Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence?
Abstract
Psychologists with their expertise in statistics and regarding human perception and behavior can contribute valuable insights to the development of innovative and useful artificial intelligence (AI) systems. Therefore, we need to raise attention and curiosity for AI and foster the willingness to engage with it among psychology students. This requires identifying approaches to integrate a general understanding of AI technology into formal psychological training and education. This study investigated to what extent psychology students currently accept and use AI and what affects their perception and usage. Therefore, an AI acceptance model based on established technology acceptance models was developed and tested in a sample of 218 psychology students. An acceptable fit with the data was found for an adapted version. Perceived usefulness and ease of use were most predictive for the students’ attitude towards AI; attitude itself, as well as perceived usefulness, social norm, and perceived knowledge, were predictors for the intention to use AI. In summary, we identified relevant factors for designing AI training approaches in psychology curricula. In this way, possible restraints regarding the use of AI can be reduced and its beneficial opportunities exploited in psychological contexts.
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Prior studies (Gado et al., 2022; Hu, 2022) found that perceived usefulness of AI had a significant relationship with students' intention to use AI.
“Similarly, Gado et al. (2022) and Hu (2022) found that perceived usefulness of AI had a significant relationship with students' intention to use AI.”
From An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI (2023) · cites this paper as Gado et al. (2022)
The results of that study suggest a link between perceived social norms, perceived ease of use, perceived usefulness, and perceived knowledge with students' intention to use AI-enabled tools.
“The results suggest a link between the perceived social norms, perceived ease of use, perceived usefulness, and perceived knowledge with students’ intention to use AI-enabled tools.”
From Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study (2023) · cites this paper as [17]
Only 1 study has examined the predictors of the intention to use AI-enabled tools in mental health care.
“However, only 1 study has examined the predictors of the intention to use AI-enabled tools in mental health care [17].”
From Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study (2023) · cites this paper as [17]
Several studies have demonstrated the applicability of the UTAUT in investigating the implementation of AI-CDSSs.
“Several studies have already demonstrated the applicability of the UTAUT in investigating the implementation of AI-CDSSs [12-17].”
From Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study (2023) · cites this paper as [17]
By differentiating between specific tool understanding and more general cognitive technology readiness, this study moves beyond previous research that focused on the role of general AI knowledge in predicting general use intention.
“By differentiating between specific tool understanding and more general cognitive technology readiness, this study moves beyond previous research that focused on the role of general AI knowledge in predicting general use intention [17].”
From Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study (2023) · cites this paper as [17]
Gado et al found support for the direct effects of perceived usefulness, AI knowledge, and perceived social norms on the intention to use AI as well as indirect effects of perceived ease of use on use intention via positive attitudes toward AI in a sample of psychology students.
“Gado et al [17] found support for the direct effects of perceived usefulness, AI knowledge, and perceived social norms on the intention to use AI as well as indirect effects of perceived ease of use on use intention via positive attitudes toward AI in a sample of psychology students.”
From Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study (2023) · cites this paper as [17]
Cites
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788 · see citing works
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587 · see citing works
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194 · see citing works
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · see citing works
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033 · see citing works
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035 · see citing works
Principles and Practice of Structural Equation Modeling
Tabri, N., & Elliott, C. M. (2012) · Canadian Graduate Journal of Sociology and Criminology · doi:10.15353/cgjsc.v1i1.3787 · Cited by 39,065 · see citing works
<b>lavaan</b> : An <i>R</i> Package for Structural Equation Modeling
Rosseel, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i02 · Cited by 27,955 · see citing works
(1980) · Medical Entomology and Zoology · Cited by 25,810 · see citing works
User Acceptance of Computer Technology: A Comparison of Two Theoretical Models
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989) · Management Science · doi:10.1287/mnsc.35.8.982 · Cited by 25,709 · see citing works
A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066 · see citing works
Structural Equation Modeling: Concepts, Issues, and Applications.
