Research Article
Abeer Aidh Alshwiah, Lamees Abdulrahman Alaulamie
CONT ED TECHNOLOGY, Volume 18, Issue 1, Article No: ep635
ABSTRACT
This study aimed to determine the extent and effects of pre-service teachers’ (PSTs) knowledge of generative artificial intelligence (GenAI) techniques, and to identify their perceptions of the benefits and potential challenges of GenAI. It also sought to determine PSTs’ anxiety over GenAI learning and the risk of job replacement. Students’ perceptions of artificial intelligence were therefore gathered to help ascertain the changes necessary for integrating GenAI into their courses. To achieve these aims, a mixed methods approach was adopted. Thus, a survey (quantitative method) was conducted to discover the relationship between PSTs’ knowledge of GenAI and the following variables: (1) willingness to use GenAI, (2) concerns regarding the use of GenAI, (3) anxiety about learning, and (4) anxiety about job replacement. A sample of 170 PSTs participated in this survey, with results indicating moderate knowledge of GenAI. Moreover, positive correlations were found between the participants’ knowledge of GenAI and their willingness to use it, concern over its uses, and anxiety about learning and job replacement. Interviews (a qualitative method) were subsequently carried out with 10 survey participants to explore the potential benefits and challenges associated with using GenAI in learning, and to validate the survey results. GenAI technologies can provide users with instantly accessible feedback and suggestions for assignments. However, the interviewees mentioned a number of challenges, like a lack of training courses, presence of bias and discrimination in the data, and ignorance of the University’s rules for GenAI use.
Keywords: generative artificial intelligence, expectancy-value theory, anxiety, pre-service teachers
Research Article
Izida I. Ishmuradova, Alexey A. Chistyakov, Tatyana A. Brodskaya, Nikolay N. Kosarenko, Natalia V. Savchenko, Natalya N. Shindryaeva
CONT ED TECHNOLOGY, Volume 17, Issue 2, Article No: ep565
ABSTRACT
This investigation aimed to ascertain latent profiles of university students predicated on fundamental factors influencing their intentions to acquire knowledge in artificial intelligence (AI). The study scrutinized four dimensions: supportive social norms, facilitating conditions, self-efficacy in AI learning, and perceived utility of AI. Through the utilization of latent profile analysis (LPA), the investigation endeavored to unveil distinct subgroups of students delineated by unique amalgamations of these factors. The study was carried out with a cohort of 391 university students from diverse academic disciplines. LPA disclosed five unique subgroups of students: Cautious Participants, Enthusiastic Advocates, Reserved Skeptics, Pragmatic Acceptors, and Disengaged Critics. These categories showed somewhat different goals to learn AI; Enthusiastic Advocates showed the highest intention while Disengaged Critics showed the lowest. The findings enhance the growing corpus of research on AI education in higher education by providing a sophisticated knowledge of the variation among university students about their attitudes and preparedness to learn AI. Subgroups of students show that learners need unique educational strategies and interventions to meet their diverse needs and attitudes. AI is changing many fields, therefore college students must learn about it and prepare for it. The findings advance AI education research and impact curriculum and policy.
Keywords: artificial intelligence, higher education, latent profile analysis, situated expectancy-value theory, intention to learn AI