Importance of University Students’ Perception of Adoption and Training in Artificial Intelligence Tools
<p>Structural model. Note: own elaboration using Stata (18) [<a href="#B33-societies-14-00141" class="html-bibr">33</a>].</p> "> Figure 2
<p>Loadings comparison across groups (study). (1) Architecture, art, and design. (2) Health sciences. (3) Business, social sciences, humanities, and education. (4) Engineering. Note: own elaboration using Stata (18) (Stata Corp., 2023).</p> "> Figure 3
<p>Path coefficients comparison across groups (study). (1) Architecture, art, and design. (2) Health sciences. (3) Business, social sciences, humanities, and education. (4) Engineering. Note: own elaboration using Stata (18) (Stata Corp., 2023).</p> "> Figure 4
<p>Loadings comparison across groups (gender). Note: own elaboration using Stata (18) (Stata Corp., 2023).</p> "> Figure 5
<p>Path coefficients comparison across groups (gender). Note: own elaboration using Stata (18) (Stata Corp., 2023).</p> ">
Abstract
:1. Introduction
2. Theoretical Framework
2.1. The Relevance of Adoption and Training in the Use of AI Tools within Universities
2.2. Perception as a Central Element in the Training Processes for AI Tools
3. Materials and Methods
3.1. Instrument
3.2. Data Analysis
- X7. Training in artificial intelligence topics would give me confidence in using basic artificial intelligence tools if necessary.
- X8. Training in artificial intelligence topics will allow me to evaluate the various existing artificial intelligence tools and algorithms in the discipline or profession.
- X9. Training in artificial intelligence topics would give me the basic knowledge necessary to routinely work with artificial intelligence tools in my discipline or profession.
- (i)
- The impact of the use of AI tools in the profession.
- (ii)
- The understanding of AI tools and professional implications.
- (iii)
- Attitude and perception toward the relevance of training for appropriately using these tools.
- 1.
- Calculate the latent variable scores:
- Sq represents the score for latent variable q.
- Dq represents the number of indicator variables for latent variable q.
- mDq represents the weight for indicator variable D of latent variable q.
- yDq represents the value of indicator variable D of latent variable q.
- 2.
- The inner weights are calculated for the latent variables using the factorial scheme and the path scheme [35].
- 3.
- The scores of the latent variables Sq obtained in the previous step are updated, obtaining new scores.
- 4.
- To update the external weights of the reflective models, we use:
- 5.
- Latent variable scores are estimated using:
- 6.
- Steps 2 through 5 are repeated until a convergence criterion is met.
4. Results
- Impact: This variable has a standardized path coefficient of 0.3114 and a p-value of 0. This indicates that it positively and significantly impacts attitude. A positive standardized coefficient means that as the impact increases, the attitude also increases. The p-value of 0 indicates that this effect is statistically significant.
- Understanding: This variable has a standardized path coefficient of 0.0522 and a p-value of (0.2451). The coefficient is close to zero, suggesting that it has a weak effect on attitude. The p-value in parentheses, greater than 0.05, indicates that this effect is not statistically significant.
- Perception: This variable has a standardized path coefficient of 0.5357 and a p-value of 0. Like impact, it positively and significantly affects attitude. The higher coefficient indicates that perception has a stronger impact on attitude than impact.
- a.
- Latent variable: impact
- -
- x1: The loading coefficients were high across all groups, indicating that the latent variable “impact” was well represented by the measure “x1” in all groups.
- -
- x2: The measure “x2” represented the variable “impact” well in all groups, although it was lower in Group 3.
- b.
- Latent variable: understanding
- -
- x3: Overall, “x3” was a good measure of the variable “understanding”, although its representation was weaker in Group 4.
- -
- x4: The measure “x4” appeared to represent the variable “understanding” in all groups.
- -
- x5: The measure “x5” had a moderate representation of the variable “understanding” in all groups, with a weaker representation in Group 4.
- -
- x6: The measure “x6” appeared less consistent in representing the variable “understanding”, especially in Group 4.
- c.
- Latent variable: attitude
- -
- x10: The measure “x10” well represented the latent variable “attitude” in all groups.
- -
- x11: Similar to “x10”, “x11” well represented the latent variable “attitude” in all groups.