Smith, M. D., & Hoyle, R. H. (1996) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2988418 · Cited by 6,868 · see citing works
(1985) · Cited by 6,185 · see citing works
The theory of planned behaviour: Reactions and reflections
Ajzen, I. (2011) · Psychology and Health · doi:10.1080/08870446.2011.613995 · Cited by 4,846 · see citing works
Bamberg, S., & Möser, G. (2007) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2006.12.002 · Cited by 4,140 · see citing works
The Explanatory and Predictive Scope of Self-Efficacy Theory
Bandura, A. (1986) · Journal of Social and Clinical Psychology · doi:10.1521/jscp.1986.4.3.359 · Cited by 3,771 · see citing works
Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009) · International Journal of Social Robotics · doi:10.1007/s12369-008-0001-3 · Cited by 3,193 · see citing works
A meta-analysis of the technology acceptance model
King, W. R., & He, J. (2006) · Information & Management · doi:10.1016/j.im.2006.05.003 · Cited by 2,906 · see citing works
Assessing IT Usage: The Role of Prior Experience
Taylor, S., & Todd, P. (1995) · MIS Quarterly · doi:10.2307/249633 · Cited by 2,891 · see citing works
Predicting and Changing Behavior
Fishbein, M., & Ajzen, I. (2011) · doi:10.4324/9780203838020 · Cited by 2,807 · see citing works
The Technology Acceptance Model: Its past and its future in health care
Holden, R. J., & Karsh, B.-T. (2010) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2009.07.002 · Cited by 2,669 · see citing works
Uses and abuses of coefficient alpha
Schmitt, N. (1996) · Psychological Assessment · doi:10.1037/1040-3590.8.4.350 · Cited by 2,588 · see citing works
The Technology Acceptance Model: Past, Present, and Future
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003) · Communications of the Association for Information Systems · doi:10.17705/1cais.01250 · Cited by 2,572 · see citing works
Guidelines for Human-AI Interaction
Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300233 · Cited by 2,439 · see citing works
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019) · Information Systems Frontiers · doi:10.1007/s10796-017-9774-y · Cited by 2,137 · see citing works
Examining the technology acceptance model using physician acceptance of telemedicine technology
Hu, P. J., Chau, P. Y. K., Sheng, O. R. L., & Tam, K. Y. (1999) · Journal of Management Information Systems · doi:10.1080/07421222.1999.11518247 · Cited by 1,951 · see citing works
Scherer, R., Siddiq, F., & Tondeur, J. (2019) · Computers & Education · doi:10.1016/j.compedu.2018.09.009 · Cited by 1,923 · see citing works
Maxwell, A. E., & Harman, H. H. (1968) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2343736 · Cited by 1,853 · see citing works
Schepers, J., & Wetzels, M. G. M. (2006) · Information & Management · doi:10.1016/j.im.2006.10.007 · Cited by 1,690 · see citing works
Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000) · Organizational Behavior and Human Decision Processes · doi:10.1006/obhd.2000.2896 · Cited by 1,237 · see citing works
The Bayesian information criterion: background, derivation, and applications
Neath, A. A., & Cavanaugh, J. E. (2011) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.199 · Cited by 1,070 · see citing works
Hoque, R., & Sorwar, G. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.02.002 · Cited by 1,004 · see citing works
Machine Learning Approaches for Clinical Psychology and Psychiatry
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966 · see citing works
Nadarzynski, T., Miles, O., Cowie, A., & Ridge, D. (2019) · DIGITAL HEALTH · doi:10.1177/2055207619871808 · Cited by 810 · see citing works
Initial validation of the general attitudes towards Artificial Intelligence Scale
Schepman, A., & Rodway, P. (2020) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2020.100014 · Cited by 785 · see citing works
Medical students' attitude towards artificial intelligence: a multicentre survey
Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., & Baeßler, B. (2019) · European Radiology · doi:10.1007/s00330-018-5601-1 · Cited by 754 · see citing works
Meta-Analysis of the Reasoned Action Approach (RAA) to Understanding Health Behaviors
McEachan, R., Taylor, N. J., Harrison, R., Lawton, R., Gardner, P. H., & Conner, M. T. (2016) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9798-4 · Cited by 706 · see citing works
Why Are Self-Report and Behavioral Measures Weakly Correlated?
