The role of techno-competence in AI-based assessments: exploring its influence on students’ boredom, self-esteem, and writing development

Abstract

Abstract This study inspected how techno-competence in artificial intelligence (AI)-driven evaluations affected EFL learners’ boredom, self-esteem, and writing skills. To do so, 66 Saudi Arabian male students from Saudi Arabia participated in the study. They were then split into the experimental group (EG) and the control group (CG). While the CG was assessed using conventional techniques, the EG was subjected to AI-enhanced assessments. Pre- and post-assessments were used to gauge the students’ degrees of boredom, writing proficiency, and self-esteem. The results showed that for every variable measured, the EG performed better than the CG. In particular, participants’ levels of boredom decreased, their self-esteem was raised, and their writing abilities improved when AI was used in the evaluations. These outcomes imply that incorporating AI-based assessments in language learning can foster a more supportive and engaging milieu, leading to better educational results. This research highlights the potential of techno-competence in AI to transform the academic landscape, especially in language assessment, by addressing learners’ needs and developing overall learning experiences. However, the research’s limitations, including a small and homogenous sample, a short study duration, and the focus on specific skills, highlight the need for further research.

Keywords

• AI-driven assessments
• Boredom in assessment
• Self-esteem
• Techno-competence
• Writing development