Factors Influencing College Students’ Generative Artificial Intelligence Usage Behavior in Mathematics Learning: A Case from China
Abstract
:1. Introduction
- (1)
- What factors influence college students’ intention to use and behavior regarding the use of GAI to support their mathematics learning?
- (2)
- Does mathematics motivation influence college students’ intention to use and behavior regarding the use of GAI to support their mathematics learning?
- (3)
- Does mathematics motivation moderate the relationships between the pathways in the model of college students’ use of GAI?
2. Literature Review and Hypothesis Development
2.1. Generative Artificial Intelligence in Mathematics Education
2.2. Characteristics of College Students’ Mathematics Learning
2.3. Unified Theory of Acceptance and Use of Technology
2.3.1. Performance Expectancy (PE)
2.3.2. Effort Expectancy (EE)
2.3.3. Social Influence (SI)
2.3.4. Facilitating Conditions (FC)
2.3.5. Personal Innovativeness (PI)
2.3.6. Individual Demand (ID)
2.3.7. Behavioral Intention (BI) and Usage Behavior (UB)
2.4. Mathematics Motivation (MM) to Apply GAI for Learning
2.4.1. Theoretical Analysis
2.4.2. Proposed Model
3. Methodology
3.1. Study Instrument
3.2. Data Collection
3.3. Data Analysis
3.4. Ethical Considerations
4. Results
4.1. Normality Analysis
4.2. Measurement Model
4.3. Confirmatory Factor Analysis
4.4. Structural Model and Hypothesis Test
4.5. Direct, Indirect, and Total Effect
4.6. Moderating Effect
5. Discussion
6. Conclusions and Implications
7. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
GAI | generative artificial intelligence |
UTAUT | the Unified Theory of Acceptance and Use of Technology |
PLS-SEM | partial least squares structural equation modeling |
Appendix A
Items | Constructs and Contents | Reference |
---|---|---|
Performance Expectancy (PE) | (Venkatesh et al., 2012; Wijaya et al., 2022c) | |
PE1 | I think GAI is very helpful in my math studies. | |
PE2 | GAI can increase my chances of gaining important knowledge in math learning. | |
PE3 | GAI can effectively inspire me to solve math problems and give me problem-solving ideas. | |
Effort Expectancy (EE) | (Venkatesh et al., 2012; Zeng, 2019) | |
EE1 | I find it easy to use GAI in my math studies. | |
EE2 | GAI provides math information that is easy to understand. | |
EE3 | I feel that the GAI platform has good interactive performance in math learning. | |
Social Influence (SI) | (Venkatesh et al., 2003, 2012) | |
SI1 | My teacher encouraged me to use GAI to help me learn math. | |
SI2 | I use GAI because most of the students in my class use it. | |
SI3 | I use GAI because mainstream media recommend it. | |
Facilitating Conditions (FC) | (Venkatesh et al., 2003, 2012) | |
FC1 | I have the necessary electronic equipment and network environment to use GAI for learning mathematics. | |
FC2 | I have the basic knowledge to use GAI for math. | |
Individual Demand (ID) | (Gao, 2023) | |
ID1 | GAI can identify the math materials I need based on the problem. | |
ID2 | GAI can recommend highly adapted math problems based on my learning situation. | |
ID3 | GAI can gradually understand my math learning needs over successive sessions. | |
ID4 | GAI plays the role of a good teacher and peer in solving my math problems. | |