Dang, J., King, K. M., & Inzlicht, M. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.01.007 · Cited by 634 · see citing works
Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.01.001 · Cited by 597 · see citing works
Green Consumption Behavior Antecedents: Environmental Concern, Knowledge, and Beliefs
Pagiaslis, A. P., & Krystallis, A. (2014) · Psychology and Marketing · doi:10.1002/mar.20698 · Cited by 544 · see citing works
Nomura, T., Kanda, T., & Suzuki, T. (2005) · AI & Society · doi:10.1007/s00146-005-0012-7 · Cited by 530 · see citing works
Cassidy, S., & Eachus, P. (2002) · Journal of Educational Computing Research · doi:10.2190/jgjr-0kvl-hrf7-gcnv · Cited by 506 · see citing works
Knoke, D., Marsden, P. V., & Kalleberg, A. L. (2017) · doi:10.1002/9781405164061.ch34 · Cited by 487 · see citing works
Long-Term Trends in the Public Perception of Artificial Intelligence
Fast, E., & Horvitz, E. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10635 · Cited by 467 · cited 2× · see citing works
“However, they also have high hopes for AI in healthcare and education (Fast & Horvitz, 2017; Cave & Dihal, 2019) and, according to Oracle ’ s AI@Work Study 2019, about 50% of the respondents are already using some form of AI technology at work.”
BackgroundAssessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems
Rahman, M. M., Lesch, M. F., Horrey, W. J., & Strawderman, L. J. (2017) · Accident Analysis & Prevention · doi:10.1016/j.aap.2017.09.011 · Cited by 431 · see citing works
Wu, K., Zhao, Y. C., Zhu, Q., Tan, X., & Zheng, H. (2011) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2011.03.004 · Cited by 399 · see citing works
Seufert, S., Guggemos, J., & Sailer, M. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106552 · Cited by 369 · see citing works
Hopes and fears for intelligent machines in fiction and reality
Cave, S., & Dihal, K. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0020-9 · Cited by 364 · see citing works
Edmunds, R., Thorpe, M., & Conole, G. (2010) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2010.01142.x · Cited by 336 · see citing works
Acceptance of Automated Road Transport Systems (ARTS): An Adaptation of the UTAUT Model
Madigan, R., Louw, T., Dziennus, M., Graindorge, T., Ortega, E., Graindorge, M., & Merat, N. (2016) · Transportation research procedia · doi:10.1016/j.trpro.2016.05.237 · Cited by 316 · see citing works
The Sisyphean Cycle of Technology Panics
Orben, A. (2020) · Perspectives on Psychological Science · doi:10.1177/1745691620919372 · Cited by 316 · see citing works
International Journal of Social Robotics
(2013) · International Journal of Social Robotics · doi:10.1007/12369.1875-4805 · Cited by 313 · see citing works
A Meta-analysis of the Unified Theory of Acceptance and Use of Technology (UTAUT)
Dwivedi, Y. K., Rana, N. P., Chen, H., & Williams, M. D. (2011) · IFIP Advances in Information and Communication Technology · doi:10.1007/978-3-642-24148-2_10 · Cited by 312 · see citing works
Augmented reality smart glasses: an investigation of technology acceptance drivers
Rauschnabel, P. A., & Ro, Y. K. (2016) · International Journal of Technology Marketing · doi:10.1504/ijtmkt.2016.075690 · Cited by 306 · see citing works
International Journal of Technology and Design Education
(2013) · International Journal of Technology and Design Education · doi:10.1007/10798.1573-1804 · Cited by 300 · see citing works
Artificial Intelligence in Psychological Practice: Current and Future Applications and Implications
Luxton, D. D. (2014) · Professional Psychology Research and Practice · doi:10.1037/a0034559 · Cited by 297 · see citing works
Theory of reasoned action application for green information technology acceptance
