Assessing Artificial Intelligence-driven Personalised Reading Recommendations' Impact on Students' Reading Habits
DOI:
https://doi.org/10.66545/tswdv304Keywords:
Artificial Intelligence, constructivist theories, personalised learning, reading motivation, reading satisfactionAbstract
This study examined the influence of AI-driven personalised reading recommendations on learners' reading behaviour. Utilising a quasi-experimental design, the research involved 200 undergraduate students over a semester, grouped into two – control and experimental groups – receiving personalised recommendations through an AI-based system. Data were collected through pre- and post-intervention surveys, reading logs, and focus group discussions. The findings indicate a significant increase in reading frequency and diversity of genres among students exposed to AI-driven recommendations compared to the control group. Additionally, qualitative data revealed enhanced engagement and motivation to read, attributed to the personalised nature of the recommendations. This study highlights the capacity of AI to adapt learning experiences to individual student preferences, thereby promoting a more engaging and effective reading culture. Implications for educators include the adoption of AI technologies to support personalised learning pathways while future research should explore long-term effects and scalability across diverse educational contexts.
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