Cultural Adaptation in AI-Assisted Learning Environments

Abstract

There has been an increasing application of artificial intelligence-assisted learning environments in higher educational institutions, but there have been limited cultural adaptations to this functionality. The study aimed to investigate how cultural aspects affected learning experiences using artificial intelligence-assisted learning environments, thereby determining opportunities and challenges that arise. The study used a qualitative study design, where data were collected using face-to-face interview techniques involving fifteen participants majoring from different academic departments. The collected data were then retrieved using thematic analysis. Results indicated that artificial intelligence-assisted learning facilitates self-directed learning through flexible pacing, instant feedback, and dynamic instructional support. Culturally aligned learning environments facilitate increased engagement and inclusivity in learning by using culturally aligned language and illustrative examples. Nonetheless, difficulties such as language incongruities, inadequate cultural alignment, bias, and technical issues pose setbacks to learning. Culturally responsive learning environments enhance learner motivation, engagement, and inclusion by incorporating culturally relevant language and examples. Conversely, however, mismatches in language, a lack of cultural customization, algorithmic bias, and technical limitations resulted in disadvantages for several participants. The study concluded that artificial intelligence-assisted learning environments are more effective when developed using culturally responsive and human-centered design principles, and emphasis was laid on the fact that closer collaboration is needed between educators and developers.