AI curriculum helped students and students from different learning backgrounds learn about AI. There were no significant between-group differences in cognitive style and computational thinking among the university students after taking the AI course. To help students from different learning backgrounds learn AI, the use of appropriate teaching tools and teachers who can explain programming thinking may help students better understand AI. In addition, a visual programming learning environment may be a potentially important technology in the field of AI to help all students easily learn about AI. This study discusses a means by which all students can learn about AI, allowing them to learn AI concepts without the constraints of programming syntax, questions, and practical learning tasks.

This study was limited by the lack of pre-testing to examine students’ cognitive style and computational thinking abilities, which means that we did not know the original learning status of the students. and it was also limited by short implementation time. In this study, the survey was only given to university students from two different departments, which made it impossible to learn about the learning situation of general university students. In general, the majority of university students in the Electrical Engineering and Computer Science College are interested in and major in computer science. The majority of university students in the College of Education had a wide range of learning interests and a preference for studying literary subjects. However, to understand the learning situation of different university students in terms of the AI curriculum, the AI technology could be used to monitor and collect data on students’ learning history to provide a better understanding of changes in the learning situation of three different groups of university students. Future research could consider a systematic survey of AI technology and the use of general university student surveys to compare the learning of AI among three groups. Finally, the lack of a long-term evaluation of the effectiveness of the course limits the understanding of its impact. However, it is important to note that different learners have different ways of processing information (i.e., different cognitive styles) and develop different computational thinking skills during an AI course. It would be interesting to discover what learning processes change the cognitive style and computational thinking skills of successful AI learners after a long period of study, because this information is essential for instructional designers and educators, so it would be useful to conduct research on changes in learning processes around cognitive style and computational thinking skills. Future research should examine the learning outcomes of using AI courses over time and evaluate the learning benefits of AI as they relate to different aspects of learning (e.g., self-regulated learning).

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants’ legal guardian was not required to participate in this study in accordance with the national legislation and the institutional requirements. Written informed consent was obtained from the individuals for the publication of any potentially identifiable images or data included in this article.

C-JW was responsible for analyzing and interpreting the data. J-HC was in charge of designing the research framework, collecting and interpreting the data, and revising the article. P-SC was responsible for collecting the data and revising the article. P-HW was in charge of designing the research framework and collecting the data. H-XZ was responsible for the data analysis, the design of the research framework, and wrote the first draft of the manuscript. J-HC, C-JW, and H-XZ did the overall design of the study. J-HC and C-JW reviewed, edited, and supervised the article. All authors have read and approved the final version of the manuscript, designed the questionnaire, collected the data, and wrote sections detailing the sample of students and data collection, made a substantial, direct, and intellectual contribution to the study, and agreed to the published final manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.