Ai in e-learning: the affordance perspective

Jing Zhang, Zilong Liu, Haibin Lv and Ming Jiang

Behaviour and Information Technology, 2024, vol. 43, issue 15, 3863-3892

Abstract: The AI-enabled intelligent learning system (AEILS) is able to provide personalised and intelligent tutoring and is more capable of meeting individuals’ need. Nevertheless, limited studies focused on the effect of AI-specific factors on user behaviour. To fill this research gap, we identified the AEILS-specific affordances (i.e. interactivity, personalisation, competition, convenience) and explored their effect on user engagement and foreign language speaking anxiety (FLSA). In this paper, we integrate quantitative and qualitative studies to explore user behaviour in AEILS. Survey data was collected from 457 respondents and analysed using structural equation modelling with the smart-PLS software. The results showed that AEILS-specific affordances significantly affect flow experience and self-expansion, thus facilitating user engagement and alleviating FLSA. Semi-structured interviews were conducted to corroborate the findings of the quantitative study. These findings highlighted the importance of technology affordance in AEILS. This study contributed to the literature on IS and education by incorporating context-specific factors into account.

Date: 2024
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DOI: 10.1080/0144929X.2023.2287660

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