- d.
- Latent variable: perception
- -
- x7: The measure “x7” represented the variable “perception” well in all groups.
- -
- x8: “x8” had a solid representation of the variable “perception”, although it was slightly weaker in Group 4.
- -
- x9: The measure “x9” had a moderate representation of the variable “perception” in all groups.
5. Discussion
- -
- The perception of the impact of AI tools in the profession has a positive and significant effect on students’ attitudes towards adopting and training these tools. This suggests that professionals who perceive that AI will substantially impact their work are more likely to be willing to embrace and learn to use it.
- -
- Understanding AI tools and their professional implications does not significantly affect attitude. This finding suggests that while understanding AI is essential, it alone is not enough to foster a positive attitude toward its adoption.
- -
- The perception of AI tools positively and significantly affects attitude. Similar to perceived impact, this suggests that professionals who have a positive perception of AI are more likely to be willing to adopt and learn to use it.
- -
- The predictor variables explained in the model suggest that training in AI tools, the perception of their impact, and the understanding of their implications are vital factors influencing professionals’ attitudes toward adopting these tools.
- -
- No significant differences were found in the correlations between the study variables based on gender. This implies that the results apply to both men and women.
6. Conclusions
6.1. Theoretical and Practical Implications
6.2. Limitations and Future Lines of Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Measurement Effect | Global | Group_1 | Group_2 | Abs_Diff | Statistic | p-Value |
---|---|---|---|---|---|---|
impact -> x1 | 0.936 | 0.935 | 0.938 | 0.003 | 0.347 | 0.729 |
impact -> x2 | 0.692 | 0.734 | 0.637 | 0.097 | 0.898 | 0.37 |
understanding -> x3 | 0.781 | 0.61 | 0.861 | 0.251 | 1.092 | 0.276 |
understanding -> x4 | 0.918 | 0.967 | 0.918 | 0.049 | 0.29 | 0.772 |
understanding -> x5 | 0.516 | 0.353 | 0.555 | 0.202 | 0.575 | 0.566 |
understanding -> x6 | 0.533 | 0.271 | 0.682 | 0.41 | 0.974 | 0.331 |
attitude -> x10 | 0.92 | 0.917 | 0.929 | 0.012 | 0.415 | 0.679 |
attitude -> x11 | 0.918 | 0.917 | 0.926 | 0.009 | 0.314 | 0.754 |
perception -> x7 | 0.903 | 0.901 | 0.909 | 0.007 | 0.04 | 0.968 |
perception -> x8 | 0.883 | 0.871 | 0.899 | 0.028 | 0.669 | 0.504 |
perception -> x9 | 0.889 | 0.865 | 0.921 | 0.056 | 1.076 | 0.283 |
Structural Effect | Global | Group_1 | Group_2 | Abs_Diff | Statistic | p-Value |
---|---|---|---|---|---|---|
impact -> attitude | 0.311 | 0.284 | 0.347 | 0.063 | 0.374 | 0.709 |
understanding -> attitude | 0.052 | 0.029 | 0.095 | 0.067 | 0.67 | 0.503 |
perception -> attitude | 0.536 | 0.568 | 0.478 | 0.09 | 0.663 | 0.508 |
1 | For more information on these criteria, please refer to Mehmetoglu and Venturini [34]. |
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Category | X | Ítem |
---|---|---|
Impact of using AI tools in the profession | X1 | 1. Artificial Intelligence will play an important role in the teaching and development of my profession. |
