Motivation to learn artificial intelligence applications and mental well-being in university students: A cross-sectional study

Abstract

BACKGROUND:

Artificial intelligence (AI) is increasingly integrated into higher education, while students’ mental well-being has become a critical concern. The relationship between AI learning motivation and mental well-being was investigated.

MATERIALS AND METHODS:

A cross-sectional correlational study was conducted among 195 students at the Faculty of Education, Najran University, Saudi Arabia, during the first semester of 2025–2026. Data were collected using the motivation to learn AI applications scale and a researchers-developed mental well-being scale (Cronbach’s α = 0.792). Data were analyzed using Statistical Package for the Social Sciences version 26 with Pearson correlation, independent samples t-test, and linear regression (P ≤ 0.050).

RESULTS:

Motivation to learn AI applications showed a significant positive correlation with mental well-being (r = 0.458, P < 0.001). Students with higher motivation reported significantly higher mental well-being (P < 0.001). Female students showed higher motivation (P = 0.007), while no significant gender differences were found in mental well-being (P = 0.421). Motivation significantly predicted mental well-being (β = 0.233, R² = 0.184, P < 0.001).

CONCLUSION:

Motivation to learn AI applications was positively associated with students’ mental well-being and explained a modest proportion of its variance.

Introduction

Artificial intelligence (AI) has rapidly evolved into a transformative force across multiple sectors, with higher education emerging as one of its most dynamic domains of application. Universities are increasingly integrating AI-driven technologies into teaching and learning processes to enhance instructional quality and student engagement. Students routinely use AI applications to search for information, complete assignments, and support independent learning, thereby reshaping their academic experiences. As a result, AI is no longer perceived merely as a technological innovation, but rather as a fundamental component of contemporary educational systems. The widespread adoption of AI in education has been further accelerated by the growing need for flexible, adaptive, and technology-enhanced learning environments that respond to diverse student needs.[1]

AI technologies have been implemented in various educational contexts to support interactive and student-centered learning experiences. These technologies enable adaptive learning pathways, personalized feedback, and opportunities for critical thinking and problem-solving, which enhance the overall effectiveness of the educational process.[2,3,4,5] In addition, AI tools assist instructors in designing engaging learning tasks that promote student participation and active learning.[6,7] Consequently, higher education institutions have increasingly emphasized the importance of developing students’ competencies in AI applications to meet the demands of the digital era.[8] Recent studies have also highlighted the role of AI-supported educational environments in shaping students’ learning experiences and educational satisfaction in higher education settings.[9] This transformation has led to a growing recognition that students’ readiness to engage with AI technologies depends not only on access to these tools but also on their motivation to learn and use them effectively.[8]

Motivation plays a central role in shaping students’ engagement with technology-enhanced learning environments. Students with higher levels of motivation are more likely to participate actively in academic tasks, persist in the face of challenges, and achieve better learning outcomes.[10] Theoretical perspectives, such as self-determination theory and expectancy–value theory, provide a comprehensive explanation of the mechanisms underlying students’ motivation. These theories emphasize the importance of intrinsic factors such as curiosity and personal interest, as well as extrinsic factors, including academic rewards and future career opportunities.[11,12,13] In the context of AI learning, motivation is influenced by students’ expectations regarding the usefulness of AI applications and the value they assign to acquiring these skills.[8] Empirical studies have demonstrated that AI-supported learning environments can enhance students’ engagement and sustain their interest in learning activities.[13] Furthermore, interaction with AI tools has been associated with increased academic confidence and more positive attitudes toward learning.[13,14]

