Artificial intelligence and English learner remotivation
A quasi-experimental study of student perceptions
Bibliographic Data
| ID | 22433657 |
|---|---|
| Authors | Bakr Al-Sofi (University of Bisha, corresponding author) |
| Year | 2026 |
| Volume | 13 |
| Issue | 1 |
| Publication date | 2026-06-29 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | East European Journal of Psycholinguistics (JOURNAL) |
| Journal identifiers | ISSN: 2312-3265 • E-ISSN: 2313-2116 |
| Publisher | Lesya Ukrainka Volyn National University (PUBLISHER • UA) |
| DOI | 10.29038/eejpl.2026.13.1.al-s |
| OpenAlex | W7167343793 |
| Language | EN |
| References cited | 45 |
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.
Cognition · Construct validity · Educational technology · Exploratory research · Facilitator · Interpersonal communication · Perception · AI in Service Interactions · Educational and Psychological Assessments · EFL/ESL Teaching and Learning
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From Demotivation to Remotivation
| Highly cited | No |
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