Table 1 Some previous studies on students’ perceptions of AI and ChatGPT
From: An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI
Authors | Technological focus | Theoretical lens | Research methods | Academic level | Country | Sample size | Key findings |
|---|---|---|---|---|---|---|---|
Kim et al. (2020) | AI teaching assistants (AITA, machine teachers) | Technology Acceptance Model (TAM) | Quantitative | Higher education | United States | 321 | Students’ perceived ease of communication with AITA, perceived usefulness, and positive attitudes are three determinants of students’ intention to adopt AITA |
Zou et al. (2020) | Voice recognition technology in AI-English Language Learning apps (VRT-Assisted AI-ELLs Apps) | N/A | Sequential explanatory mixed-methods | Higher education | China | 113 in the quantitative research; 6 for the interviews | Students had a generally positive attitude towards VRT-Assisted AI-ELLs Apps usage with the advantages of providing feedback in the absence of a tutor; but there were also certain limitations regarding grading criteria and reliability of the feedback |
Gado et al. (2022) | AI usage | An integrated model based on Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) | Quantitative | Higher education | Germany | 218 psychology students | Perceived usefulness, perceived social norm regarding AI, and attitude towards AI predicted intention to use AI; intention to use AI was not significantly affected by perceived usefulness |
Hu (2022) | AI-supported smart learning environment | Technology Acceptance Model (TAM) | Mixed methods | Higher education | N/A | 50 first-year students | Perceived ease of use and perceived usefulness positively affected students’ behavioural intention |
Kumar and Raman (2022) | AI usage | N/A | Mixed methods | Higher education | India | 682 | Students had different perceptions and preferences over the effective use of AI in various processes, e.g., teaching and learning, admission, placement, and administrative processes |
Raffaghelli et al. (2022) | Early warning systems | Unified Theory of Acceptance and Use of Technology (UTAUT) | Quantitative | Higher Education | Spain | 347 students | A low level of expected effort in the tool’s usage was correlated with a high level of perceived usefulness of the tool; students’ acceptance of the tool declined in the post-usage stage |
Haensch et al. (2023) | ChatGPT | N/A | Content analysis | N/A | N/A | 100 most popular TikTok videos with #chatgpt | Popularity and potential knowledge gaps were identified among users, especially regarding the lack of discussion over the failures of ChatGPT |
Bonsu and Baffour-Koduah (2023) | ChatGPT | Technology Acceptance Model (TAM) | Mixed methods | Higher education | Ghana | 107 in the quantitative research; 10 for the interviews | No statistically significant relationship between students’ perceptions and their intention or use of ChatGPT in higher education, but students had the intention to use ChatGPT and advocated the technology for its convenience, accuracy, and generation of better results |
Raman et al. (2023) | ChatGPT | Rogers' perceived theory of attributes | Mixed methods | Higher education | India | 288 | Relative advantage, compatibility, ease of Use, observability, and trialability were identified as factors influencing students’ intention of using ChatGPT. Gender-based differences were also observed regarding the preferences for ChatGPT adoption |
Chan and Hu (2023) | GenAI | N/A | Mixed methods | Higher education | Hong Kong, China | 399 | Five benefits and six challenges regarding GenAI in teaching and learning were identified |
Abdelwahab et al. (2023) | AI & the quality of higher education | 4-Quality Indicator Model of Service Quality (adapted from Malechwanzi et al., 2016) | Mixed methods | Higher education | the Netherlands | 95 | Students' overall impressions of AI education are less than favourable. While students have a basic awareness of AI, their comprehension lacks the depth required to equip them with the necessary skills and knowledge for future workplace |
Dahlkemper et al. (2023) | ChatGPT (perceived scientific accuracy and linguistic quality) | Unified Theory of Acceptance and Use of Technology (in stage 1 of the research) | Mixed methods | Higher education | Germany | 102 physics students | The majority of the students had already heard of ChatGPT (84%), but less than half had used the chatbot. When students have sufficient knowledge, they can make adequate evaluation on the scientific accuracy and linguistic quality of ChatGPT generated answers compared with sample solutions |