Mental well-being is a crucial determinant of students’ academic success and overall quality of life. It reflects an individual’s ability to maintain psychological balance, manage stress, think clearly, and sustain positive social relationships.[15] Students with higher levels of mental well-being tend to demonstrate greater resilience, improved academic adjustment, and higher life satisfaction.[15] Nevertheless, university students often encounter multiple challenges, including academic pressure, technological demands, and uncertainty about future careers, which may negatively affect their psychological well-being.[16,17,18] In recent years, research has increasingly explored the role of digital technologies, including AI, in shaping students’ psychological experiences. Some studies suggest that AI-based learning environments can enhance students’ engagement, self-regulation, and perceived competence, which may contribute positively to their mental well-being.[19,20] AI tools, such as chatbots and intelligent learning systems, provide personalized feedback and structured learning experiences, which may help students manage academic challenges more effectively.[20] At the same time, the integration of AI in education may introduce new cognitive and emotional demands, highlighting the importance of examining both its benefits and potential challenges. Despite the growing interest in AI in higher education, research examining the relationship between students’ motivation to learn AI applications and their mental well-being remains limited, particularly within Arab university contexts where AI integration is still developing.[8] Many universities in the region continue to adopt relatively traditional approaches to teaching, and empirical evidence on the psychological implications of AI learning remains scarce. Therefore, there is a clear need for research that explores how motivational factors associated with AI learning relate to students’ mental well-being within these emerging educational contexts. Accordingly, the present study was conducted to examine the relationship between motivation to learn AI applications and mental well-being among university students, to identify gender differences in these variables, and to determine the extent to which motivation to learn AI applications predicts students’ mental well-being.

Materials and Methods

Study design and setting

The current study adopted a cross-sectional correlational design to examine the relationship between motivation to learn AI applications and mental well-being among university students. This design allows the identification of associations between variables without manipulation and does not imply causality. Accordingly, findings from the present study should be interpreted as correlational rather than causal.

Study participants and sampling

The study was conducted at the Faculty of Education, Najran University, Saudi Arabia, during the first semester of the 2025–2026 academic year. The study population consisted of 1,481 students enrolled in the faculty.

The research was implemented in two phases. In the pilot phase, the instruments were administered to 140 students (excluded from the main sample) to verify the psychometric properties. In the main phase, a simple random sample of 195 male and female students was selected to ensure representativeness across academic levels and specializations.

Sample size

The sample size was determined using the following formula for correlational studies:

n = Z² × p (1 − p)/d²

where Z represents the standard normal deviation, p the estimated proportion, and d the margin of error. The selected sample (n = 195) was considered adequate for statistical analysis.

Data collection tools and techniques

Demographic questionnaire

A demographic questionnaire was developed to collect participants’ information, including gender, academic level, and specialization.

Motivation to learn AI applications scale

This scale was originally developed by Yurt and Kasari and adapted into Arabic by.[8] It consists of 20 items across five dimensions: achievement expectancy, utility value, achievement value, intrinsic value, and cost. Responses are rated on a five-point Likert scale (1–5), with higher scores indicating higher motivation.

The psychometric properties of the scale were examined using item–total correlations, confirmatory factor analysis, and internal consistency measures. Item–total correlations were all statistically significant and ranged from 0.213 to 0.712, indicating acceptable internal consistency.

Correlations between items and their respective dimensions were also statistically significant, supporting the internal structure of the scale. The results of confirmatory factor analysis showed that all factor loadings were statistically significant at the 0.05 level.

Model fit indices indicated an acceptable fit to the data (Chi-square divided by degrees of freedom [CMIN/DF] = 3.71, Goodness-of-Fit Index [GFI] = 0.73, Normed Fit Index [NFI] = 0.85, Incremental Fit Index [IFI] = 0.89, Comparative Fit Index [CFI] = 0.89, and Root Mean Square Error of Approximation [RMSEA] = 0.041), suggesting that the proposed factor structure is reasonably supported.

Reliability analysis showed that Cronbach’s alpha coefficients ranged from 0.654 to 0.711 across the dimensions, with an overall reliability of 0.768, indicating acceptable to moderate levels of internal consistency.

Detailed psychometric results are provided in the Supplementary Materials [Tables S1–S5].

Table S1.

