THE INFLUENCE OF PREDICTOR FACTORS ON BEHAVIORAL INTENTION TO IMPLEMENT AI-PROCTORED ONLINE ASSESSMENT
DOI:
https://doi.org/10.35631/IJMOE.830037Keywords:
AI-Proctoring, Effort Expectancy, Facilitating Condition, Online Assessment, Social InfluenceAbstract
The purpose of this study was to identify the factors influencing educators’ behavioural intention to implement AI-proctored online assessments (automated proctoring) and to determine the strength of these predictor factors. The focus of this study is on health science programmes involving formative and summative assessment. This study was necessary to the institution make plans related to the use of AI in assessment. A total of 260 educators from 14 Institut Latihan Kementerian Kesihatan Malaysia (ILKKM) were selected using simple random sampling. The study employed a questionnaire adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT). Data were analysed using SPSS version 27 through descriptive and inferential statistics, including mean, standard deviation, and multiple regression analysis. The results indicated that performance expectancy (M = 4.14, SD = .806), effort expectancy (M = 4.23, SD = .746), social influence (M = 3.95, SD = .794), and behavioural intention (M = 4.06, SD = .807) were at high levels, while facilitating conditions (M = 3.60, SD = .909) were at a moderate level. Multiple regression analysis revealed that effort expectancy, social influence, and facilitating conditions significantly influenced behavioural intention. Social influence emerged as the strongest predictor (β = .778, p < 0.05), contributing 60.5% of the variance change (R² = .605). The combination of social influence and effort expectancy increased the explained variance to 67.0% (R² = .67), while the inclusion of facilitating conditions raised it to 71.5% (R² = .715). In conclusion, social influence, effort expectancy, and facilitating conditions play significant roles in shaping educators’ behavioural intention to implement AI-proctored online assessments within the ILKKM context. By identifying the predictor factors influencing this intention, the effectiveness of implementing AI-proctored online assessments at ILKKM can be enhanced.
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