X2 | 2. Some job profiles related to my profession will be replaced by artificial intelligence tools during my lifetime. | |
Understanding AI tools and professional implications | X3 | 3. I understand the basic principles of artificial intelligence—how it works and is used. |
X4 | 4. I feel comfortable with the terminology related to artificial intelligence and can discuss the topic with my colleagues and acquaintances. | |
X5 | 5. I understand the limitations of artificial intelligence tools in my discipline or profession. | |
X6 | 6. I understand the ethical implications of using artificial intelligence tools in my discipline or profession. | |
Attitude and perception of the relevance of training for the proper use of these tools | X7 | 7. Training in artificial intelligence topics would give me confidence in using basic artificial intelligence tools if necessary. |
X8 | 8. Training in artificial intelligence topics will allow me to evaluate the various existing artificial intelligence tools and algorithms in the discipline or profession. | |
X9 | 9. Training in artificial intelligence topics would give me the basic knowledge necessary to routinely work with artificial intelligence tools in my discipline or profession. | |
Attitude towards including this knowledge as part of the professional training process | X10 | 10. Have you received any training—class, course, workshop—on using artificial intelligence tools in your profession? |
X11 | 11. Training in artificial intelligence tools will benefit my professional development. | |
Training | X12 | 12. All students and professionals in my discipline should receive training in artificial intelligence tools as part of their professional studies. |
Measure | Value |
---|---|
Absolute GoF | 0.64089 |
Average communality | 0.67572 |
Average Redundancy | 0.51341 |
Average R-squared | 0.60786 |
Number of obs | 238 |
Relative GoF | 0.97249 |
Tolerance | 1.00 × 10−7 |
Reflective of Impact | Reflective of Understanding | Reflective of Attitude | Reflective Perception | |
---|---|---|---|---|
x1 | 0.9356 | |||
x2 | 0.6924 | |||
x3 | 0.7807 | |||
x4 | 0.9184 | |||
x5 | 0.5162 | |||
x6 | 0.533 | |||
x10 | 0.9201 | |||
x11 | 0.9179 | |||
x7 | 0.9027 | |||
x8 | 0.8833 | |||
x9 | 0.8889 |
Impact | Understand~g | Attitude | Perception | |
---|---|---|---|---|
Cronbach | 0.5645 | 0.7225 | 0.816 | 0.8713 |
DG | 0.8043 | 0.7909 | 0.9158 | 0.9209 |
rho_A | 0.7602 | 0.996 | 0.8161 | 0.8744 |
Impact | Understand~g | Attitude | Perception | |
---|---|---|---|---|
impact | 1 | 0.1227 | 0.4014 | 0.3218 |
understand~g | 0.1227 | 1 | 0.1283 | 0.1352 |
attitude | 0.4014 | 0.1283 | 1 | 0.5351 |
perception | 0.3218 | 0.1352 | 0.5351 | 1 |
AVE | 0.6774 | 0.5009 | 0.8446 | 0.7951 |
Variable Attitude | |
---|---|
impact | 0.3114 |
p-value | 0 |
understanding | 0.0522 |
p-value | (0.2451) |
perception | 0.5357 |
p-value | 0 |
r2_a | 0.6028 |
Impact | Understand~g | Attitude | Perception | |
---|---|---|---|---|
impact | 1 | |||
understand~g | 0.3502 | 1 | ||
attitude | 0.6336 | 0.3582 | 1 | |
perception | 0.5673 | 0.3676 | 0.7315 | 1 |
Impact | Under~g | Attit~e | Pe~tion | |
---|---|---|---|---|
x1 | 0.9356 | 0.3564 | 0.6515 | 0.5947 |
x2 | 0.6924 | 0.3187 | ||
x3 | 0.7807 | |||
x4 | 0.3718 | 0.9184 | 0.401 | 0.3989 |
x5 | 0.5162 | |||
x6 | 0.533 | |||