Item-total correlations for Motivation Scale

Singular		Correlation coefficient		Singular		Correlation coefficient		Singular		Correlation coefficient		Singular		Correlation coefficient
1		0.564**		6		0.213*		11		0.447**		16		0.432**
2		0.523**		7		0.511**		12		0.498**		17		0.476**
3		0.712**		8		0.598**		13		0.534**		18		0.268*
4		0.675**		9		0.710**		14		0.694**		19		0.442**
5		0.548**		10		0.564**		15		0.581**		20		0.453**

**Significance level of 0.01, *Significance level of 0.05

Table S5.

Reliability coefficients (Motivation Scale)

M		Dimensions		Alpha coefficient
1		Achievement Expectations		0.654
2		Value of Benefit		0.711
3		Achievement Value		0.702
4		Intrinsic Value/Interest		0.698
5		Cost		0.691
		Scale as a whole		0.768

Table S2.

Item-total correlations by dimension

Achievement Expectations				Value of Benefit				Achievement Value				Intrinsic Value/Interest				Cost
Singular		Correlation coefficient		Singular		Correlation coefficient		Singular		Correlation coefficient		Singular		Correlation coefficient		Singular		Correlation coefficient
1		0.576**		5		0.559**		9		0.748**		13		0.548**		17		0.487**
2		0.543**		6		0.256*		10		0.578**		14		0.697**		18		0.288*
3		0.718**		7		0.548**		11		0.449**		15		0.588**		19		0.448**
4		0.701**		8		0.603**		12		0.507**		16		0.448**		20		0.455**

**Significance level of 0.01, *Significance level of 0.05

Table S3.

Factor loadings (Motivation Scale)

Dimension		Singular		Standard Downward Weight		Downhill weight		Measurement error		Critical Percentage		Significance Level
Value of Benefit		1		0.87		1.86		0.23		7.12		0.05
		2		0.90		1.08		0.18		6.56		0.05
		3		0.92		1.04		0.16		6.24		0.05
		4		0.80		1.98		0.29		7.63		0.05
Achievement Expectations		5		0.90		1.17		0.17		7.41		0.05
		6		0.86		1.09		0.26		7.73		0.05
		7		0.84		1.14		0.27		7.85		0.05
		8		0.89		1.22		0.21		7.52		0.05
Achievement Value		9		0.89		1.97		0.22		7.29		0.05
		10		0.93		1.12		0.14		6.66		0.05
		11		0.93		1.48		0.13		6.41		0.05
		12		0.93		1.27		0.14		6.65		0.05
Intrinsic Value/Interest		13		0.80		1.16		0.26		7.83		0.05
		14		0.89		1.13		0.20		7.28		0.05
		15		0.90		1.90		0.16		7.06		0.05
		16		0.91		1.19		0.16		6.83		0.05
Cost		17		0.88		1.02		0.22		7.17		0.05
		18		0.83		1.11		0.21		7.58		0.05
		19		0.95		1.45		0.08		4.91		0.05
		20		0.89		1.32		0.15		7.02		0.05

Table S4.

Model fit indices

Pointer		Value		Ideal Range
Chi-square (CMIN)		593.62
Significance Level		0.05
DF		160
CMIN/DF		3.71		Less than 5
Goodness of Fit Index (GFI)		0.73		From (zero) to (1): A high value (which is close to or equal to 1 true) indicates a better match for the model.
Normed fit index (NFI)		0.85		From (zero) to (1): A high value (which is close to or equal to 1 true) indicates a better match for the model.
Incremental Fit Index (IFI)		0.89		From (zero) to (1): A high value (which is close to or equal to 1 true) indicates a better match for the model.
Comparative Fit Index (CFI)		0.89		From (zero) to (1): A high value (which is close to or equal to 1 true) indicates a better match for the model.
RMSEA		0.041		RMSEA < 0.08

Mental well-being scale

The mental well-being scale was developed by the research team and consists of 21 items measuring seven dimensions: mental clarity, cognitive flexibility, control of thought and attention, metacognitive self-awareness, problem-solving and decision-making, mental stability, and creative thinking.

