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.

Attitudes Toward the Adoption of two AI-Enabled Mental Health Tools Among Prospective Psychotherapists: A Cross-Sectional Study

“…By differentiating between specific tool understanding and more general cognitive technology readiness, the current study moves beyond previous research that focused on the role of general AI knowledge in predicting general usage intention (Gado et al, 2022). The mediation analyses revealed that none of the three UTAUT variables mediate the relationship between tool understanding/cognitive technology readiness with the intention to use the feedback tool.…”

“…Furthermore, Tran et al (2021) identified social influence as the only significant predictor of the intention to use AI-CDSSs among undergraduate medical students. Gado et al (2022) found support for 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 usage intention via positive attitudes towards AI in a sample of psychology students. This consistent link between social influence and AI usage intentions found in studies using student samples may be explained by the greater susceptibility of students to the influence of peers and prospective employers (Hurst & Good, 2009).…”

“…The first goal of the current study is to test the applicability of a modified version of the UTAUT in the mental health context to understand the factors that influence the intention to use two specific AI-enabled mental healthcare tools (Gado et al, 2022;Venkatesh, 2022;Venkatesh et al, 2016;Venkatesh et al, 2003). In line with the UTAUT, we propose tool-specific perceived usefulness (i.e., the degree to which an individual believes that using a system will enhance their performance) and perceived ease of use (i.e., the degree of ease associated with using the technology) to predict the behavioral intention to use the tools in their future work.…”

“…Building on the New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies (NASSS, Greenhalgh et al, 2017), we propose that knowledge regarding the technology is a predictor of its perceived value. Consequently, we suggest that students with the knowledge and skills to apply the tools and understand how the recommendations are derived are more likely to perceive them as useful (Gado et al, 2022;Seufert et al, 2021). To test this, we extend the UTAUT by including cognitive technology readiness as an indicator of general AI knowledge and understanding of the tool as an indicator of specific AI knowledge as predictors of perceived usefulness, perceived ease of use, and trust.…”

“…Several studies have already demonstrated the applicability of UTAUT in investigating AI-CDSSs implementation (Arfi et al, 2021;Fan et al, 2020a;Gado et al, 2022;Lin et al, 2021;Tran et al, 2021;Zhai et al, 2021). But only one study has thus far examined the predictors of the intention to use AI-enabled tools in mental health care (Gado et al, 2022). The authors asked psychology students about their general knowledge and attitudes toward AI systems.…”