Artificial intelligence and English learner remotivation: A quasi-experimental study of student perceptions

Bakr Bagash Mansour Ahmed Al-Sofi

doi:10.29038/eejpl.2026.13.1.al-s

Authors

Bakr Bagash Mansour Ahmed Al-Sofi Department of English Language and Literature, College of Arts and Letters, University of Bisha, Saudi Arabia https://orcid.org/0000-0001-6115-313X

DOI:

https://doi.org/10.29038/eejpl.2026.13.1.al-s

Keywords:

artificial intelligence, affective filter, Gemini, remotivation, self-determination theory

Abstract

This quasi-experimental study investigated the primary factors contributing to English-language demotivation among first-year Saudi undergraduates. It evaluated the longitudinal impact of a structured Gemini intervention on student perceptions of remotivation. Baseline metrics identified perceived instructional distance and evaluation-related classroom anxieties, specifically fear of negative peer judgment, as the most severe demotivators. Following a 12-week intervention, a parametric Two-Way Mixed ANOVA revealed a monumental, highly significant longitudinal interaction effect in motivational recovery, with an independent Exploratory Factor Analysis confirming excellent psychometric construct validity for the AI remotivation subscale. Qualitative interviews triangulated these findings, demonstrating that participants perceived Gemini's intervention as a primary restorative catalyst for functional, individualistic remotivation. It established a private, zero-anxiety digital sandbox that unblocked finite cognitive processing resources and elevated perceived autonomy and competence. However, the results revealed a critical "efficiency–relatedness paradox": the automated intervention strained perceptions of social relatedness and instructional delivery. The study concludes that for generative AI to serve as a sustainable motivational driver, institutions should move beyond technological substitution models in favor of a blended, human-in-the-loop framework in which instructors leverage automated interfaces for technical feedback while intentionally repurposing saved classroom hours to deepen human mentorship, socio-affective rapport, and empathetic interpersonal validation.

Disclosure Statement

The author reported no potential conflicts of interest.

Generative AI Statement

During the preparation of this work, the author used Gemini 3.1 Pro and Grammarly to refine language and improve clarity. The author critically reviewed and edited all AI-generated suggestions and remains fully responsible for the integrity, accuracy, and original contributions of the final manuscript.

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Author Biography

Bakr Bagash Mansour Ahmed Al-Sofi, Department of English Language and Literature, College of Arts and Letters, University of Bisha, Saudi Arabia

Bisha 61922, P.O. Box 551, Saudi Arabia

Email: [email protected]

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