This scale was designed to capture context-specific aspects of students’ psychological functioning within AI-supported learning environments. Although standardized instruments, such as the Warwick–Edinburgh mental well-being scale, are widely used, they mainly assess general emotional and psychological well-being. In contrast, the present study focuses on additional dimensions more directly related to academic and cognitive functioning in technology-rich contexts such as cognitive flexibility, metacognitive awareness, problem-solving, and mental clarity, which are particularly relevant for students engaging with AI applications in higher education. Therefore, a researcher-developed instrument was considered more appropriate for capturing these context-specific aspects. However, this choice is acknowledged as a limitation, and future studies are encouraged to use standardized instruments alongside context-specific measures for validation and comparison purposes.

Responses are rated on a five-point Likert scale, with higher scores indicating better mental well-being. Content validity was established through expert review, and construct validity was examined using exploratory factor analysis (Kaiser-Meyer-Olkin (KMO) = 0.696). The KMO value indicates a marginally acceptable level of sampling adequacy. Although values above 0.60 are generally considered acceptable for factor analysis,[21] the present value should still be interpreted with caution.

Psychometric properties were assessed using item–total correlations, factor analysis, and internal consistency measures. Item–total correlations ranged from 0.149 to 0.671 and were statistically significant; however, some correlations were relatively low, indicating variability in how certain items represent the construct. Correlations between items and their respective dimensions were generally significant, providing partial support for the internal structure of the scale.

Exploratory factor analysis suggested a multidimensional structure consistent with the proposed dimensions. However, some factor loadings were relatively weak or borderline, indicating that the structural validity of the instrument remains preliminary and should be interpreted cautiously. Reliability analysis showed that Cronbach’s alpha coefficients ranged from 0.711 to 0.791 across the dimensions, with an overall reliability of 0.792, indicating acceptable but moderate internal consistency.

Detailed psychometric results are provided in the Supplementary Materials [Tables S6–S9].

Table S6.

Item-total correlations (Mental Well-Being Scale)

Singular		Correlation coefficient		Singular		Correlation coefficient		Singular		Correlation coefficient
1		0.155*		8		0.633**		15		0.352**
2		0.446**		9		0.671**		16		0.365**
3		0.375**		10		0.525**		17		0.543**
4		0.158*		11		0.556**		18		0.149*
5		0.534**		12		0.519**		19		0.459*
6		0.214*		13		0.654**		20		0.532**
7		0.373**		14		0.612**		21		0.213*

Table S9.

Reliability coefficients (Mental Well-Being Scale)

Cronbach’s alpha coefficient		Dimensions
0,765		Mental clarity
0,789		Cognitive flexibility
0,734		Control of thinking and attention
0,711		Cognitive Self-Awareness
0,791		Problem Solving and Decision Making
0,776		Mental stability
0,746		Creative Thinking
0,792		Total score of the questionnaire

Table S7.

Item-total correlations by dimension

Mental clarity				Cognitive flexibility				Control of thinking and attention				Cognitive Self-Awareness
Singular		Correlation coefficient		Singular		Correlation coefficient		Singular		Correlation coefficient		Singular		Correlation coefficient
1		0.243*		4		0.205*		7		0.425**		10		0.531**
2		0.401**		5		0.576**		8		0.656**		11		0.586**
3		0.412**		6		0.225*		9		0.701**		12		0.596**
Problem Solving and Decision Making				Mental stability				Creative Thinking
Singular		Correlation coefficient		Singular		Correlation coefficient		Singular		Correlation coefficient
13		0.665**		16		0.432**		19		0.525**
14		0.652**		17		0.556**		20		0.556**
15		0.378**		18		0.169*		21		0.225*

Table S8.