Personal Innovativeness (PI) | (Tian et al., 2024; Wijaya et al., 2022b) | |
PI1 | I am willing to use new technology in my math learning. | |
PI2 | When I hear about a new technology, I am curious to experience it and feel how it can help my math learning. | |
PI3 | I usually try new technologies for learning mathematics earlier than my peers around me. | |
PI4 | When a new learning technology is available, I will use it to learn mathematics. | |
Behavioral Intention (BI) | (Alzahrani et al., 2019) | |
BI1 | I am willing to continue using GAI in my math learning if the content is appropriate. | |
BI2 | I would be willing to try using GAI in the learning process of other courses. | |
BI3 | I would recommend that a friend use GAI for math learning. | |
Usage Behavior (UB) | (Wijaya et al., 2022c; Yuan et al., 2023) | |
UB1 | I am relatively satisfied with the effectiveness of my use of GAI. | |
UB2 | I have extensive experience using GAI technology for learning mathematics. | |
UB3 | Using GAI has become part of my daily math learning routine. |
Items | Constructs and Contents | Reference |
---|---|---|
Intrinsic Goal Orientation (IGO) | (Pintrich, 1991; Zheng et al., 2024) | |
IGO1 | I tend to choose more challenging math problems and materials provided by GAI. | |
IGO2 | I want GAI to provide math course materials that stimulate curiosity, even if they are not easy to learn. | |
IGO3 | The most satisfying thing for me is the help GAI can provide in promoting a thorough understanding of what I am learning. | |
IGO4 | I wish GAI had more extensive homework and project materials, even if they don’t necessarily improve my math grades immediately. | |
IGO5 | Most of the time, I get satisfaction and a sense of accomplishment from GAI’s help learning math. | |
Extrinsic Goal Orientation (EGO) | (Liu & Lin, 2010; Pintrich, 1991) | |
EGO1 | I hope using GAI will help me improve my academic performance in math. | |
EGO2 | I hope using GAI will help me achieve higher grades in my math courses than most classmates. | |
EGO3 | When learning math, I use GAI to show others how well I can learn math. | |
EGO4 | I expect to receive praise from my teacher or recognition from my classmates for using GAI in my math learning. | |
EGO5 | Using GAI in math will help me get into the college I want to attend. |
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Demographic | Type | Frequency | Percentage |
---|---|---|---|
Gender | Male | 121 | 36.56% |
Female | 210 | 63.44% | |
Age | Freshman | 64 | 19.34% |
Sophomore | 51 | 15.41% | |
Junior | 106 | 32.02% | |
Senior | 83 | 25.07% | |
Postgraduate | 27 | 8.16% | |
Use GAI in Math Learning | Everyday | 17 | 5.14% |
Frequently | 88 | 26.59% | |
Sometimes | 176 | 53.17% | |
Seldom | 34 | 10.27% | |
Rarely | 16 | 4.83% | |
Major | Mathematics | 220 | 66.47% |
Engineering | 26 | 7.85% | |
Economics | 68 | 20.54% | |
Others | 17 | 5.14% |
Items | Mean | Std. Deviation | Skewness | Kurtosis |
---|---|---|---|---|
Statistic | Statistic | Statistic | Statistic | |
PE1 | 3.73 | 0.825 | −0.384 | 0.356 |
PE2 | 3.70 | 0.834 | −0.338 | 0.091 |
PE3 | 3.69 | 0.832 | −0.323 | −0.071 |
EE1 | 3.60 | 0.859 | −0.279 | −0.131 |
EE2 | 3.69 | 0.807 | −0.072 | −0.528 |
EE3 | 3.57 | 0.833 | −0.185 | −0.355 |
SI1 | 3.62 | 0.842 | −0.319 | 0.436 |
SI2 | 3.32 | 0.957 | −0.123 | −0.346 |
