Abstract:

In digital education research, researchers are increasingly adopting a diverse and inclusive approach to studying various aspects of education in the digital realm. Educators across diverse disciplines consistently investigate innovative pedagogical strategies intended to sustain learner engagement and optimize the overall learning experience. Correspondingly, language instructors are actively integrating advanced technological tools to achieve the parallel objective of enhancing student learning outcomes.

This two-year action quantitative research study was to create an application system as a tool and materials for training interpretation in English combined with artificial intelligence, machine learning, and chatbot technologies. Student learning effectiveness, affect, and motivation from a self-determination theory perspective were studied. An AI interpretation training platform was designed using AI Unity Plugin and chatbot. Shadowing practice, sight translation, and listening and interpreting are the practice modes with each one having vocabulary, phrase, grammar, sentence, and paragraph sections. Every question in the three modes has time limitation for the purpose of imitating the real-world interpretation and speeding the responses of the learners so that they progressed from consecutive interpretation to simultaneous interpretation.

The actual experiments were done after the 6 modules on the platform were completed. 301 college students were invited for the training and completed the survey with an informed consent approved by from the Research Ethics Committee at the National Chengchi University in Taiwan. The survey includes the dimensions in the Self-Determination Theory (SDT): autonomy, competence, and relatedness. A five-point Likert scale was used.

A model with fit indices was built using structural equation modeling (SEM) analysis. Before the SEM analysis, a confirmatory factor analysis was conducted with the overall Cronbach's Alpha value for the entire survey being 0.955. The results revealed that self-determination motive has direct effect on intrinsic motivation but the effect was also mediated by their regulatory styles and digital materials experience. Additionally, intrinsic motivation has direct effect on positive and negative affect, which has direct effect on concentration. Self-determination motive has indirect effects on positive and negative affect through the mediating role of intrinsic motivation. Self-determined motive has indirect effects on digital material experience (DME) through the mediating role of overall regulation.

The study findings implies that autonomy can be improved by providing users with sufficient choices and options, having control over the platform, and having a personalized experience. Additionally, when students were unaware of the platform’s full capabilities, their sense of competence was diminished. Ultimately, the individual must feel connected to other people in a meaningful way. Participants felt understood and supported by others in a meaningful way. Hence, the platform has significant potential to engage students, foster autonomy, competence, and relatedness, thereby enhancing their intrinsic motivation.

Accordingly, language teaching researchers are encouraged to take advantage of these advanced technologies for various aspects of language teaching and learning so that more innovative technological learning tools can be created in anticipation of developing student autonomous learning.

Keywords:

Motivation, artificial intelligence, chatbot, structural equation modelling analysis.