Factor matrix (EFA)

Factors Phrase numbers		K1		K2		K3		K.4		v5		K6		v7
1		0.872
2		0.719
3		0.577
4				0.786
5				0.653
6				0.572
7						0.493
8						0.416
9						0.655
10								0.869
11								0.698
12								0.698
13										0,437
14										0,357
15										0,332
16												0,451
17												0,318
18												0,324
19														0,334
20														0,321
21														0,352
Latent root		5.390		3,231		3,892		2.346		2.211		1.160		1.432
Contrast %		15.128		9.156		10.431		7.553		7.211		4.988		6.431

Ethical considerations

This study followed the ethical principles of the Declaration of Helsinki for research involving human participants. Participation in the study was voluntary, and students were informed about the purpose of the research before completing the study scales. Confidentiality of the participants’ responses was assured, and the collected data were used only for research purposes. The study was approved by the Research Ethics Committee at King Faisal University, Approval No: KFU253998.

Statistical and data analysis

Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 26.

Descriptive statistics were used (to summarize data). Pearson correlation was applied (to examine relationships), independent samples t-test (to compare groups), and linear regression (to assess prediction).

The level of significance was set at P ≤ 0.050. Assumptions of normality, homogeneity, and linearity were verified.

Study Results and Discussion

First hypothesis

Result

The first hypothesis states that “there is a statistically significant correlation between the average scores of students in motivation for learning AI and their scores on the Mental Well-Being Scale” and to verify the validity of the hypothesis, the Pearson correlation coefficient was used for the parametric samples through the statistical program SPSS and Table 1 shows the matrix of correlation coefficients between the scores of the motivation scale for AI learning and the scores of the mental well-being scale.

Table 1.

The matrix of correlation coefficients between students’ scores on the motivation scale for learning AI and their scores on the mental well-being scale (n=195)

Dimensions		Mental clarity		Cognitive flexibility		Control of thinking and attention		Cognitive self-awareness		Problem-solving and decision-making		Mental stability		Creative thinking		Overall score of the mental well-being scale
Predict achievement		0.322**		0.249**		0.189**		0.174*		0.254**		0.229**		0.320**		0.393**
Value of benefit		0.381**		0.257**		0.213**		0.255**		0.211**		0.320**		0.227**		0.412**
Achievement value		0.289**		0.243**		0.183*		0.269**		0.177*		0.257**		0.284**		0.343**
Intrinsic value of interest		0.258**		0.242**		0.270**		0.321**		0.341**		0.230**		0.323**		0.253**
Cost		0.242**		0.226**		0.155*		0.245**		0.267**		0.228**		0.430**		0.597**
The overall score of the motivation scale		0.393**		0.235**		0.320**		0.325**		0.366**		0.388**		0.398**		0.458**

AI=Artificial intelligence

There is a positive and statistically significant correlation between students’ scores on the dimensions of the motivation to learn AI scale and their scores on the dimensions of the mental well-being scale, as well as the total scores of both scales, at the significance level of 0.01. In addition, the relationship between control of thought and attention in the well-being scale and the dimensions of cost and achievement value, as well as the relationship between problem solving and decision-making and achievement value, was significant at the 0.05 level. These findings suggest that students who report higher motivation to learn AI also tend to report higher levels of mental well-being. A significant positive association was also found between students’ motivation to learn AI applications and their mental well-being (r = 0.458, P < 0.01).

Discussion

Given the preliminary psychometric nature of the researchers-developed mental well-being scale, the present findings should be interpreted with caution. Although the scale demonstrated acceptable internal consistency and preliminary evidence of validity, some psychometric indicators suggest that the instrument remains exploratory and context specific.

This result is generally in line with previous studies such as,[20] which indicated that motivation to learn AI may be associated with students’ mental well-being. Similar patterns were also reported by,[17] who noted that AI-supported learning environments can be related to both academic engagement and well-being. In addition, the study[18] found that the use of AI technologies in educational contexts was positively associated with academic performance and students’ mental well-being.