SI3 | 3.46 | 0.944 | −0.338 | −0.214 |
FC1 | 3.80 | 0.770 | −0.479 | 0.513 |
FC2 | 3.73 | 0.811 | −0.260 | −0.031 |
ID1 | 3.59 | 0.835 | −0.291 | −0.160 |
ID2 | 3.56 | 0.852 | −0.130 | −0.162 |
ID3 | 3.66 | 0.820 | −0.462 | 0.255 |
ID4 | 3.58 | 0.854 | −0.290 | −0.108 |
PI1 | 4.00 | 0.701 | −0.532 | 0.891 |
PI2 | 3.88 | 0.769 | −0.554 | 0.456 |
PI3 | 3.56 | 0.859 | −0.208 | −0.041 |
PI4 | 3.79 | 0.828 | −0.408 | 0.050 |
BI1 | 3.88 | 0.738 | −0.436 | 0.916 |
BI2 | 3.79 | 0.768 | −0.352 | 0.327 |
BI3 | 3.81 | 0.786 | −0.399 | 0.480 |
UB1 | 3.67 | 0.785 | −0.170 | 0.035 |
UB2 | 3.58 | 0.760 | 0.039 | 0.066 |
UB3 | 3.54 | 0.881 | −0.067 | −0.449 |
IGO1 | 3.73 | 0.809 | −0.530 | 0.632 |
IGO2 | 3.78 | 0.813 | −0.329 | −0.146 |
IGO3 | 3.78 | 0.775 | −0.348 | 0.071 |
IGO4 | 3.78 | 0.812 | −0.424 | 0.166 |
IGO5 | 3.69 | 0.769 | −0.128 | −0.137 |
EGO1 | 3.89 | 0.748 | −0.382 | −0.003 |
EGO2 | 3.71 | 0.831 | −0.346 | 0.118 |
EGO3 | 3.43 | 0.942 | −0.222 | −0.227 |
EGO4 | 3.45 | 0.953 | −0.310 | −0.100 |
EGO5 | 3.74 | 0.816 | −0.196 | −0.303 |
Constructs | Items | Factor Loading | Cronbach’s Alpha | Composite Reliability | AVE |
---|---|---|---|---|---|
Performance Expectancy | PE1 | 0.898 | 0.874 | 0.923 | 0.799 |
PE2 | 0.897 | ||||
PE3 | 0.887 | ||||
Effort Expectancy | EE1 | 0.877 | 0.848 | 0.908 | 0.767 |
EE2 | 0.862 | ||||
EE3 | 0.888 | ||||
Social Influence | SI1 | 0.820 | 0.817 | 0.891 | 0.732 |
SI2 | 0.867 | ||||
SI3 | 0.879 | ||||
Facilitating Conditions | FC1 | 0.905 | 0.813 | 0.914 | 0.842 |
FC2 | 0.930 | ||||
Individual Demand | ID1 | 0.896 | 0.921 | 0.944 | 0.808 |
ID2 | 0.904 | ||||
ID3 | 0.883 | ||||
ID4 | 0.913 | ||||
Personal Innovativeness | PI1 | 0.820 | 0.873 | 0.914 | 0.726 |
PI2 | 0.877 | ||||
PI3 | 0.810 | ||||
PI4 | 0.898 | ||||
Behavioral Intention | BI1 | 0.911 | 0.890 | 0.932 | 0.820 |
BI2 | 0.879 | ||||
BI3 | 0.926 | ||||
Usage Behavior | UB1 | 0.895 | 0.881 | 0.926 | 0.807 |
UB2 | 0.908 | ||||
UB3 | 0.892 | ||||
Mathematics Motivation | IGO1 | 0.808 | 0.930 | 0.941 | 0.613 |
IGO2 | 0.805 | ||||
IGO3 | 0.773 | ||||
IGO4 | 0.805 | ||||
IGO5 | 0.850 | ||||
EGO1 | 0.752 | ||||
EGO2 | 0.765 | ||||
EGO3 | 0.771 | ||||
EGO4 | 0.702 | ||||
EGO5 | 0.791 |
PI | ID | UB | FC | EE | MM | SI | PE | BI | |
---|---|---|---|---|---|---|---|---|---|
PI | 0.852 | ||||||||
ID | 0.667 | 0.899 | |||||||
UB | 0.702 | 0.716 | 0.899 | ||||||
FC | 0.655 | 0.583 | 0.751 | 0.917 | |||||
EE | 0.705 | 0.781 | 0.724 | 0.652 | 0.876 | ||||
MM | 0.738 | 0.726 | 0.781 | 0.684 | 0.745 | 0.783 | |||
SI | 0.552 | 0.606 | 0.656 | 0.583 | 0.589 | 0.670 | 0.856 | ||
PE | 0.626 | 0.712 | 0.666 | 0.579 | 0.729 | 0.655 | 0.517 | 0.894 | |
BI | 0.722 | 0.635 | 0.765 | 0.743 | 0.678 | 0.778 | 0.613 | 0.653 | 0.905 |
PI | ID | UB | FC | EE | MM | SI | PE | BI | |
---|---|---|---|---|---|---|---|---|---|
PI | |||||||||
ID | 0.745 | ||||||||
UB | 0.802 | 0.795 | |||||||
FC | 0.776 | 0.672 | 0.882 | ||||||
EE | 0.820 | 0.886 | 0.835 | 0.780 | |||||
MM | 0.813 | 0.787 | 0.861 | 0.778 | 0.839 | ||||
SI | 0.651 | 0.697 | 0.773 | 0.714 | 0.708 | 0.771 | |||
PE | 0.714 | 0.791 | 0.757 | 0.685 | 0.846 | 0.722 | 0.609 | ||
BI | 0.818 | 0.700 | 0.863 | 0.875 | 0.777 | 0.844 | 0.715 | 0.739 |
Hypothesis | Path Coefficient (β) | Sample Mean | Standard Deviation | T Statistic | p Value | Interpretation (p < 0.05) | |
