AI Teaching Assistants in Hybrid Learning: Strengths and Limitations in Healthcare Education

Zijing Hu, Caixia Qiu

Research output: Contribution to journalArticlepeer-review

Abstract

The rapid development of Artificial Intelligence has transformed higher education, offering innovative tools such as AI teaching assistants to enhance learning experiences. However, there is a lack of research exploring participants’ views and experiences on AI teaching assistants in the South African context. This study explored students’ views and experiences of AI teaching assistants in sustaining hybrid learning environments in health science education at a South African university. The Diffusion of Innovation model anchored in this study as a theoretical framework. The author adopted a qualitative case study design. Semi-structured interviews were utilised. A purposive sampling technique was employed to recruit six participants for this study. Thematic analysis was followed to analyse the data. Trustworthiness and ethical considerations were ensured. While AI teaching assistants offer significant benefits, their integration into healthcare education requires addressing ethical and technical challenges. Participants appreciated the in-time accessibility, personalised feedback, and interactive features of AI teaching assistants, which enhanced their engagement and flexibility in learning. However, they also highlighted challenges, including concerns about data privacy, content accuracy, and the lack of emotional support and mentorship from AI teaching assistants. The study concluded that a hybrid approach, combining AI with human teaching, is essential to maximise the potential of AI in education while preserving the human touch critical for mentoring and emotional support. Recommendations included providing comprehensive training, ensuring data privacy, and conducting pilot programmes to facilitate the successful implementation of AI teaching assistants in hybrid learning environments.

Original languageEnglish
Pages (from-to)556-575
Number of pages20
JournalInternational Journal of Learning, Teaching and Educational Research
Volume24
Issue number7
DOIs
Publication statusPublished - Jul 2025

Keywords

  • AI teaching assistants
  • acupuncture
  • artificial intelligent
  • health sciences
  • hybrid learning

ASJC Scopus subject areas

  • Education

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