Mishra, D., Akman, I., & Mishra, A. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.03.030 · Cited by 275 · see citing works
Ullman, J. B., & Bentler, P. M. (2003) · Handbook of Psychology · doi:10.1002/0471264385.wei0224 · Cited by 273 · see citing works
Plotnikoff, R. C., Costigan, S. A., Karunamuni, N. D., & Lubans, D. R. (2013) · Preventive Medicine · doi:10.1016/j.ypmed.2013.01.013 · Cited by 271 · see citing works
Predictive factors of telemedicine service acceptance and behavioral intention of physicians
Rho, M. J., Choi, I. Y., & Lee, J. (2014) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2014.05.005 · Cited by 260 · see citing works
A meta-analysis of the UTAUT model: Eleven years later
Khechine, H., Lakhal, S., & Ndjambou, P. (2016) · Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration · doi:10.1002/cjas.1381 · Cited by 246 · see citing works
Mohiuddin, M., Al Mamun, A., Syed, F., Mehedi Masud, M., & Su, Z. (2018) · Sustainability · doi:10.3390/su10051534 · Cited by 233 · see citing works
Liu, L., Miguel Cruz, A., Rios Rincon, A., Buttar, V., Ranson, Q., & Goertzen, D. (2015) · Disability and Rehabilitation · doi:10.3109/09638288.2014.923529 · Cited by 229 · see citing works
Computer based assessment: Gender differences in perceptions and acceptance
Terzis, V., & Economides, A. A. (2011) · Computers in Human Behavior · doi:10.1016/j.chb.2011.06.005 · Cited by 195 · see citing works
Extremes of acceptance: Employee attitudes toward artificial intelligence
Lichtenthaler, U. (2019) · Journal of Business Strategy · doi:10.1108/jbs-12-2018-0204 · Cited by 164 · see citing works
Journal of Convergence for Information Technology
Ju, E.-S. (2018) · Journal of Convergence Information Technology · doi:10.22156/cs4smb.2018.8.2.067 · Cited by 150 · see citing works
Teo, T. P., & van Schaik, P. (2011) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2011.581892 · Cited by 143 · see citing works
Effects of network quality attributes on customer adoption intentions of Internet Banking
Shih, Y., & Fang, K. (2005) · Total Quality Management & Business Excellence · doi:10.1080/14783360500249661 · Cited by 117 · see citing works
Technology Acceptance Model for Business Intelligence Systems: Preliminary Research
Bach, M. P., Čeljo, A., & Zoroja, J. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.09.270 · Cited by 96 · see citing works
Dam, N. T. V., Hobkirk, A. L., Danoff‐Burg, S., & Earleywine, M. (2012) · Assessment · doi:10.1177/1073191112438743 · Cited by 95 · see citing works
Han, Y., & Hansen, H. (2012) · International Journal of Psychological Studies · doi:10.5539/ijps.v4n1p22 · Cited by 78 · see citing works
Moving beyond Adoption: Exploring the Determinants of Student Intention to Use Technology
Robinson, L. (2006) · Marketing Education Review · doi:10.1080/10528008.2006.11488962 · Cited by 63 · see citing works
Bendig, E., Erb, B., Schulze-Thuesing, L., & Baumeister, H. (2019) · Verhaltenstherapie · doi:10.1159/000499492 · Cited by 62 · see citing works
Nutrition knowledge, attitudes and practices of canadian public health nurses
Schwartz, N. E. (1976) · Journal of Nutrition Education · doi:10.1016/s0022-3182(76)80113-6 · Cited by 61 · see citing works
HR Professionals’ Intention to Adopt and Use of Artificial Intelligence in Recruiting Talents
Alam, M. S., Khan, T.-U.-Z., Dhar, S. S., & Munira, K. S. (2020) · Business Perspective Review · doi:10.38157/business-perspective-review.v2i2.122 · Cited by 56 · see citing works
Werner, C. S., Schermelleh-Engel, K., Gerhard, C., & Gäde, J. C. (2015) · Springer-Lehrbuch · doi:10.1007/978-3-642-41089-5_17 · Cited by 16 · see citing works
Möchten Krankenhausärzte wirklich auf digitale Systeme umsteigen?