An unexpected finding in this study is the positive relationship between the cost dimension and mental well-being. From a theoretical perspective, perceived cost is often expected to be negatively associated with positive outcomes. Therefore, this finding should be interpreted with caution. One possible explanation is that some students may perceive the effort, time, and cognitive demands associated with learning AI applications as part of meaningful academic engagement and future professional development. However, this interpretation remains tentative and may also reflect the characteristics of the measurement instrument or the operationalization of the cost variable within the present context. Further research is needed to examine this relationship more rigorously.

Second hypothesis

Result

The second hypothesis states that “there are differences between high and low motivation students to learn AI in their mental well-being in the direction of high motivation” and to verify the validity of the hypothesis, the researcher calculated the value of “t” to indicate the differences between the averages, and Table 2 shows the averages and standard deviations and the value of “t” for the scores of students with high and low motivation to learn AI in the dimensions of mental well-being and the overall score.

Table 2.

Averages, standard deviations, and “T” value for the scores of students with high and low motivation learning AI in the dimensions of mental well-being and total score

Acceptance		High motivation to learn AI n=114				Low motivation to learn AI n=81				Value T		Significance level
Dimensions of mental well-being		M1		K1		M2		K2
Mental clarity		11.56		1.24		8.67		0.84		17.52		Significant
Cognitive flexibility		12.59		0.87		9.67		1.11		19.66		Significant
Control of thinking and attention		12.37		1.02		9.83		1.19		15.87		Significant
Cognitive self-awareness		13.47		0.83		10.70		1.40		17.19		Significant
Problem-solving and decision-making		12.70		1.08		9.67		0.97		20.06		Significant
Mental stability		12.96		0.85		10.28		1.12		18.98		Significant
Creative thinking		11.82		0.91		9.88		0.93		14.42		Significant
Total grade		87.50		6.49		68.83		7.25		18.83		Significant

AI=Artificial intelligence

Table 2 shows that students with higher motivation to learn AI applications tend to report higher levels of mental well-being.

Discussion

One possible explanation for this finding is that highly motivated students may be more actively engaged in learning activities, show greater persistence in dealing with academic challenges, and experience higher confidence in adapting to emerging technologies. Such engagement may contribute positively to students’ psychological adjustment and overall well-being within university environments.

This pattern is generally consistent with previous studies. For example,[17] noted that higher self-motivation is often associated with greater concentration and persistence in learning, which may be associated with students’ psychological well-being and their ability to cope with academic challenges. Similar observations were reported by,[22] who suggested that motivation to engage with AI-related learning can be related to both academic engagement and students’ overall well-being. In addition,[18] reported that the use of AI technologies in educational contexts was positively associated with academic performance and mental well-being among university students.

From a theoretical perspective, these findings may be understood in light of self-determination theory, which suggests that students tend to show higher motivation when they feel capable of learning new technologies and experience greater autonomy in learning. Previous literature also indicates that higher motivation is often associated with academic engagement and psychological adjustment, which may help explain the observed relationship with mental well-being.

Third hypothesis

Result

The third hypothesis states that “there are no differences between students’ motivation to learn AI and their mental well-being attributable to the gender variable” and to verify the validity of the hypothesis, the researcher calculated the value of “t” for the significance of the differences between the averages, and Table 3 shows the averages, standard deviations, and the value of “t” for the total score of students on the scale of motivation to learn AI and the total score of mental well-being.

Table 3.

Averages, standard deviations, and the value of “T” for the total score of students on the motivation scale for learning, AI, and the total score of mental well-being

Acceptance		Female students n=117				Students n=78				Value T		Significance level
		M1		K1		M2		K2
Motivational AI learning		82.88		11.04		78.05		11.91		2.69		Significant
Mental well-being		66.26		10.61		65.63		11.04		1.20		Not significant

AI=Artificial intelligence

Table 3 shows that there are statistically significant differences between male and female students in their motivation to learn AI applications, in favor of female students. However, no statistically significant differences were found in mental well-being according to gender.