---|---|---|---|---|---|---|---|
H1 | Performance Expectancy→Behavioral Intention | 0.178 | 0.180 | 0.063 | 2.813 | 0.005 | Sig. |
H2 | Effort Expectancy→Behavioral Intention | 0.046 | 0.046 | 0.081 | 0.567 | 0.571 | Not Sig. |
H3 | Social Influence→Behavioral Intention | 0.120 | 0.117 | 0.059 | 2.042 | 0.041 | Sig. |
H4 | Facilitating Conditions→Usage Behavior | 0.290 | 0.290 | 0.057 | 5.117 | 0.000 | Sig. |
H5 | Personal Innovativeness→Behavioral Intention | 0.257 | 0.250 | 0.067 | 3.815 | 0.000 | Sig. |
H6 | Individual Demand→Behavioral Intention | −0.067 | −0.069 | 0.077 | 0.873 | 0.383 | Not Sig. |
H7 | Individual Demand→Usage Behavior | 0.232 | 0.234 | 0.063 | 3.703 | 0.000 | Sig. |
H8 | Behavioral Intention→Usage Behavior | 0.204 | 0.193 | 0.081 | 2.527 | 0.012 | Sig. |
H9 | Mathematics Motivation→Behavioral Intention | 0.406 | 0.413 | 0.087 | 4.672 | 0.000 | Sig. |
H10 | Mathematics Motivation→Usage Behavior | 0.255 | 0.264 | 0.078 | 3.276 | 0.001 | Sig. |
Factor | Determinant | Effect | ||
---|---|---|---|---|
Direct | Indirect | Total | ||
Behavioral Intention (BI) (R2 = 0.680) | Performance Expectancy | 0.178 | 0 | 0.178 |
Effort Expectancy | 0.046 | 0 | 0.046 | |
Social Influence | 0.120 | 0 | 0.120 | |
Individual Demand | −0.067 | 0 | −0.067 | |
Personal Innovativeness | 0.257 | 0 | 0.257 | |
Mathematics Motivation | 0.406 | 0 | 0.406 | |
Usage Behavior (UB) (R2 = 0.739) | Personal Innovativeness | 0 | 0.052 | 0.052 |
Individual Demand | 0.218 | 0 | 0.218 | |
Facilitating Conditions | 0.290 | 0 | 0.290 | |
Effort Expectancy | 0 | 0.009 | 0.009 | |
Mathematics Motivation | 0.338 | 0 | 0.338 | |
Social Influence | 0 | 0.024 | 0.024 | |
Performance Expectancy | 0 | 0.036 | 0.036 | |
Behavioral Intention | 0.204 | 0 | 0.204 |
Regression Analysis (n = 331) | Standardized Regression Coefficient (β) | T Statistic | p Statistic | |
---|---|---|---|---|
Dependent Variable | Independent Variable | |||
Usage Behavior | ||||
Gender | −0.033 | −1.007 | 0.315 | |
Age | 0.024 | 0.715 | 0.475 | |
ID | 0.317 | 6.617 *** | 0.000 | |
MM | 0.525 | 10.819 *** | 0.000 | |
ID × MM | 0.079 | 2.305 * | 0.022 | |
Usage Behavior | ||||
Gender | −0.050 | −1.588 | 0.113 | |
Age | −0.018 | −0.562 | 0.575 | |
BI | 0.430 | 8.861 *** | 0.000 | |
MM | 0.435 | 8.846 *** | 0.000 | |
BI × MM | 0.080 | 2.436 * | 0.015 |
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Lin, W.; Jiang, P. Factors Influencing College Students’ Generative Artificial Intelligence Usage Behavior in Mathematics Learning: A Case from China. Behav. Sci. 2025, 15, 295. https://doi.org/10.3390/bs15030295
Lin W, Jiang P. Factors Influencing College Students’ Generative Artificial Intelligence Usage Behavior in Mathematics Learning: A Case from China. Behavioral Sciences. 2025; 15(3):295. https://doi.org/10.3390/bs15030295
Chicago/Turabian StyleLin, Wenqian, and Peijie Jiang. 2025. "Factors Influencing College Students’ Generative Artificial Intelligence Usage Behavior in Mathematics Learning: A Case from China" Behavioral Sciences 15, no. 3: 295. https://doi.org/10.3390/bs15030295
APA StyleLin, W., & Jiang, P. (2025). Factors Influencing College Students’ Generative Artificial Intelligence Usage Behavior in Mathematics Learning: A Case from China. Behavioral Sciences, 15(3), 295. https://doi.org/10.3390/bs15030295