Duyck, W., Pynoo, B., Devolder, P., Voet, T., Adang, L., & Vercruysse, J. (2008) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/s-2008-1027344 · Cited by 15 · see citing works
Mayerl, J., & Giehl, C. (2017) · Survey research methods · doi:10.18148/srm/2018.v12i3.7207 · Cited by 11 · see citing works
Johansson, R., Skantze, G., & Jönsson, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67401-8_20 · Cited by 9 · see citing works
Determinanten der Technologie- und Prozessakzeptanz im Kontext kooperativer Arbeit
Fischbach, J. (2019) · Zeitschrift für Arbeitswissenschaft · doi:10.1007/s41449-019-00146-1 · Cited by 8 · see citing works
Paltoglou, A. E., Carter, W., & Davies, E. (2019) · Psychology Learning & Teaching · doi:10.1177/1475725718819290 · Cited by 7 · see citing works
The interface between computers and psychology: Toward a psychology of computerization
Mruk, C. J. (1987) · Computers in Human Behavior · doi:10.1016/0747-5632(87)90021-5 · Cited by 6 · see citing works
Haas, P., Scheiner, C. W., Witt, M., et al. (2013) · Alexandria (UniSG) (University of St.Gallen) · Cited by 3 · see citing works
A Simulator for Analyzing the Accuracy of Correlative Interferometer Direction Finder
정보통신학부, 백., Lim, J., Kim, Y., et al. (2017) · 융합정보논문지(구 중소기업융합학회논문지) · doi:10.22156/cs4smb.2017.7.2.053 · Cited by 1 · see citing works
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Xia, Q., Chiu, T. K. F., Chai, C. S., & Xie, K. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13305 · Cited by 203 · see citing works
Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., Sabio, J. B., & de los Santos, J. A. (2023) · Nurse Education in Practice · doi:10.1016/j.nepr.2023.103815 · Cited by 196 · see citing works
An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI
Chan, C. K. Y., & Zhou, W. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00284-4 · Cited by 192 · see citing works
Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., & Sabio, J. B. (2023) · Nurse Education Today · doi:10.1016/j.nedt.2023.105945 · Cited by 145 · see citing works
Students’ attitudes towards using ChatGPT as a learning tool: the case of the University of Jordan.
O. Ajlouni, A., Abd-Alkareem Wahba, F., & Salem Almahaireh, A. (2023) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v17i18.41753 · Cited by 116 · see citing works
Pokrivcakova, S. (2023) · Journal of language and cultural education · doi:10.2478/jolace-2023-0031 · Cited by 81 · see citing works
Kleine, A.-K., Kokje, E., Lermer, E., & Gaube, S. (2023) · JMIR Human Factors · doi:10.2196/46859 · Cited by 49 · see citing works
Supporting learner agency in collaborative writing with generative <scp>AI</scp>
Kim, S., So, H., & Park, K. (2026) · British Journal of Educational Technology · doi:10.1111/bjet.70015 · Cited by 30 · see citing works
Kelly, S., Kaye, S.-A., White, K. M., & Oviedo-Trespalacios, O. (2025) · AI & SOCIETY · doi:10.1007/s00146-025-02334-6 · Cited by 17 · see citing works
Castro-Lopez, A., El Abed, M., Cervero, A., & Álvarez-Blanco, L. (2026) · European Journal of Higher Education · doi:10.1080/21568235.2026.2620686 · Cited by 3 · see citing works
- Authors
- Gado, S., Kempen, R., Lingelbach, K., & Bipp, T.
- Affiliations
- University of Würzburg (DE)
- University of Stuttgart (DE)
- Heidelberg University (DE)
- Corresponding author
- Gado, Sabrina
- Venue
- Psychology Learning & Teaching
- Publisher
- SAGE Publications
- Published
- 2021-08-12
- Volume
- 21
- Issue
- 1
- Pages
- 37-56
- ISSN
- 1475-7257 (ISSN-L)
- 2057-3022
- OpenAlex
- W3194107456
- Cited by
- 153
- References
- 85
- Citations by year
- 2026: 44 · 2025: 49 · 2024: 33 · 2023: 22 · 2022: 4 · 2021: 1
- FWCI
- 13.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychology Learning & Teaching (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- OPUS (Aalen University) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, psychology students, technology acceptance, perceived usefulness, perceived ease of use, attitudes toward AI, intention to use AI, social norms, perceived knowledge, psychology education
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