Discussion

One possible explanation for the higher motivation among female students is their strong interest in academic achievement and their willingness to explore new learning areas that may support their academic and professional development. In many cases, students’ interest in learning new technologies may also be driven by curiosity and a desire to gain knowledge rather than only by academic grades.

This finding is generally consistent with the study of,[23] which reported that female students often use AI tools, including chatbots, for various academic tasks and may show greater engagement with these technologies. However, the result differs from the findings of[24] who reported that female students showed lower self-efficacy and self-motivation when using generative AI tools, along with higher perceptions of potential challenges.

The higher motivation observed among female students may reflect greater academic engagement and interest in emerging learning technologies. Supportive educational environments and increasing opportunities for women in technology-related fields may also contribute to this pattern.

Regarding mental well-being, the results of this study indicate that male and female students report similar levels of well-being. This may be explained by the fact that university students often share similar academic environments and experiences, including academic demands and social interactions. They may also receive comparable support from family and peers, which can contribute to maintaining their psychological balance. Mental well-being is influenced by several personal and contextual factors, such as coping strategies, social relationships, and emotional regulation, which may affect students regardless of gender. Therefore, the absence of gender differences in mental well-being may reflect the stronger influence of these broader factors rather than gender alone.

Fourth hypothesis

Result

The fourth hypothesis states that “Motivation for learning AI contributes to predicting students’ mental well-being” and to verify the validity of this hypothesis, the researchers used regression analysis tests for the two variables of motivation for learning AI and mental well-being through the SPSS program, and the results shown in Table 4.

Table 4.

Regression equation for variables (motivation for learning AI and mental well-being)

Variable		B		Std. error		Beta		T		Sig.
Constant		77.81		3.279		0.000		23.73		0.000
Motivation for AI learning		0.178		0.045		0.233		3.94		0.000

AI=Artificial intelligence

Table 4 shows that the value of “t” for the regression coefficients is statistically significant at the 0.01 level. This result indicates that students’ scores on the motivation to learn AI scale are significantly associated with their scores on the mental well-being scale. In other words, higher motivation to learn AI applications tends to be accompanied by higher levels of mental well-being among Najran University students.

The regression equation can be expressed as follows:

Mental well-being = 77.81 + (0.178 × motivation to learn AI)

Table 5 shows that the level of statistical significance is less than 0.05, indicating that the relationship between students’ motivation to learn AI and their mental well-being is statistically significant. The multiple correlation coefficient reached (0.429), while the coefficient of determination (R²) was (0.184). This indicates that motivation to learn AI was positively associated with mental well-being among the students in this sample, while the remaining variance may be related to other factors that were not included in the regression model.

Table 5.

Results of variance analysis of the motivation variable for AI learning on mental well-being

Variable		Source of contrast		Total squares		Degree of freedom		Average squares		Value (P)		Significance		Correlation coefficient (R)		Explained variation R2
Motivation for AI learning		Regression		1663.65		1		1663.6		15.58		0.000		0.429		0.184
		Leftovers		28927.8		193		106.74
		Total		30591.4		194

AI=Artificial intelligence

Discussion

The results support the fourth hypothesis, indicating that motivation to learn AI is associated with students’ scores on the mental well-being scale. In general, students who report higher motivation to learn AI applications also tend to report higher levels of mental well-being. This pattern may be related to factors such as academic engagement, self-efficacy, and the ability to adapt to academic challenges.

One possible interpretation is that students with higher motivation to learn AI applications may also report greater engagement in learning activities and greater confidence in developing new skills. Participation in AI-related learning activities may be linked to opportunities for problem-solving, collaboration with peers, and exploration of new technologies. These experiences may be associated with students’ sense of competence and their overall psychological adjustment in the academic environment.

These findings are generally consistent with previous studies such as,[13] which reported that engagement with AI-related learning activities may be associated with students’ academic motivation, self-confidence, and positive attitudes toward learning. Such factors are often related to students’ ability to cope with academic demands and maintain better levels of psychological well-being.