x10 | 0.5955 | 0.3579 | 0.9201 | 0.6684 |
x11 | 0.5689 | 0.3001 | 0.9179 | 0.6762 |
x7 | 0.5131 | 0.3162 | 0.6845 | 0.9027 |
x8 | 0.4849 | 0.3331 | 0.6056 | 0.8833 |
x9 | 0.5181 | 0.3353 | 0.6623 | 0.8889 |
Variable | Attitude |
---|---|
Impact | 1.527 |
Understanding | 1.197 |
Perception | 1.549 |
Measurement Effect | Global | Group_1 | Group_2 | Group_3 | Group_4 | AD_2vs1 | AD_3vs1 | AD_4vs1 | S_2vs1 | S_3vs1 | S_4vs1 | P_2vs1 | P_3vs1 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
impact -> x1 | 0.936 | 0.928 | 0.828 | 0.956 | 0.954 | 0.1 | 0.028 | 0.026 | 2.318 | 0.954 | 0.204 | 0.021 | 0.341 |
impact -> x2 | 0.692 | 0.731 | 0.949 | 0.654 | 0.6 | 0.219 | 0.077 | 0.131 | 1 | 0.706 | 0.98 | 0.319 | 0.481 |
understanding -> x3 | 0.781 | 0.784 | 0.683 | 0.251 | 0.805 | 0.101 | 0.533 | 0.021 | 0.64 | 2.588 | 0.69 | 0.523 | 0.01 |
understanding -> x4 | 0.918 | 0.902 | 0.926 | 0.964 | 0.944 | 0.024 | 0.062 | 0.042 | 0.484 | 0.514 | 0.81 | 0.629 | 0.608 |
understanding -> x5 | 0.516 | 0.469 | 0.805 | 0.395 | 0.611 | 0.336 | 0.074 | 0.142 | 0.477 | 0.151 | 0.138 | 0.634 | 0.88 |
understanding -> x6 | 0.533 | 0.643 | 0.508 | −0.14 | 0.443 | 0.136 | 0.783 | 0.201 | 0.355 | 1.738 | 0.554 | 0.723 | 0.084 |
attitude -> x10 | 0.92 | 0.92 | 0.939 | 0.93 | 0.892 | 0.02 | 0.01 | 0.028 | 0.708 | 0.06 | 1.465 | 0.48 | 0.952 |
attitude -> x11 | 0.918 | 0.917 | 0.951 | 0.935 | 0.888 | 0.033 | 0.017 | 0.029 | 0.368 | 0.183 | 0.011 | 0.713 | 0.855 |
perception -> x7 | 0.903 | 0.915 | 0.822 | 0.902 | 0.846 | 0.093 | 0.014 | 0.07 | 1.639 | 0.678 | 1.605 | 0.103 | 0.498 |
perception -> x8 | 0.883 | 0.907 | 0.97 | 0.531 | 0.872 | 0.062 | 0.376 | 0.036 | 0.905 | 3.101 | 0.854 | 0.366 | 0.002 |
perception -> x9 | 0.889 | 0.885 | 0.668 | 0.886 | 0.915 | 0.217 | 0.001 | 0.029 | 2.158 | 0.528 | 0.497 | 0.032 | 0.598 |
Structural Effect | Global | Group_1 | Group_2 | Group_3 | Group_4 | AD_2vs1 | AD_3vs1 | AD_4vs1 | S_2vs1 | S_3vs1 | S_4vs1 | P_2vs1 | P_3vs1 | P_4vs1 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
impact -> attitude | 0.311 | 0.355 | 0.092 | 0.04 | 0.358 | 0.262 | 0.315 | 0.003 | 0.066 | 1.678 | 0.1 | 0.948 | 0.095 | 0.921 |
understanding -> attitude | 0.052 | 0.036 | −0.06 | 0.22 | 0.068 | 0.097 | 0.184 | 0.032 | 0.34 | 1.177 | 0.289 | 0.734 | 0.24 | 0.772 |
perception -> attitude | 0.536 | 0.527 | 0.928 | 0.537 | 0.337 | 0.401 | 0.009 | 0.191 | 0.691 | 0.305 | 1.218 | 0.491 | 0.761 | 0.225 |
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Vázquez-Parra, J.C.; Henao-Rodríguez, C.; Lis-Gutiérrez, J.P.; Palomino-Gámez, S. Importance of University Students’ Perception of Adoption and Training in Artificial Intelligence Tools. Societies 2024, 14, 141. https://doi.org/10.3390/soc14080141
Vázquez-Parra JC, Henao-Rodríguez C, Lis-Gutiérrez JP, Palomino-Gámez S. Importance of University Students’ Perception of Adoption and Training in Artificial Intelligence Tools. Societies. 2024; 14(8):141. https://doi.org/10.3390/soc14080141
Chicago/Turabian StyleVázquez-Parra, José Carlos, Carolina Henao-Rodríguez, Jenny Paola Lis-Gutiérrez, and Sergio Palomino-Gámez. 2024. "Importance of University Students’ Perception of Adoption and Training in Artificial Intelligence Tools" Societies 14, no. 8: 141. https://doi.org/10.3390/soc14080141