From an educational perspective, these findings may indicate that fostering students’ motivation toward AI learning is not only relevant for technological competence but may also be associated with broader psychological adjustment in university settings.

Recommendations and suggestions

• Promote active learning of AI applications to raise student motivation.

• Designing programs and training courses that encourage students to learn AI applications practically and continuously, as higher motivation to learn AI may be associated with better mental well-being.

• Integrating AI into student activities: It is recommended to introduce group projects and applied competitions in the field of AI.

• Conducting prospective studies to explore the factors influencing the relationship between motivation for AI learning and mental well-being.

Limitations

This study has several limitations that should be considered when interpreting the results. First, the study used a cross-sectional design, which means that the findings describe relationships between variables but cannot establish causal conclusions. Second, the data were collected using self-report instruments, which may be influenced by students’ personal perceptions and responses. Third, the study was conducted in a single university context, which may limit the generalizability of the findings to other universities or educational settings. Future studies may benefit from using larger and more diverse samples and applying longitudinal designs to provide a deeper understanding of the relationship between motivation to learn AI and students’ mental well-being. In addition, although the mental well-being scale demonstrated acceptable internal consistency, some psychometric indicators—including the borderline KMO value, relatively low item–total correlations for some items, and weak factor loadings—suggest that the structural robustness of the instrument remains preliminary. Therefore, findings involving this measure should be interpreted with caution. Future research is encouraged to further validate the instrument using larger and more diverse samples and to compare it with standardized mental well-being measures.

In addition, the statistical analyses primarily relied on Pearson correlation and simple linear regression to examine the direct association between motivation to learn AI applications and mental well-being. Although demographic variables, such as gender, academic level, and specialization, were examined descriptively, they were not included as control variables in an adjusted regression model. Therefore, the possibility of omitted variable bias cannot be fully excluded. Future research is encouraged to employ adjusted regression models controlling for demographic and contextual variables to provide a more comprehensive understanding of the relationships under investigation.

Conclusion

Motivation to learn AI applications was positively associated with mental well-being among university students and explained a modest proportion of its variance. Female students reported higher motivation, while no significant gender differences were observed in mental well-being. However, these findings should be interpreted with caution, given the preliminary psychometric nature of the researcher-developed mental well-being scale.

Universities may benefit from integrating AI learning within supportive educational environments that align with student well-being frameworks. Policies may promote balanced academic workloads, digital literacy, and psychological support services. Embedding AI training within curricula, while ensuring accessible guidance and counseling, may be associated with improved student engagement and well-being. Institutional strategies may also emphasize safe technology use, stress management, and inclusive learning environments consistent with health promotion benchmarks in higher education.

It is recommended to enhance students’ motivation to learn AI through structured training programs, interactive learning activities, and curriculum integration. Future research should employ longitudinal designs, include diverse samples, and examine additional psychological and contextual variables influencing students’ mental well-being.

AI use disclosure

The authors used AI tools (ChatGPT and Grammarly) only for language editing and paraphrasing to improve the clarity and grammar of the manuscript, as the authors are not native English speakers. No AI tools were used to generate research data, results, analysis, or interpretations. All scientific content and conclusions are the responsibility of the authors.

Ethical approval

This study was conducted in accordance with the principles of the Declaration of Helsinki. Participation was voluntary, participants' responses were kept confidential, and the data were used only for research purposes. Ethical approval was obtained from the Research Ethics Committee at King Faisal University (Approval No. KFU253998).

Author’s contributions

AAO, HSA, WMI and AFI contributed to the conceptualization and methodology of the study. AAO and HSA were responsible for data collection and investigation. AFI and WMI conducted the formal analysis and interpreted the data. AAO and WMI prepared the original draft of the manuscript. AFI and HSA reviewed and edited the manuscript. AFI supervised the study and managed the project. All authors read and approved the final version of the manuscript.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (Proposal Number: KFU253998).